ATLAS distributed computing: experience and evolution

CERN Document Server

Nairz, A; The ATLAS collaboration

2014-01-01

The ATLAS experiment has just concluded its first running period which commenced in 2010. After two years of remarkable performance from the LHC and ATLAS, the experiment has accumulated more than 25/fb of data. The total volume of beam and simulated data products exceeds 100~PB distributed across more than 150 computing centres around the world, managed by the experiment's distributed data management system. These sites have provided up to 150,000 computing cores to ATLAS's global production and analysis processing system, enabling a rich physics programme including the discovery of the Higgs-like boson in 2012. The wealth of accumulated experience in global data-intensive computing at this massive scale, and the considerably more challenging requirements of LHC computing from 2015 when the LHC resumes operation, are driving a comprehensive design and development cycle to prepare a revised computing model together with data processing and management systems able to meet the demands of higher trigger rates, e...

AGIS: Integration of new technologies used in ATLAS Distributed Computing

OpenAIRE

Anisenkov, Alexey; Di Girolamo, Alessandro; Alandes Pradillo, Maria

2017-01-01

The variety of the ATLAS Distributed Computing infrastructure requires a central information system to define the topology of computing resources and to store different parameters and configuration data which are needed by various ATLAS software components. The ATLAS Grid Information System (AGIS) is the system designed to integrate configuration and status information about resources, services and topology of the computing infrastructure used by ATLAS Distributed Computing applications and s...

Exploitation of heterogeneous resources for ATLAS Computing

CERN Document Server

Chudoba, Jiri; The ATLAS collaboration

2018-01-01

LHC experiments require significant computational resources for Monte Carlo simulations and real data processing and the ATLAS experiment is not an exception. In 2017, ATLAS exploited steadily almost 3M HS06 units, which corresponds to about 300 000 standard CPU cores. The total disk and tape capacity managed by the Rucio data management system exceeded 350 PB. Resources are provided mostly by Grid computing centers distributed in geographically separated locations and connected by the Grid middleware. The ATLAS collaboration developed several systems to manage computational jobs, data files and network transfers. ATLAS solutions for job and data management (PanDA and Rucio) were generalized and now are used also by other collaborations. More components are needed to include new resources such as private and public clouds, volunteers' desktop computers and primarily supercomputers in major HPC centers. Workflows and data flows significantly differ for these less traditional resources and extensive software re...

AGIS: Evolution of Distributed Computing information system for ATLAS

Science.gov (United States)

Anisenkov, A.; Di Girolamo, A.; Alandes, M.; Karavakis, E.

2015-12-01

ATLAS, a particle physics experiment at the Large Hadron Collider at CERN, produces petabytes of data annually through simulation production and tens of petabytes of data per year from the detector itself. The ATLAS computing model embraces the Grid paradigm and a high degree of decentralization of computing resources in order to meet the ATLAS requirements of petabytes scale data operations. It has been evolved after the first period of LHC data taking (Run-1) in order to cope with new challenges of the upcoming Run- 2. In this paper we describe the evolution and recent developments of the ATLAS Grid Information System (AGIS), developed in order to integrate configuration and status information about resources, services and topology of the computing infrastructure used by the ATLAS Distributed Computing applications and services.

ATLAS@Home: Harnessing Volunteer Computing for HEP

CERN Document Server

Cameron, David; The ATLAS collaboration

2015-01-01

The ATLAS collaboration has setup a volunteer computing project called ATLAS@home. Volunteers running Monte-Carlo simulation on their personal computer provide significant computing resources, but also belong to a community potentially interested in HEP. Four types of contributors have been identified, whose questions range from advanced technical details to the reason why simulation is needed, how Computing is organized and how it relates to society. The creation of relevant outreach material for simulation, event visualization and distributed production will be described, as well as lessons learned while interacting with the BOINC volunteers community.

Automating ATLAS Computing Operations using the Site Status Board

CERN Document Server

Andreeva, J; The ATLAS collaboration; Campana, S; Di Girolamo, A; Espinal Curull, X; Gayazov, S; Magradze, E; Nowotka, MM; Rinaldi, L; Saiz, P; Schovancova, J; Stewart, GA; Wright, M

2012-01-01

The automation of operations is essential to reduce manpower costs and improve the reliability of the system. The Site Status Board (SSB) is a framework which allows Virtual Organizations to monitor their computing activities at distributed sites and to evaluate site performance. The ATLAS experiment intensively uses SSB for the distributed computing shifts, for estimating data processing and data transfer efficiencies at a particular site, and for implementing automatic exclusion of sites from computing activities, in case of potential problems. ATLAS SSB provides a real-time aggregated monitoring view and keeps the history of the monitoring metrics. Based on this history, usability of a site from the perspective of ATLAS is calculated. The presentation will describe how SSB is integrated in the ATLAS operations and computing infrastructure and will cover implementation details of the ATLAS SSB sensors and alarm system, based on the information in SSB. It will demonstrate the positive impact of the use of SS...

The Evolution of Cloud Computing in ATLAS

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00224309; Berghaus, Frank; Brasolin, Franco; Cordeiro, Cristovao; Desmarais, Ron; Field, Laurence; Gable, Ian; Giordano, Domenico; Di Girolamo, Alessandro; Hover, John; Leblanc, Matthew Edgar; Love, Peter; Paterson, Michael; Sobie, Randall; Zaytsev, Alexandr

2015-01-01

The ATLAS experiment has successfully incorporated cloud computing technology and cloud resources into its primarily grid-based model of distributed computing. Cloud R&D activities continue to mature and transition into stable production systems, while ongoing evolutionary changes are still needed to adapt and refine the approaches used, in response to changes in prevailing cloud technology. In addition, completely new developments are needed to handle emerging requirements. This paper describes the overall evolution of cloud computing in ATLAS. The current status of the virtual machine (VM) management systems used for harnessing infrastructure as a service (IaaS) resources are discussed. Monitoring and accounting systems tailored for clouds are needed to complete the integration of cloud resources within ATLAS' distributed computing framework. We are developing and deploying new solutions to address the challenge of operation in a geographically distributed multi-cloud scenario, including a system for ma...

The Evolution of Cloud Computing in ATLAS

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00224309; The ATLAS collaboration; Berghaus, Frank; Love, Peter; Leblanc, Matthew Edgar; Di Girolamo, Alessandro; Paterson, Michael; Gable, Ian; Sobie, Randall; Field, Laurence

2015-01-01

The ATLAS experiment has successfully incorporated cloud computing technology and cloud resources into its primarily grid-based model of distributed computing. Cloud R&D activities continue to mature and transition into stable production systems, while ongoing evolutionary changes are still needed to adapt and refine the approaches used, in response to changes in prevailing cloud technology. In addition, completely new developments are needed to handle emerging requirements. This work will describe the overall evolution of cloud computing in ATLAS. The current status of the VM management systems used for harnessing IAAS resources will be discussed. Monitoring and accounting systems tailored for clouds are needed to complete the integration of cloud resources within ATLAS' distributed computing framework. We are developing and deploying new solutions to address the challenge of operation in a geographically distributed multi-cloud scenario, including a system for managing VM images across multiple clouds, ...

The Next Generation ARC Middleware and ATLAS Computing Model

CERN Document Server

Filipcic, A; The ATLAS collaboration; Smirnova, O; Konstantinov, A; Karpenko, D

2012-01-01

The distributed NDGF Tier-1 and associated Nordugrid clusters are well integrated into the ATLAS computing model but follow a slightly different paradigm than other ATLAS resources. The current strategy does not divide the sites as in the commonly used hierarchical model, but rather treats them as a single storage endpoint and a pool of distributed computing nodes. The next generation ARC middleware with its several new technologies provides new possibilities in development of the ATLAS computing model, such as pilot jobs with pre-cached input files, automatic job migration between the sites, integration of remote sites without connected storage elements, and automatic brokering for jobs with non-standard resource requirements. ARC's data transfer model provides an automatic way for the computing sites to participate in ATLAS' global task management system without requiring centralised brokering or data transfer services. The powerful API combined with Python and Java bindings can easily be used to build new ...

AGIS: Integration of new technologies used in ATLAS Distributed Computing

CERN Document Server

Anisenkov, Alexey; The ATLAS collaboration; Alandes Pradillo, Maria

2016-01-01

AGIS is the information system designed to integrate configuration and status information about resources, services and topology of the computing infrastructure used by ATLAS Distributed Computing (ADC) applications and services. In this note, we describe the evolution and the recent developments of AGIS functionalities, related to integration of new technologies recently become widely used in ATLAS Computing like flexible computing utilization of opportunistic Cloud and HPC resources, ObjectStore services integration for Distributed Data Management (Rucio) and ATLAS workload management (PanDA) systems, unified storage protocols declaration required for PandDA Pilot site movers and others.

Analytics Platform for ATLAS Computing Services

CERN Document Server

Vukotic, Ilija; The ATLAS collaboration; Bryant, Lincoln

2016-01-01

Big Data technologies have proven to be very useful for storage, processing and visualization of derived metrics associated with ATLAS distributed computing (ADC) services. Log file data and database records, and metadata from a diversity of systems have been aggregated and indexed to create an analytics platform for ATLAS ADC operations analysis. Dashboards, wide area data access cost metrics, user analysis patterns, and resource utilization efficiency charts are produced flexibly through queries against a powerful analytics cluster. Here we explore whether these techniques and analytics ecosystem can be applied to add new modes of open, quick, and pervasive access to ATLAS event data so as to simplify access and broaden the reach of ATLAS public data to new communities of users. An ability to efficiently store, filter, search and deliver ATLAS data at the event and/or sub-event level in a widely supported format would enable or significantly simplify usage of machine learning tools like Spark, Jupyter, R, S...

Operation of the ATLAS distributed computing

CERN Document Server

Barreiro Megino, Fernando Harald; The ATLAS collaboration

2018-01-01

We describe the central operation of the ATLAS distributed computing system. The majority of compute intensive activities within ATLAS are carried out on some 350,000 CPU cores on the Grid, augmented by opportunistic usage of significant HPC and volunteer resources. The increasing scale, and challenging new payloads, demand fine-tuning of operational procedures together with timely developments of the production system. We describe several such developments, motivated directly from operational experience. Optimization of inefficient task requests, from both official production and users, is made possible by automatic detection of payload properties. User education, job shaping or preventative throttling help to increase the overall throughput of the available resources.

Overview of the ATLAS distributed computing system

CERN Document Server

Elmsheuser, Johannes; The ATLAS collaboration

2018-01-01

The CERN ATLAS experiment successfully uses a worldwide computing infrastructure to support the physics program during LHC Run 2. The grid workflow system PanDA routinely manages 250 to 500 thousand concurrently running production and analysis jobs to process simulation and detector data. In total more than 300 PB of data is distributed over more than 150 sites in the WLCG and handled by the ATLAS data management system Rucio. To prepare for the ever growing LHC luminosity in future runs new developments are underway to even more efficiently use opportunistic resources such as HPCs and utilize new technologies. This presentation will review and explain the outline and the performance of the ATLAS distributed computing system and give an outlook to new workflow and data management ideas for the beginning of the LHC Run 3.

ATLAS Distributed Computing in LHC Run2

CERN Document Server

Campana, Simone; The ATLAS collaboration

2015-01-01

The ATLAS Distributed Computing infrastructure has evolved after the first period of LHC data taking in order to cope with the challenges of the upcoming LHC Run2. An increased data rate and computing demands of the Monte-Carlo simulation, as well as new approaches to ATLAS analysis, dictated a more dynamic workload management system (ProdSys2) and data management system (Rucio), overcoming the boundaries imposed by the design of the old computing model. In particular, the commissioning of new central computing system components was the core part of the migration toward the flexible computing model. The flexible computing utilization exploring the opportunistic resources such as HPC, cloud, and volunteer computing is embedded in the new computing model, the data access mechanisms have been enhanced with the remote access, and the network topology and performance is deeply integrated into the core of the system. Moreover a new data management strategy, based on defined lifetime for each dataset, has been defin...

System administration of ATLAS TDAQ computing environment

Science.gov (United States)

Adeel-Ur-Rehman, A.; Bujor, F.; Benes, J.; Caramarcu, C.; Dobson, M.; Dumitrescu, A.; Dumitru, I.; Leahu, M.; Valsan, L.; Oreshkin, A.; Popov, D.; Unel, G.; Zaytsev, A.

2010-04-01

This contribution gives a thorough overview of the ATLAS TDAQ SysAdmin group activities which deals with administration of the TDAQ computing environment supporting High Level Trigger, Event Filter and other subsystems of the ATLAS detector operating on LHC collider at CERN. The current installation consists of approximately 1500 netbooted nodes managed by more than 60 dedicated servers, about 40 multi-screen user interface machines installed in the control rooms and various hardware and service monitoring machines as well. In the final configuration, the online computer farm will be capable of hosting tens of thousands applications running simultaneously. The software distribution requirements are matched by the two level NFS based solution. Hardware and network monitoring systems of ATLAS TDAQ are based on NAGIOS and MySQL cluster behind it for accounting and storing the monitoring data collected, IPMI tools, CERN LANDB and the dedicated tools developed by the group, e.g. ConfdbUI. The user management schema deployed in TDAQ environment is founded on the authentication and role management system based on LDAP. External access to the ATLAS online computing facilities is provided by means of the gateways supplied with an accounting system as well. Current activities of the group include deployment of the centralized storage system, testing and validating hardware solutions for future use within the ATLAS TDAQ environment including new multi-core blade servers, developing GUI tools for user authentication and roles management, testing and validating 64-bit OS, and upgrading the existing TDAQ hardware components, authentication servers and the gateways.

Consolidation of cloud computing in ATLAS

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00224309; The ATLAS collaboration; Cordeiro, Cristovao; Hover, John; Kouba, Tomas; Love, Peter; Mcnab, Andrew; Schovancova, Jaroslava; Sobie, Randall; Giordano, Domenico

2017-01-01

Throughout the first half of LHC Run 2, ATLAS cloud computing has undergone a period of consolidation, characterized by building upon previously established systems, with the aim of reducing operational effort, improving robustness, and reaching higher scale. This paper describes the current state of ATLAS cloud computing. Cloud activities are converging on a common contextualization approach for virtual machines, and cloud resources are sharing monitoring and service discovery components. We describe the integration of Vacuum resources, streamlined usage of the Simulation at Point 1 cloud for offline processing, extreme scaling on Amazon compute resources, and procurement of commercial cloud capacity in Europe. Finally, building on the previously established monitoring infrastructure, we have deployed a real-time monitoring and alerting platform which coalesces data from multiple sources, provides flexible visualization via customizable dashboards, and issues alerts and carries out corrective actions in resp...

ATLAS experience with HEP software at the Argonne leadership computing facility

International Nuclear Information System (INIS)

Uram, Thomas D; LeCompte, Thomas J; Benjamin, D

2014-01-01

A number of HEP software packages used by the ATLAS experiment, including GEANT4, ROOT and ALPGEN, have been adapted to run on the IBM Blue Gene supercomputers at the Argonne Leadership Computing Facility. These computers use a non-x86 architecture and have a considerably less rich operating environment than in common use in HEP, but also represent a computing capacity an order of magnitude beyond what ATLAS is presently using via the LCG. The status and potential for making use of leadership-class computing, including the status of integration with the ATLAS production system, is discussed.

ATLAS Experience with HEP Software at the Argonne Leadership Computing Facility

CERN Document Server

LeCompte, T; The ATLAS collaboration; Benjamin, D

2014-01-01

A number of HEP software packages used by the ATLAS experiment, including GEANT4, ROOT and ALPGEN, have been adapted to run on the IBM Blue Gene supercomputers at the Argonne Leadership Computing Facility. These computers use a non-x86 architecture and have a considerably less rich operating environment than in common use in HEP, but also represent a computing capacity an order of magnitude beyond what ATLAS is presently using via the LCG. The status and potential for making use of leadership-class computing, including the status of integration with the ATLAS production system, is discussed.

Consolidation of cloud computing in ATLAS

Science.gov (United States)

Taylor, Ryan P.; Domingues Cordeiro, Cristovao Jose; Giordano, Domenico; Hover, John; Kouba, Tomas; Love, Peter; McNab, Andrew; Schovancova, Jaroslava; Sobie, Randall; ATLAS Collaboration

2017-10-01

Throughout the first half of LHC Run 2, ATLAS cloud computing has undergone a period of consolidation, characterized by building upon previously established systems, with the aim of reducing operational effort, improving robustness, and reaching higher scale. This paper describes the current state of ATLAS cloud computing. Cloud activities are converging on a common contextualization approach for virtual machines, and cloud resources are sharing monitoring and service discovery components. We describe the integration of Vacuum resources, streamlined usage of the Simulation at Point 1 cloud for offline processing, extreme scaling on Amazon compute resources, and procurement of commercial cloud capacity in Europe. Finally, building on the previously established monitoring infrastructure, we have deployed a real-time monitoring and alerting platform which coalesces data from multiple sources, provides flexible visualization via customizable dashboards, and issues alerts and carries out corrective actions in response to problems.

Common accounting system for monitoring the ATLAS distributed computing resources

International Nuclear Information System (INIS)

Karavakis, E; Andreeva, J; Campana, S; Saiz, P; Gayazov, S; Jezequel, S; Sargsyan, L; Schovancova, J; Ueda, I

2014-01-01

This paper covers in detail a variety of accounting tools used to monitor the utilisation of the available computational and storage resources within the ATLAS Distributed Computing during the first three years of Large Hadron Collider data taking. The Experiment Dashboard provides a set of common accounting tools that combine monitoring information originating from many different information sources; either generic or ATLAS specific. This set of tools provides quality and scalable solutions that are flexible enough to support the constantly evolving requirements of the ATLAS user community.

ATLAS and LHC computing on CRAY

CERN Document Server

Haug, Sigve; The ATLAS collaboration

2016-01-01

Access and exploitation of large scale computing resources, such as those offered by general purpose HPC centres, is one import measure for ATLAS and the other Large Hadron Collider experiments in order to meet the challenge posed by the full exploitation of the future data within the constraints of flat budgets. We report on the effort moving the Swiss WLCG T2 computing, serving ATLAS, CMS and LHCb from a dedicated cluster to the large CRAY systems at the Swiss National Supercomputing Centre CSCS. These systems do not only offer very efficient hardware, cooling and highly competent operators, but also have large backfill potentials due to size and multidisciplinary usage and potential gains due to economy at scale. Technical solutions, performance, expected return and future plans are discussed.

ATLAS and LHC computing on CRAY

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00297774; The ATLAS collaboration; Haug, Sigve

2017-01-01

Access and exploitation of large scale computing resources, such as those offered by general purpose HPC centres, is one important measure for ATLAS and the other Large Hadron Collider experiments in order to meet the challenge posed by the full exploitation of the future data within the constraints of flat budgets. We report on the effort of moving the Swiss WLCG T2 computing, serving ATLAS, CMS and LHCb, from a dedicated cluster to the large Cray systems at the Swiss National Supercomputing Centre CSCS. These systems do not only offer very efficient hardware, cooling and highly competent operators, but also have large backfill potentials due to size and multidisciplinary usage and potential gains due to economy at scale. Technical solutions, performance, expected return and future plans are discussed.

ATLAS Distributed Computing in LHC Run2

International Nuclear Information System (INIS)

Campana, Simone

2015-01-01

The ATLAS Distributed Computing infrastructure has evolved after the first period of LHC data taking in order to cope with the challenges of the upcoming LHC Run-2. An increase in both the data rate and the computing demands of the Monte-Carlo simulation, as well as new approaches to ATLAS analysis, dictated a more dynamic workload management system (Prodsys-2) and data management system (Rucio), overcoming the boundaries imposed by the design of the old computing model. In particular, the commissioning of new central computing system components was the core part of the migration toward a flexible computing model. A flexible computing utilization exploring the use of opportunistic resources such as HPC, cloud, and volunteer computing is embedded in the new computing model; the data access mechanisms have been enhanced with the remote access, and the network topology and performance is deeply integrated into the core of the system. Moreover, a new data management strategy, based on a defined lifetime for each dataset, has been defined to better manage the lifecycle of the data. In this note, an overview of an operational experience of the new system and its evolution is presented. (paper)

ATLAS Distributed Computing Monitoring tools during the LHC Run I

CERN Document Server

Schovancova, J; The ATLAS collaboration; Di Girolamo, A; Jezequel, S; Ueda, I; Wenaus, T

2013-01-01

This contribution summarizes evolution of the ATLAS Distributed Computing (ADC) Monitoring project during the LHC Run I. The ADC Monitoring targets at the three groups of customers: ADC Operations team to early identify malfunctions and escalate issues to an activity or a service expert, ATLAS national contacts and sites for the real-time monitoring and long-term measurement of the performance of the provided computing resources, and the ATLAS Management for long-term trends and accounting information about the ATLAS Distributed Computing resources.\\\\ During the LHC Run I a significant development effort has been invested in standardization of the monitoring and accounting applications in order to provide extensive monitoring and accounting suite. ADC Monitoring applications separate the data layer and the visualization layer. The data layer exposes data in a predefined format. The visualization layer is designed bearing in mind visual identity of the provided graphical elements, and re-usability of the visua...

ATLAS Distributed Computing Monitoring tools during the LHC Run I

CERN Document Server

Schovancova, J; The ATLAS collaboration; Di Girolamo, A; Jezequel, S; Ueda, I; Wenaus, T

2014-01-01

This contribution summarizes evolution of the ATLAS Distributed Computing (ADC) Monitoring project during the LHC Run I. The ADC Monitoring targets at the three groups of customers: ADC Operations team to early identify malfunctions and escalate issues to an activity or a service expert, ATLAS national contacts and sites for the real-time monitoring and long-term measurement of the performance of the provided computing resources, and the ATLAS Management for long-term trends and accounting information about the ATLAS Distributed Computing resources.\\\\ During the LHC Run I a significant development effort has been invested in standardization of the monitoring and accounting applications in order to provide extensive monitoring and accounting suite. ADC Monitoring applications separate the data layer and the visualization layer. The data layer exposes data in a predefined format. The visualization layer is designed bearing in mind visual identity of the provided graphical elements, and re-usability of the visua...

ATLAS grid compute cluster with virtualized service nodes

International Nuclear Information System (INIS)

Mejia, J; Stonjek, S; Kluth, S

2010-01-01

The ATLAS Computing Grid consists of several hundred compute clusters distributed around the world as part of the Worldwide LHC Computing Grid (WLCG). The Grid middleware and the ATLAS software which has to be installed on each site, often require a certain Linux distribution and sometimes even specific version thereof. On the other hand, mostly due to maintenance reasons, computer centres install the same operating system and version on all computers. This might lead to problems with the Grid middleware if the local version is different from the one for which it has been developed. At RZG we partly solved this conflict by using virtualization technology for the service nodes. We will present the setup used at RZG and show how it helped to solve the problems described above. In addition we will illustrate the additional advantages gained by the above setup.

Automating usability of ATLAS distributed computing resources

International Nuclear Information System (INIS)

Tupputi, S A; Girolamo, A Di; Kouba, T; Schovancová, J

2014-01-01

The automation of ATLAS Distributed Computing (ADC) operations is essential to reduce manpower costs and allow performance-enhancing actions, which improve the reliability of the system. In this perspective a crucial case is the automatic handling of outages of ATLAS computing sites storage resources, which are continuously exploited at the edge of their capabilities. It is challenging to adopt unambiguous decision criteria for storage resources of non-homogeneous types, sizes and roles. The recently developed Storage Area Automatic Blacklisting (SAAB) tool has provided a suitable solution, by employing an inference algorithm which processes history of storage monitoring tests outcome. SAAB accomplishes both the tasks of providing global monitoring as well as automatic operations on single sites. The implementation of the SAAB tool has been the first step in a comprehensive review of the storage areas monitoring and central management at all levels. Such review has involved the reordering and optimization of SAM tests deployment and the inclusion of SAAB results in the ATLAS Site Status Board with both dedicated metrics and views. The resulting structure allows monitoring the storage resources status with fine time-granularity and automatic actions to be taken in foreseen cases, like automatic outage handling and notifications to sites. Hence, the human actions are restricted to reporting and following up problems, where and when needed. In this work we show SAAB working principles and features. We present also the decrease of human interactions achieved within the ATLAS Computing Operation team. The automation results in a prompt reaction to failures, which leads to the optimization of resource exploitation.

Exploiting Virtualization and Cloud Computing in ATLAS

CERN Multimedia

CERN. Geneva

2012-01-01

This work will present the current status of the Virtualization and Cloud Computing R&D project in ATLAS Distributed Computing. First, strategies for deploying PanDA queues on cloud sites will be discussed, including the introduction of a "cloud factory" for managing cloud VM instances. Ne...

Consolidation of Cloud Computing in ATLAS

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00224309; The ATLAS collaboration; Cordeiro, Cristovao; Di Girolamo, Alessandro; Hover, John; Kouba, Tomas; Love, Peter; Mcnab, Andrew; Schovancova, Jaroslava; Sobie, Randall

2016-01-01

Throughout the first year of LHC Run 2, ATLAS Cloud Computing has undergone a period of consolidation, characterized by building upon previously established systems, with the aim of reducing operational effort, improving robustness, and reaching higher scale. This paper describes the current state of ATLAS Cloud Computing. Cloud activities are converging on a common contextualization approach for virtual machines, and cloud resources are sharing monitoring and service discovery components. We describe the integration of Vac resources, streamlined usage of the High Level Trigger cloud for simulation and reconstruction, extreme scaling on Amazon EC2, and procurement of commercial cloud capacity in Europe. Building on the previously established monitoring infrastructure, we have deployed a real-time monitoring and alerting platform which coalesces data from multiple sources, provides flexible visualization via customizable dashboards, and issues alerts and carries out corrective actions in response to problems. ...

ATLAS computing activities and developments in the Italian Grid cloud

International Nuclear Information System (INIS)

Rinaldi, L; Ciocca, C; K, M; Annovi, A; Antonelli, M; Martini, A; Barberis, D; Brunengo, A; Corosu, M; Barberis, S; Carminati, L; Campana, S; Di, A; Capone, V; Carlino, G; Doria, A; Esposito, R; Merola, L; De, A; Luminari, L

2012-01-01

The large amount of data produced by the ATLAS experiment needs new computing paradigms for data processing and analysis, which involve many computing centres spread around the world. The computing workload is managed by regional federations, called “clouds”. The Italian cloud consists of a main (Tier-1) center, located in Bologna, four secondary (Tier-2) centers, and a few smaller (Tier-3) sites. In this contribution we describe the Italian cloud facilities and the activities of data processing, analysis, simulation and software development performed within the cloud, and we discuss the tests of the new computing technologies contributing to evolution of the ATLAS Computing Model.

Enabling the ATLAS Experiment at the LHC for High Performance Computing

CERN Document Server

AUTHOR|(CDS)2091107; Ereditato, Antonio

In this thesis, I studied the feasibility of running computer data analysis programs from the Worldwide LHC Computing Grid, in particular large-scale simulations of the ATLAS experiment at the CERN LHC, on current general purpose High Performance Computing (HPC) systems. An approach for integrating HPC systems into the Grid is proposed, which has been implemented and tested on the „Todi” HPC machine at the Swiss National Supercomputing Centre (CSCS). Over the course of the test, more than 500000 CPU-hours of processing time have been provided to ATLAS, which is roughly equivalent to the combined computing power of the two ATLAS clusters at the University of Bern. This showed that current HPC systems can be used to efficiently run large-scale simulations of the ATLAS detector and of the detected physics processes. As a first conclusion of my work, one can argue that, in perspective, running large-scale tasks on a few large machines might be more cost-effective than running on relatively small dedicated com...

Extending ATLAS Computing to Commercial Clouds and Supercomputers

CERN Document Server

Nilsson, P; The ATLAS collaboration; Filipcic, A; Klimentov, A; Maeno, T; Oleynik, D; Panitkin, S; Wenaus, T; Wu, W

2014-01-01

The Large Hadron Collider will resume data collection in 2015 with substantially increased computing requirements relative to its first 2009-2013 run. A near doubling of the energy and the data rate, high level of event pile-up, and detector upgrades will mean the number and complexity of events to be analyzed will increase dramatically. A naive extrapolation of the Run 1 experience would suggest that a 5-6 fold increase in computing resources are needed - impossible within the anticipated flat computing budgets in the near future. Consequently ATLAS is engaged in an ambitious program to expand its computing to all available resources, notably including opportunistic use of commercial clouds and supercomputers. Such resources present new challenges in managing heterogeneity, supporting data flows, parallelizing workflows, provisioning software, and other aspects of distributed computing, all while minimizing operational load. We will present the ATLAS experience to date with clouds and supercomputers, and des...

The ATLAS Computing Agora: a resource web site for citizen science projects

CERN Document Server

Bourdarios, Claire; The ATLAS collaboration

2016-01-01

The ATLAS collaboration has recently setup a number of citizen science projects which have a strong IT component and could not have been envisaged without the growth of general public computing resources and network connectivity: event simulation through volunteer computing, algorithms improvement via Machine Learning challenges, event display analysis on citizen science platforms, use of open data, etc. Most of the interactions with volunteers are handled through message boards, but specific outreach material was also developed, giving an enhanced visibility to the ATLAS software and computing techniques, challenges and community. In this talk the Atlas Computing Agora (ACA) web platform will be presented as well as some of the specific material developed for some of the projects.

The Next Generation ARC Middleware and ATLAS Computing Model

International Nuclear Information System (INIS)

Filipčič, Andrej; Cameron, David; Konstantinov, Aleksandr; Karpenko, Dmytro; Smirnova, Oxana

2012-01-01

The distributed NDGF Tier-1 and associated NorduGrid clusters are well integrated into the ATLAS computing environment but follow a slightly different paradigm than other ATLAS resources. The current paradigm does not divide the sites as in the commonly used hierarchical model, but rather treats them as a single storage endpoint and a pool of distributed computing nodes. The next generation ARC middleware with its several new technologies provides new possibilities in development of the ATLAS computing model, such as pilot jobs with pre-cached input files, automatic job migration between the sites, integration of remote sites without connected storage elements, and automatic brokering for jobs with non-standard resource requirements. ARC's data transfer model provides an automatic way for the computing sites to participate in ATLAS’ global task management system without requiring centralised brokering or data transfer services. The powerful API combined with Python and Java bindings can easily be used to build new services for job control and data transfer. Integration of the ARC core into the EMI middleware provides a natural way to implement the new services using the ARC components

Spanish ATLAS Tier-1 &Tier-2 perspective on computing over the next years

CERN Document Server

Gonzalez de la Hoz, Santiago; The ATLAS collaboration

2018-01-01

Since the beginning of the WLCG Project the Spanish ATLAS computer centres have contributed with reliable and stable resources as well as personnel for the ATLAS Collaboration. Our contribution to the ATLAS Tier2s and Tier1s computing resources (disk and CPUs) in the last 10 years has been around 5%, even though the Spanish contribution to the ATLAS detector construction as well as the number of authors are both close to 3%. In 2015 an international advisory committee recommended to revise our contribution according to the participation in the ATLAS experiment. With this scenario, we are optimising the federation of three sites located in Barcelona, Madrid and Valencia, taking into account that the ATLAS collaboration has developed workflows and tools to flexibly use all the resources available to the collaboration, where the Tiered structure is somehow vanishing. In this contribution, we would like to show the evolution and technical updates in the ATLAS Spanish Federated Tier2 and Tier1. Some developments w...

Integration of the Chinese HPC Grid in ATLAS Distributed Computing

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00081160; The ATLAS collaboration

2016-01-01

Fifteen Chinese High Performance Computing sites, many of them on the TOP500 list of most powerful supercomputers, are integrated into a common infrastructure providing coherent access to a user through an interface based on a RESTful interface called SCEAPI. These resources have been integrated into the ATLAS Grid production system using a bridge between ATLAS and SCEAPI which translates the authorization and job submission protocols between the two environments. The ARC Computing Element (ARC CE) forms the bridge using an extended batch system interface to allow job submission to SCEAPI. The ARC CE was setup at the Institute for High Energy Physics, Beijing, in order to be as close as possible to the SCEAPI front-end interface at the Computing Network Information Center, also in Beijing. This paper describes the technical details of the integration between ARC CE and SCEAPI and presents results so far with two supercomputer centers, Tianhe-IA and ERA. These two centers have been the pilots for ATLAS Monte C...

Integration of the Chinese HPC Grid in ATLAS Distributed Computing

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00081160

2017-01-01

Fifteen Chinese High-Performance Computing sites, many of them on the TOP500 list of most powerful supercomputers, are integrated into a common infrastructure providing coherent access to a user through an interface based on a RESTful interface called SCEAPI. These resources have been integrated into the ATLAS Grid production system using a bridge between ATLAS and SCEAPI which translates the authorization and job submission protocols between the two environments. The ARC Computing Element (ARC-CE) forms the bridge using an extended batch system interface to allow job submission to SCEAPI. The ARC-CE was setup at the Institute for High Energy Physics, Beijing, in order to be as close as possible to the SCEAPI front-end interface at the Computing Network Information Center, also in Beijing. This paper describes the technical details of the integration between ARC-CE and SCEAPI and presents results so far with two supercomputer centers, Tianhe-IA and ERA. These two centers have been the pilots for ATLAS Monte C...

ATLAS Distributed Computing experience and performance during the LHC Run-2

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00081160; The ATLAS collaboration

2017-01-01

ATLAS Distributed Computing during LHC Run-1 was challenged by steadily increasing computing, storage and network requirements. In addition, the complexity of processing task workflows and their associated data management requirements led to a new paradigm in the ATLAS computing model for Run-2, accompanied by extensive evolution and redesign of the workflow and data management systems. The new systems were put into production at the end of 2014, and gained robustness and maturity during 2015 data taking. ProdSys2, the new request and task interface; JEDI, the dynamic job execution engine developed as an extension to PanDA; and Rucio, the new data management system, form the core of Run-2 ATLAS distributed computing engine. One of the big changes for Run-2 was the adoption of the Derivation Framework, which moves the chaotic CPU and data intensive part of the user analysis into the centrally organized train production, delivering derived AOD datasets to user groups for final analysis. The effectiveness of the...

ATLAS Distributed Computing experience and performance during the LHC Run-2

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00081160; The ATLAS collaboration

2016-01-01

ATLAS Distributed Computing during LHC Run-1 was challenged by steadily increasing computing, storage and network requirements. In addition, the complexity of processing task workflows and their associated data management requirements led to a new paradigm in the ATLAS computing model for Run-2, accompanied by extensive evolution and redesign of the workflow and data management systems. The new systems were put into production at the end of 2014, and gained robustness and maturity during 2015 data taking. ProdSys2, the new request and task interface; JEDI, the dynamic job execution engine developed as an extension to PanDA; and Rucio, the new data management system, form the core of the Run-2 ATLAS distributed computing engine. One of the big changes for Run-2 was the adoption of the Derivation Framework, which moves the chaotic CPU and data intensive part of the user analysis into the centrally organized train production, delivering derived AOD datasets to user groups for final analysis. The effectiveness of...

Improving ATLAS computing resource utilization with HammerCloud

CERN Document Server

Schovancova, Jaroslava; The ATLAS collaboration

2018-01-01

HammerCloud is a framework to commission, test, and benchmark ATLAS computing resources and components of various distributed systems with realistic full-chain experiment workflows. HammerCloud contributes to ATLAS Distributed Computing (ADC) Operations and automation efforts, providing the automated resource exclusion and recovery tools, that help re-focus operational manpower to areas which have yet to be automated, and improve utilization of available computing resources. We present recent evolution of the auto-exclusion/recovery tools: faster inclusion of new resources in testing machinery, machine learning algorithms for anomaly detection, categorized resources as master vs. slave for the purpose of blacklisting, and a tool for auto-exclusion/recovery of resources triggered by Event Service job failures that is being extended to other workflows besides the Event Service. We describe how HammerCloud helped commissioning various concepts and components of distributed systems: simplified configuration of qu...

ATLAS Distributed Computing Experience and Performance During the LHC Run-2

Science.gov (United States)

Filipčič, A.; ATLAS Collaboration

2017-10-01

ATLAS Distributed Computing during LHC Run-1 was challenged by steadily increasing computing, storage and network requirements. In addition, the complexity of processing task workflows and their associated data management requirements led to a new paradigm in the ATLAS computing model for Run-2, accompanied by extensive evolution and redesign of the workflow and data management systems. The new systems were put into production at the end of 2014, and gained robustness and maturity during 2015 data taking. ProdSys2, the new request and task interface; JEDI, the dynamic job execution engine developed as an extension to PanDA; and Rucio, the new data management system, form the core of Run-2 ATLAS distributed computing engine. One of the big changes for Run-2 was the adoption of the Derivation Framework, which moves the chaotic CPU and data intensive part of the user analysis into the centrally organized train production, delivering derived AOD datasets to user groups for final analysis. The effectiveness of the new model was demonstrated through the delivery of analysis datasets to users just one week after data taking, by completing the calibration loop, Tier-0 processing and train production steps promptly. The great flexibility of the new system also makes it possible to execute part of the Tier-0 processing on the grid when Tier-0 resources experience a backlog during high data-taking periods. The introduction of the data lifetime model, where each dataset is assigned a finite lifetime (with extensions possible for frequently accessed data), was made possible by Rucio. Thanks to this the storage crises experienced in Run-1 have not reappeared during Run-2. In addition, the distinction between Tier-1 and Tier-2 disk storage, now largely artificial given the quality of Tier-2 resources and their networking, has been removed through the introduction of dynamic ATLAS clouds that group the storage endpoint nucleus and its close-by execution satellite sites. All stable

The Future of PanDA in ATLAS Distributed Computing

CERN Document Server

De, Kaushik; The ATLAS collaboration; Maeno, Tadashi; Nilsson, Paul; Oleynik, Danila; Panitkin, Sergey; Petrosyan, Artem; Schovancova, Jaroslava; Vaniachine, Alexandre; Wenaus, Torre

2015-01-01

Experiments at the Large Hadron Collider (LHC) face unprecedented computing challenges. Heterogeneous resources are distributed worldwide at hundreds of sites, thousands of physicists analyze the data remotely, the volume of processed data is beyond the exabyte scale, while data processing requires more than a few billion hours of computing usage per year. The PanDA (Production and Distributed Analysis) system was developed to meet the scale and complexity of LHC distributed computing for the ATLAS experiment. In the process, the old batch job paradigm of locally managed computing in HEP was discarded in favor of a far more automated, flexible and scalable model. The success of PanDA in ATLAS is leading to widespread adoption and testing by other experiments. PanDA is the first exascale workload management system in HEP, already operating at more than a million computing jobs per day, and processing over an exabyte of data in 2013. There are many new challenges that PanDA will face in the near future, in addi...

ATLAS Distributed Computing Shift Operation in the first 2 full years of LHC data taking

CERN Document Server

Schovancová, J; The ATLAS collaboration; Elmsheuser, J; Jézéquel, S; Negri, G; Ozturk, N; Sakamoto, H; Slater, M; Smirnov, Y; Ueda, I; Van Der Ster, D C

2012-01-01

ATLAS Distributed Computing organized 3 teams to support data processing at Tier-0 facility at CERN, data reprocessing, data management operations, Monte Carlo simulation production, and physics analysis at the ATLAS computing centers located world-wide. In this paper we describe how these teams ensure that the ATLAS experiment data is delivered to the ATLAS physicists in a timely manner in the glamorous era of the LHC data taking. We describe experience with ways how to improve degraded service performance, we detail on the Distributed Analysis support over the exciting period of the computing model evolution.

Integration of the Chinese HPC Grid in ATLAS Distributed Computing

Science.gov (United States)

Filipčič, A.; ATLAS Collaboration

2017-10-01

Fifteen Chinese High-Performance Computing sites, many of them on the TOP500 list of most powerful supercomputers, are integrated into a common infrastructure providing coherent access to a user through an interface based on a RESTful interface called SCEAPI. These resources have been integrated into the ATLAS Grid production system using a bridge between ATLAS and SCEAPI which translates the authorization and job submission protocols between the two environments. The ARC Computing Element (ARC-CE) forms the bridge using an extended batch system interface to allow job submission to SCEAPI. The ARC-CE was setup at the Institute for High Energy Physics, Beijing, in order to be as close as possible to the SCEAPI front-end interface at the Computing Network Information Center, also in Beijing. This paper describes the technical details of the integration between ARC-CE and SCEAPI and presents results so far with two supercomputer centers, Tianhe-IA and ERA. These two centers have been the pilots for ATLAS Monte Carlo Simulation in SCEAPI and have been providing CPU power since fall 2015.

Evolution of the ATLAS Distributed Computing during the LHC long shutdown

CERN Document Server

Campana, S; The ATLAS collaboration

2013-01-01

The ATLAS Distributed Computing project (ADC) was established in 2007 to develop and operate a framework, following the ATLAS computing model, to enable data storage, processing and bookkeeping on top of the WLCG distributed infrastructure. ADC development has always been driven by operations and this contributed to its success. The system has fulfilled the demanding requirements of ATLAS, daily consolidating worldwide up to 1PB of data and running more than 1.5 million payloads distributed globally, supporting almost one thousand concurrent distributed analysis users. Comprehensive automation and monitoring minimized the operational manpower required. The flexibility of the system to adjust to operational needs has been important to the success of the ATLAS physics program. The LHC shutdown in 2013-2015 affords an opportunity to improve the system in light of operational experience and scale it to cope with the demanding requirements of 2015 and beyond, most notably a much higher trigger rate and event pileu...

ATLAS Distributed Computing Monitoring tools during the LHC Run I

Science.gov (United States)

Schovancová, J.; Campana, S.; Di Girolamo, A.; Jézéquel, S.; Ueda, I.; Wenaus, T.; Atlas Collaboration

2014-06-01

This contribution summarizes evolution of the ATLAS Distributed Computing (ADC) Monitoring project during the LHC Run I. The ADC Monitoring targets at the three groups of customers: ADC Operations team to early identify malfunctions and escalate issues to an activity or a service expert, ATLAS national contacts and sites for the real-time monitoring and long-term measurement of the performance of the provided computing resources, and the ATLAS Management for long-term trends and accounting information about the ATLAS Distributed Computing resources. During the LHC Run I a significant development effort has been invested in standardization of the monitoring and accounting applications in order to provide extensive monitoring and accounting suite. ADC Monitoring applications separate the data layer and the visualization layer. The data layer exposes data in a predefined format. The visualization layer is designed bearing in mind visual identity of the provided graphical elements, and re-usability of the visualization bits across the different tools. A rich family of various filtering and searching options enhancing available user interfaces comes naturally with the data and visualization layer separation. With a variety of reliable monitoring data accessible through standardized interfaces, the possibility of automating actions under well defined conditions correlating multiple data sources has become feasible. In this contribution we discuss also about the automated exclusion of degraded resources and their automated recovery in various activities.

Evolution of the ATLAS distributed computing system during the LHC long shutdown

Science.gov (United States)

Campana, S.; Atlas Collaboration

2014-06-01

The ATLAS Distributed Computing project (ADC) was established in 2007 to develop and operate a framework, following the ATLAS computing model, to enable data storage, processing and bookkeeping on top of the Worldwide LHC Computing Grid (WLCG) distributed infrastructure. ADC development has always been driven by operations and this contributed to its success. The system has fulfilled the demanding requirements of ATLAS, daily consolidating worldwide up to 1 PB of data and running more than 1.5 million payloads distributed globally, supporting almost one thousand concurrent distributed analysis users. Comprehensive automation and monitoring minimized the operational manpower required. The flexibility of the system to adjust to operational needs has been important to the success of the ATLAS physics program. The LHC shutdown in 2013-2015 affords an opportunity to improve the system in light of operational experience and scale it to cope with the demanding requirements of 2015 and beyond, most notably a much higher trigger rate and event pileup. We will describe the evolution of the ADC software foreseen during this period. This includes consolidating the existing Production and Distributed Analysis framework (PanDA) and ATLAS Grid Information System (AGIS), together with the development and commissioning of next generation systems for distributed data management (DDM/Rucio) and production (Prodsys-2). We will explain how new technologies such as Cloud Computing and NoSQL databases, which ATLAS investigated as R&D projects in past years, will be integrated in production. Finally, we will describe more fundamental developments such as breaking job-to-data locality by exploiting storage federations and caches, and event level (rather than file or dataset level) workload engines.

Evolution of the ATLAS distributed computing system during the LHC long shutdown

International Nuclear Information System (INIS)

Campana, S

2014-01-01

The ATLAS Distributed Computing project (ADC) was established in 2007 to develop and operate a framework, following the ATLAS computing model, to enable data storage, processing and bookkeeping on top of the Worldwide LHC Computing Grid (WLCG) distributed infrastructure. ADC development has always been driven by operations and this contributed to its success. The system has fulfilled the demanding requirements of ATLAS, daily consolidating worldwide up to 1 PB of data and running more than 1.5 million payloads distributed globally, supporting almost one thousand concurrent distributed analysis users. Comprehensive automation and monitoring minimized the operational manpower required. The flexibility of the system to adjust to operational needs has been important to the success of the ATLAS physics program. The LHC shutdown in 2013-2015 affords an opportunity to improve the system in light of operational experience and scale it to cope with the demanding requirements of 2015 and beyond, most notably a much higher trigger rate and event pileup. We will describe the evolution of the ADC software foreseen during this period. This includes consolidating the existing Production and Distributed Analysis framework (PanDA) and ATLAS Grid Information System (AGIS), together with the development and commissioning of next generation systems for distributed data management (DDM/Rucio) and production (Prodsys-2). We will explain how new technologies such as Cloud Computing and NoSQL databases, which ATLAS investigated as R and D projects in past years, will be integrated in production. Finally, we will describe more fundamental developments such as breaking job-to-data locality by exploiting storage federations and caches, and event level (rather than file or dataset level) workload engines.

Backfilling the Grid with Containerized BOINC in the ATLAS computing

CERN Document Server

Wu, Wenjing; The ATLAS collaboration

2018-01-01

Virtualization is a commonly used solution for utilizing the opportunistic computing resources in the HEP field, as it provides a unified software and OS layer that the HEP computing tasks require over the heterogeneous opportunistic computing resources. However there is always performance penalty with virtualization, especially for short jobs which are always the case for volunteer computing tasks, the overhead of virtualization becomes a big portion in the wall time, hence it leads to low CPU efficiency of the jobs. With the wide usage of containers in HEP computing, we explore the possibility of adopting the container technology into the ATLAS BOINC project, hence we implemented a Native version in BOINC, which uses the singularity container or direct usage of the target OS to replace VirtualBox. In this paper, we will discuss 1) the implementation and workflow of the Native version in the ATLAS BOINC; 2) the performance measurement of the Native version comparing to the previous Virtualization version. 3)...

The ATLAS Distributed Computing project for LHC Run-2 and beyond.

CERN Document Server

Di Girolamo, Alessandro; The ATLAS collaboration

2015-01-01

The ATLAS Distributed Computing infrastructure has evolved after the first period of LHC data taking in order to cope with the challenges of the upcoming LHC Run2. An increased data rate and computing demands of the Monte-Carlo simulation, as well as new approaches to ATLAS analysis, dictated a more dynamic workload management system (ProdSys2) and data management system (Rucio), overcoming the boundaries imposed by the design of the old computing model. In particular, the commissioning of new central computing system components was the core part of the migration toward the flexible computing model. The flexible computing utilization exploring the opportunistic resources such as HPC, cloud, and volunteer computing is embedded in the new computing model, the data access mechanisms have been enhanced with the remote access, and the network topology and performance is deeply integrated into the core of the system. Moreover a new data management strategy, based on defined lifetime for each dataset, has been defin...

Evolution of the ATLAS Distributed Computing system during the LHC Long shutdown

CERN Document Server

Campana, S; The ATLAS collaboration

2014-01-01

The ATLAS Distributed Computing project (ADC) was established in 2007 to develop and operate a framework, following the ATLAS computing model, to enable data storage, processing and bookkeeping on top of the WLCG distributed infrastructure. ADC development has always been driven by operations and this contributed to its success. The system has fulfilled the demanding requirements of ATLAS, daily consolidating worldwide up to 1PB of data and running more than 1.5 million payloads distributed globally, supporting almost one thousand concurrent distributed analysis users. Comprehensive automation and monitoring minimized the operational manpower required. The flexibility of the system to adjust to operational needs has been important to the success of the ATLAS physics program. The LHC shutdown in 2013-2015 affords an opportunity to improve the system in light of operational experience and scale it to cope with the demanding requirements of 2015 and beyond, most notably a much higher trigger rate and event pileu...

Xcache in the ATLAS Distributed Computing Environment

CERN Document Server

Hanushevsky, Andrew; The ATLAS collaboration

2018-01-01

Built upon the Xrootd Proxy Cache (Xcache), we developed additional features to adapt the ATLAS distributed computing and data environment, especially its data management system RUCIO, to help improve the cache hit rate, as well as features that make the Xcache easy to use, similar to the way the Squid cache is used by the HTTP protocol. We are optimizing Xcache for the HPC environments, and adapting the HL-LHC Data Lakes design as its component for data delivery. We packaged the software in CVMFS, in Docker and Singularity containers in order to standardize the deployment and reduce the cost to resolve issues at remote sites. We are also integrating it into RUCIO as a volatile storage systems, and into various ATLAS workflow such as user analysis,

Complete distributed computing environment for a HEP experiment: experience with ARC-connected infrastructure for ATLAS

International Nuclear Information System (INIS)

Read, A; Taga, A; O-Saada, F; Pajchel, K; Samset, B H; Cameron, D

2008-01-01

Computing and storage resources connected by the Nordugrid ARC middleware in the Nordic countries, Switzerland and Slovenia are a part of the ATLAS computing Grid. This infrastructure is being commissioned with the ongoing ATLAS Monte Carlo simulation production in preparation for the commencement of data taking in 2008. The unique non-intrusive architecture of ARC, its straightforward interplay with the ATLAS Production System via the Dulcinea executor, and its performance during the commissioning exercise is described. ARC support for flexible and powerful end-user analysis within the GANGA distributed analysis framework is also shown. Whereas the storage solution for this Grid was earlier based on a large, distributed collection of GridFTP-servers, the ATLAS computing design includes a structured SRM-based system with a limited number of storage endpoints. The characteristics, integration and performance of the old and new storage solutions are presented. Although the hardware resources in this Grid are quite modest, it has provided more than double the agreed contribution to the ATLAS production with an efficiency above 95% during long periods of stable operation

Complete distributed computing environment for a HEP experiment: experience with ARC-connected infrastructure for ATLAS

Energy Technology Data Exchange (ETDEWEB)

Read, A; Taga, A; O-Saada, F; Pajchel, K; Samset, B H; Cameron, D [Department of Physics, University of Oslo, P.b. 1048 Blindern, N-0316 Oslo (Norway)], E-mail: a.l.read@fys.uio.no

2008-07-15

Computing and storage resources connected by the Nordugrid ARC middleware in the Nordic countries, Switzerland and Slovenia are a part of the ATLAS computing Grid. This infrastructure is being commissioned with the ongoing ATLAS Monte Carlo simulation production in preparation for the commencement of data taking in 2008. The unique non-intrusive architecture of ARC, its straightforward interplay with the ATLAS Production System via the Dulcinea executor, and its performance during the commissioning exercise is described. ARC support for flexible and powerful end-user analysis within the GANGA distributed analysis framework is also shown. Whereas the storage solution for this Grid was earlier based on a large, distributed collection of GridFTP-servers, the ATLAS computing design includes a structured SRM-based system with a limited number of storage endpoints. The characteristics, integration and performance of the old and new storage solutions are presented. Although the hardware resources in this Grid are quite modest, it has provided more than double the agreed contribution to the ATLAS production with an efficiency above 95% during long periods of stable operation.

ATLAS Distributed Computing Operations: Experience and improvements after 2 full years of data-taking

International Nuclear Information System (INIS)

Jézéquel, S; Stewart, G

2012-01-01

This paper summarizes operational experience and improvements in ATLAS computing infrastructure in 2010 and 2011. ATLAS has had 2 periods of data taking, with many more events recorded in 2011 than in 2010. It ran 3 major reprocessing campaigns. The activity in 2011 was similar to 2010, but scalability issues had to be addressed due to the increase in luminosity and trigger rate. Based on improved monitoring of ATLAS Grid computing, the evolution of computing activities (data/group production, their distribution and grid analysis) over time is presented. The main changes in the implementation of the computing model that will be shown are: the optimization of data distribution over the Grid, according to effective transfer rate and site readiness for analysis; the progressive dismantling of the cloud model, for data distribution and data processing; software installation migration to cvmfs; changing database access to a Frontier/squid infrastructure.

What Data to Co-register for Computing Atlases

Science.gov (United States)

Yeo, B.T. Thomas; Sabuncu, Mert; Mohlberg, Hartmut; Amunts, Katrin; Zilles, Karl; Golland, Polina; Fischl, Bruce

2015-01-01

We argue that registration should be thought of as a means to an end, and not as a goal by itself. In particular, we consider the problem of predicting the locations of hidden labels of a test image using observable features, given a training set with both the hidden labels and observable features. For example, the hidden labels could be segmentation labels or activation regions in fMRI, while the observable features could be sulcal geometry or MR intensity. We analyze a probabilistic framework for computing an optimal atlas, and the subsequent registration of a new subject using only the observable features to optimize the hidden label alignment to the training set. We compare two approaches for co-registering training images for the atlas construction: the traditional approach of only using observable features and a novel approach of only using hidden labels. We argue that the alternative approach is superior particularly when the relationship between the hidden labels and observable features is complex and unknown. As an application, we consider the task of registering cortical folds to optimize Brodmann area localization. We show that the alignment of the Brodmann areas improves by up to 25% when using the alternative atlas compared with the traditional atlas. To the best of our knowledge, these are the most accurate Brodmann area localization results (achieved via cortical fold registration) reported to date. PMID:26082678

The future of PanDA in ATLAS distributed computing

Science.gov (United States)

De, K.; Klimentov, A.; Maeno, T.; Nilsson, P.; Oleynik, D.; Panitkin, S.; Petrosyan, A.; Schovancova, J.; Vaniachine, A.; Wenaus, T.

2015-12-01

Experiments at the Large Hadron Collider (LHC) face unprecedented computing challenges. Heterogeneous resources are distributed worldwide at hundreds of sites, thousands of physicists analyse the data remotely, the volume of processed data is beyond the exabyte scale, while data processing requires more than a few billion hours of computing usage per year. The PanDA (Production and Distributed Analysis) system was developed to meet the scale and complexity of LHC distributed computing for the ATLAS experiment. In the process, the old batch job paradigm of locally managed computing in HEP was discarded in favour of a far more automated, flexible and scalable model. The success of PanDA in ATLAS is leading to widespread adoption and testing by other experiments. PanDA is the first exascale workload management system in HEP, already operating at more than a million computing jobs per day, and processing over an exabyte of data in 2013. There are many new challenges that PanDA will face in the near future, in addition to new challenges of scale, heterogeneity and increasing user base. PanDA will need to handle rapidly changing computing infrastructure, will require factorization of code for easier deployment, will need to incorporate additional information sources including network metrics in decision making, be able to control network circuits, handle dynamically sized workload processing, provide improved visualization, and face many other challenges. In this talk we will focus on the new features, planned or recently implemented, that are relevant to the next decade of distributed computing workload management using PanDA.

The Future of Distributed Computing Systems in ATLAS: Boldly Venturing Beyond Grids

CERN Document Server

Barreiro Megino, Fernando Harald; The ATLAS collaboration

2018-01-01

The Production and Distributed Analysis system (PanDA) for the ATLAS experiment at the Large Hadron Collider has seen big changes over the past couple of years to accommodate new types of distributed computing resources: clouds, HPCs, volunteer computers and other external resources. While PanDA was originally designed for fairly homogeneous resources available through the Worldwide LHC Computing Grid, the new resources are heterogeneous, at diverse scales and with diverse interfaces. Up to a fifth of the resources available to ATLAS are of such new types and require special techniques for integration into PanDA. In this talk, we present the nature and scale of these resources. We provide an overview of the various challenges faced, spanning infrastructure, software distribution, workload requirements, scaling requirements, workflow management, data management, network provisioning, and associated software and computing facilities. We describe the strategies for integrating these heterogeneous resources into ...

Evolution of the Atlas data and computing model for a Tier-2 in the EGI infrastructure

CERN Document Server

Fernandez, A; The ATLAS collaboration; AMOROS, G; VILLAPLANA, M; FASSI, F; KACI, M; LAMAS, A; OLIVER, E; SALT, J; SANCHEZ, J; SANCHEZ, V

2012-01-01

ABSTRAC ISCG 2012 Evolution of the Atlas data and computing model for a Tier2 in the EGI infrastructure During last years the Atlas computing model has moved from a more strict design, where every Tier2 had a liaison and a network dependence from a Tier1, to a more meshed approach where every cloud could be connected. Evolution of ATLAS data models requires changes in ATLAS Tier2s policy for the data replication, dynamic data caching and remote data access. It also requires rethinking the network infrastructure to enable any Tier2 and associated Tier3 to easily connect to any Tier1 or Tier2. Tier2s are becoming more and more important in the ATLAS computing model as it allows more data to be readily accessible for analysis jobs to all users, independently of their geographical location. The Tier2s disk space has been reserved for real, simulated, calibration and alignment, group, and user data. A buffer disk space is needed for input and output data for simulations jobs. Tier2s are going to be used more effic...

Data analytics in the ATLAS Distributed Computing

CERN Document Server

Vukotic, Ilija; The ATLAS collaboration; Bryant, Lincoln

2015-01-01

The ATLAS Data analytics effort is focused on creating systems which provide the ATLAS ADC with new capabilities for understanding distributed systems and overall operational performance. These capabilities include: warehousing information from multiple systems (the production and distributed analysis system - PanDA, the distributed data management system - Rucio, the file transfer system, various monitoring services etc. ); providing a platform to execute arbitrary data mining and machine learning algorithms over aggregated data; satisfy a variety of use cases for different user roles; host new third party analytics services on a scalable compute platform. We describe the implemented system where: data sources are existing RDBMS (Oracle) and Flume collectors; a Hadoop cluster is used to store the data; native Hadoop and Apache Pig scripts are used for data aggregation; and R for in-depth analytics. Part of the data is indexed in ElasticSearch so both simpler investigations and complex dashboards can be made ...

ATLAS Cloud R&D

CERN Document Server

Panitkin, S; The ATLAS collaboration; Caballero Bejar, J; Benjamin, D; DiGirolamo, A; Gable, I; Hendrix, V; Hover, J; Kucharczuk, K; Medrano LLamas, R; Love, P; Ohman, H; Paterson, M; Sobie, R; Taylor, R; Walker, R; Zaytsev, A

2014-01-01

The computing model of the ATLAS experiment was designed around the concept of grid computing and, since the start of data taking, this model has proven very successful. However, new cloud computing technologies bring attractive features to improve the operations and elasticity of scientific distributed computing. ATLAS sees grid and cloud computing as complementary technologies that will coexist at different levels of resource abstraction, and two years ago created an R&D working group to investigate the different integration scenarios. The ATLAS Cloud Computing R&D has been able to demonstrate the feasibility of offloading work from grid to cloud sites and, as of today, is able to integrate transparently various cloud resources into the PanDA workload management system. The ATLAS Cloud Computing R&D is operating various PanDA queues on private and public resources and has provided several hundred thousand CPU days to the experiment. As a result, the ATLAS Cloud Computing R&D group has gained...

ATLAS Cloud R&D

Science.gov (United States)

Panitkin, Sergey; Barreiro Megino, Fernando; Caballero Bejar, Jose; Benjamin, Doug; Di Girolamo, Alessandro; Gable, Ian; Hendrix, Val; Hover, John; Kucharczyk, Katarzyna; Medrano Llamas, Ramon; Love, Peter; Ohman, Henrik; Paterson, Michael; Sobie, Randall; Taylor, Ryan; Walker, Rodney; Zaytsev, Alexander; Atlas Collaboration

2014-06-01

The computing model of the ATLAS experiment was designed around the concept of grid computing and, since the start of data taking, this model has proven very successful. However, new cloud computing technologies bring attractive features to improve the operations and elasticity of scientific distributed computing. ATLAS sees grid and cloud computing as complementary technologies that will coexist at different levels of resource abstraction, and two years ago created an R&D working group to investigate the different integration scenarios. The ATLAS Cloud Computing R&D has been able to demonstrate the feasibility of offloading work from grid to cloud sites and, as of today, is able to integrate transparently various cloud resources into the PanDA workload management system. The ATLAS Cloud Computing R&D is operating various PanDA queues on private and public resources and has provided several hundred thousand CPU days to the experiment. As a result, the ATLAS Cloud Computing R&D group has gained a significant insight into the cloud computing landscape and has identified points that still need to be addressed in order to fully utilize this technology. This contribution will explain the cloud integration models that are being evaluated and will discuss ATLAS' learning during the collaboration with leading commercial and academic cloud providers.

AGIS: The ATLAS Grid Information System

CERN Document Server

Anisenkov, A; The ATLAS collaboration; Klimentov, A; Senchenko, A

2012-01-01

The ATLAS Computing model embraces the Grid paradigm and a high degree of decentralization and computing resources able to meet ATLAS requirements of petabytes scale data operations. In this paper we present ATLAS Grid Information System (AGIS) designed to integrate configuration and status information about resources, services and topology of whole ATLAS Grid needed by ATLAS Distributed Computing applications and services.

PanDA for ATLAS Distributed Computing in the Next Decade

CERN Document Server

Barreiro Megino, Fernando Harald; The ATLAS collaboration

2016-01-01

The Production and Distributed Analysis (PanDA) system has been developed to meet ATLAS production and analysis requirements for a data-driven workload management system capable of operating at the Large Hadron Collider (LHC) data processing scale. Heterogeneous resources used by the ATLAS experiment are distributed worldwide at hundreds of sites, thousands of physicists analyse the data remotely, the volume of processed data is beyond the exabyte scale, dozens of scientific applications are supported, while data processing requires more than a few billion hours of computing usage per year. PanDA performed very well over the last decade including the LHC Run 1 data taking period. However, it was decided to upgrade the whole system concurrently with the LHC’s first long shutdown in order to cope with rapidly changing computing infrastructure. After two years of reengineering efforts, PanDA has embedded capabilities for fully dynamic and flexible workload management. The static batch job paradigm was discarde...

PanDA for ATLAS distributed computing in the next decade

CERN Document Server

AUTHOR|(SzGeCERN)643806; The ATLAS collaboration; De, Kaushik; Klimentov, Alexei; Maeno, Tadashi; Nilsson, Paul; Oleynik, Danila; Padolski, Siarhei; Panitkin, Sergey; Wenaus, Torre

2017-01-01

The Production and Distributed Analysis (PanDA) system has been developed to meet ATLAS production and analysis requirements for a data-driven workload management system capable of operating at the Large Hadron Collider (LHC) data processing scale. Heterogeneous resources used by the ATLAS experiment are distributed worldwide at hundreds of sites, thousands of physicists analyse the data remotely, the volume of processed data is beyond the exabyte scale, dozens of scientific applications are supported, while data processing requires more than a few billion hours of computing usage per year. PanDA performed very well over the last decade including the LHC Run 1 data taking period. However, it was decided to upgrade the whole system concurrently with the LHC’s first long shutdown in order to cope with rapidly changing computing infrastructure. After two years of reengineering efforts, PanDA has embedded capabilities for fully dynamic and flexible workload management. The static batch job paradigm was discarde...

AGIS: The ATLAS Grid Information System

Science.gov (United States)

Anisenkov, A.; Di Girolamo, A.; Klimentov, A.; Oleynik, D.; Petrosyan, A.; Atlas Collaboration

2014-06-01

ATLAS, a particle physics experiment at the Large Hadron Collider at CERN, produced petabytes of data annually through simulation production and tens of petabytes of data per year from the detector itself. The ATLAS computing model embraces the Grid paradigm and a high degree of decentralization and computing resources able to meet ATLAS requirements of petabytes scale data operations. In this paper we describe the ATLAS Grid Information System (AGIS), designed to integrate configuration and status information about resources, services and topology of the computing infrastructure used by the ATLAS Distributed Computing applications and services.

ATLAS computing on Swiss Cloud SWITCHengines

Science.gov (United States)

Haug, S.; Sciacca, F. G.; ATLAS Collaboration

2017-10-01

Consolidation towards more computing at flat budgets beyond what pure chip technology can offer, is a requirement for the full scientific exploitation of the future data from the Large Hadron Collider at CERN in Geneva. One consolidation measure is to exploit cloud infrastructures whenever they are financially competitive. We report on the technical solutions and the performances used and achieved running simulation tasks for the ATLAS experiment on SWITCHengines. SWITCHengines is a new infrastructure as a service offered to Swiss academia by the National Research and Education Network SWITCH. While solutions and performances are general, financial considerations and policies, on which we also report, are country specific.

ATLAS computing on Swiss Cloud SWITCHengines

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00215485; The ATLAS collaboration; Sciacca, Gianfranco

2017-01-01

Consolidation towards more computing at flat budgets beyond what pure chip technology can offer, is a requirement for the full scientific exploitation of the future data from the Large Hadron Collider at CERN in Geneva. One consolidation measure is to exploit cloud infrastructures whenever they are financially competitive. We report on the technical solutions and the performances used and achieved running simulation tasks for the ATLAS experiment on SWITCHengines. SWITCHengines is a new infrastructure as a service offered to Swiss academia by the National Research and Education Network SWITCH. While solutions and performances are general, financial considerations and policies, on which we also report, are country specific.

AGIS: The ATLAS Grid Information System

CERN Document Server

Anisenkov, Alexey; Di Girolamo, Alessandro; Gayazov, Stavro; Klimentov, Alexei; Oleynik, Danila; Senchenko, Alexander

2012-01-01

ATLAS is a particle physics experiment at the Large Hadron Collider at CERN. The experiment produces petabytes of data annually through simulation production and tens petabytes of data per year from the detector itself. The ATLAS Computing model embraces the Grid paradigm and a high degree of decentralization and computing resources able to meet ATLAS requirements of petabytes scale data operations. In this paper we present ATLAS Grid Information System (AGIS) designed to integrate configuration and status information about resources, services and topology of whole ATLAS Grid needed by ATLAS Distributed Computing applications and services.

An ATLAS distributed computing architecture for HL-LHC

CERN Document Server

Campana, Simone; The ATLAS collaboration

2017-01-01

The ATLAS collaboration started a process to understand the computing needs for the High Luminosity LHC era. Based on our best understanding of the computing model input parameters for the HL-LHC data taking conditions, results indicate the need for a larger amount of computational and storage resources with respect of the projection of constant yearly budget for computing in 2026. Filling the gap between the projection and the needs will be one of the challenges in preparation for LHC Run-4. While the gains from improvements in offline software will play a crucial role in this process, a different model for data processing, management, access and bookkeeping should also be envisaged to optimise resource usage. In this contribution we will describe a straw man of this model, founded on basic principles such as single event level granularity for data processing and virtual data. We will explain how the current architecture will evolve adiabatically into the future distributed computing system, through the prot...

Two-stage atlas subset selection in multi-atlas based image segmentation

Energy Technology Data Exchange (ETDEWEB)

Zhao, Tingting, E-mail: tingtingzhao@mednet.ucla.edu; Ruan, Dan, E-mail: druan@mednet.ucla.edu [The Department of Radiation Oncology, University of California, Los Angeles, California 90095 (United States)

2015-06-15

Purpose: Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. Methods: An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. Results: The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. Conclusions: The authors

Two-stage atlas subset selection in multi-atlas based image segmentation.

Science.gov (United States)

Zhao, Tingting; Ruan, Dan

2015-06-01

Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. The authors have developed a novel two-stage atlas

Two-stage atlas subset selection in multi-atlas based image segmentation

International Nuclear Information System (INIS)

Zhao, Tingting; Ruan, Dan

2015-01-01

Purpose: Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. Methods: An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. Results: The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. Conclusions: The authors

The ATLAS computing challenge for HL-LHC

CERN Document Server

Campana, Simone; The ATLAS collaboration

2016-01-01

The ATLAS experiment successfully commissioned a software and computing infrastructure to support the physics program during LHC Run 2. The next phases of the accelerator upgrade will present new challenges in the offline area. In particular, at High Luminosity LHC (also known as Run 4) the data taking conditions will be very demanding in terms of computing resources: between 5 and 10 KHz of event rate from the HLT to be reconstructed (and possibly further reprocessed) with an average pile-up of up to 200 events per collision and an equivalent number of simulated samples to be produced. The same parameters for the current run are lower by up to an order of magnitude. While processing and storage resources would need to scale accordingly, the funding situation allows one at best to consider a flat budget over the next few years for offline computing needs. In this paper we present a study quantifying the challenge in terms of computing resources for HL-LHC and present ideas about the possible evolution of the ...

The evolving role of Tier2s in ATLAS with the new Computing and Data Distribution model

International Nuclear Information System (INIS)

González de la Hoz, S

2012-01-01

Originally the ATLAS Computing and Data Distribution model assumed that the Tier-2s should keep on disk collectively at least one copy of all “active” AOD and DPD datasets. Evolution of ATLAS Computing and Data model requires changes in ATLAS Tier-2s policy for the data replication, dynamic data caching and remote data access. Tier-2 operations take place completely asynchronously with respect to data taking. Tier-2s do simulation and user analysis. Large-scale reprocessing jobs on real data are at first taking place mostly at Tier-1s but will progressively be shared with Tier-2s as well. The availability of disk space at Tier-2s is extremely important in the ATLAS Computing model as it allows more data to be readily accessible for analysis jobs to all users, independently of their geographical location. The Tier-2s disk space has been reserved for real, simulated, calibration and alignment, group, and user data. A buffer disk space is needed for input and output data for simulations jobs. Tier-2s are going to be used more efficiently. In this way Tier-1s and Tier-2s are becoming more equivalent for the network and the hierarchy of Tier-1, 2 is less strict. This paper presents the usage of Tier-2s resources in different Grid activities, caching of data at Tier-2s, and their role in the analysis in the new ATLAS Computing and Data model.

Computing shifts to monitor ATLAS distributed computing infrastructure and operations

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00068610; The ATLAS collaboration; Barberis, Dario; Crepe-Renaudin, Sabine Chrystel; De, Kaushik; Fassi, Farida; Stradling, Alden; Svatos, Michal; Vartapetian, Armen; Wolters, Helmut

2017-01-01

The ATLAS Distributed Computing (ADC) group established a new Computing Run Coordinator (CRC) shift at the start of LHC Run 2 in 2015. The main goal was to rely on a person with a good overview of the ADC activities to ease the ADC experts’ workload. The CRC shifter keeps track of ADC tasks related to their fields of expertise and responsibility. At the same time, the shifter maintains a global view of the day-to-day operations of the ADC system. During Run 1, this task was accomplished by a person of the expert team called the ADC Manager on Duty (AMOD), a position that was removed during the shutdown period due to the reduced number and availability of ADC experts foreseen for Run 2. The CRC position was proposed to cover some of the AMODs former functions, while allowing more people involved in computing to participate. In this way, CRC shifters help with the training of future ADC experts. The CRC shifters coordinate daily ADC shift operations, including tracking open issues, reporting, and representing...

Computing shifts to monitor ATLAS distributed computing infrastructure and operations

CERN Document Server

Adam Bourdarios, Claire; The ATLAS collaboration

2016-01-01

The ATLAS Distributed Computing (ADC) group established a new Computing Run Coordinator (CRC) shift at the start of LHC Run2 in 2015. The main goal was to rely on a person with a good overview of the ADC activities to ease the ADC experts' workload. The CRC shifter keeps track of ADC tasks related to their fields of expertise and responsibility. At the same time, the shifter maintains a global view of the day-to-day operations of the ADC system. During Run1, this task was accomplished by the ADC Manager on Duty (AMOD), a position that was removed during the shutdown period due to the reduced number and availability of ADC experts foreseen for Run2. The CRC position was proposed to cover some of the AMOD’s former functions, while allowing more people involved in computing to participate. In this way, CRC shifters help train future ADC experts. The CRC shifters coordinate daily ADC shift operations, including tracking open issues, reporting, and representing ADC in relevant meetings. The CRC also facilitates ...

Evolution of the ATLAS PanDA workload management system for exascale computational science

International Nuclear Information System (INIS)

Maeno, T; Klimentov, A; Panitkin, S; Schovancova, J; Wenaus, T; Yu, D; De, K; Nilsson, P; Oleynik, D; Petrosyan, A; Vaniachine, A

2014-01-01

An important foundation underlying the impressive success of data processing and analysis in the ATLAS experiment [1] at the LHC [2] is the Production and Distributed Analysis (PanDA) workload management system [3]. PanDA was designed specifically for ATLAS and proved to be highly successful in meeting all the distributed computing needs of the experiment. However, the core design of PanDA is not experiment specific. The PanDA workload management system is capable of meeting the needs of other data intensive scientific applications. Alpha-Magnetic Spectrometer [4], an astro-particle experiment on the International Space Station, and the Compact Muon Solenoid [5], an LHC experiment, have successfully evaluated PanDA and are pursuing its adoption. In this paper, a description of the new program of work to develop a generic version of PanDA will be given, as well as the progress in extending PanDA's capabilities to support supercomputers and clouds and to leverage intelligent networking. PanDA has demonstrated at a very large scale the value of automated dynamic brokering of diverse workloads across distributed computing resources. The next generation of PanDA will allow other data-intensive sciences and a wider exascale community employing a variety of computing platforms to benefit from ATLAS' experience and proven tools.

Design and performance of the virtualization platform for offline computing on the ATLAS TDAQ Farm

Science.gov (United States)

Ballestrero, S.; Batraneanu, S. M.; Brasolin, F.; Contescu, C.; Di Girolamo, A.; Lee, C. J.; Pozo Astigarraga, M. E.; Scannicchio, D. A.; Twomey, M. S.; Zaytsev, A.

2014-06-01

With the LHC collider at CERN currently going through the period of Long Shutdown 1 there is an opportunity to use the computing resources of the experiments' large trigger farms for other data processing activities. In the case of the ATLAS experiment, the TDAQ farm, consisting of more than 1500 compute nodes, is suitable for running Monte Carlo (MC) production jobs that are mostly CPU and not I/O bound. This contribution gives a thorough review of the design and deployment of a virtualized platform running on this computing resource and of its use to run large groups of CernVM based virtual machines operating as a single CERN-P1 WLCG site. This platform has been designed to guarantee the security and the usability of the ATLAS private network, and to minimize interference with TDAQ's usage of the farm. Openstack has been chosen to provide a cloud management layer. The experience gained in the last 3.5 months shows that the use of the TDAQ farm for the MC simulation contributes to the ATLAS data processing at the level of a large Tier-1 WLCG site, despite the opportunistic nature of the underlying computing resources being used.

ATLAS off-Grid sites (Tier 3) monitoring. From local fabric monitoring to global overview of the VO computing activities

CERN Document Server

PETROSYAN, A; The ATLAS collaboration; BELOV, S; ANDREEVA, J; KADOCHNIKOV, I

2012-01-01

The ATLAS Distributed Computing activities have so far concentrated in the "central" part of the experiment computing system, namely the first 3 tiers (the CERN Tier0, 10 Tier1 centers and over 60 Tier2 sites). Many ATLAS Institutes and National Communities have deployed (or intend to) deploy Tier-3 facilities. Tier-3 centers consist of non-pledged resources, which are usually dedicated to data analysis tasks by the geographically close or local scientific groups, and which usually comprise a range of architectures without Grid middleware. Therefore a substantial part of the ATLAS monitoring tools which make use of Grid middleware, cannot be used for a large fraction of Tier3 sites. The presentation will describe the T3mon project, which aims to develop a software suite for monitoring the Tier3 sites, both from the perspective of the local site administrator and that of the ATLAS VO, thereby enabling the global view of the contribution from Tier3 sites to the ATLAS computing activities. Special attention in p...

Design and Performance of the Virtualization Platform for Offline computing on the ATLAS TDAQ Farm

CERN Document Server

Ballestrero, S; The ATLAS collaboration; Brasolin, F; Contescu, C; Di Girolamo, A; Lee, C J; Pozo Astigarraga, M E; Scannicchio, D A; Twomey, M S; Zaytsev, A

2013-01-01

With the LHC collider at CERN currently going through the period of Long Shutdown 1 (LS1) there is a remarkable opportunity to use the computing resources of the large trigger farms of the experiments for other data processing activities. In the case of ATLAS experiment the TDAQ farm, consisting of more than 1500 compute nodes, is particularly suitable for running Monte Carlo production jobs that are mostly CPU and not I/O bound. This contribution gives a thorough review of all the stages of Sim@P1 project dedicated to the design and deployment of a virtualized platform running on the ATLAS TDAQ computing resources and using it to run the large groups of CernVM based virtual machines operating as a single CERN-P1 WLCG site. This platform has been designed to avoid interference with TDAQ usage of the farm and to guarantee the security and the usability of the ATLAS private network; Openstack has been chosen to provide a cloud management layer. The approaches to organizing support for the sustained operation of...

Design and Performance of the Virtualization Platform for Offline computing on the ATLAS TDAQ Farm

CERN Document Server

Ballestrero, S; The ATLAS collaboration; Brasolin, F; Contescu, C; Di Girolamo, A; Lee, C J; Pozo Astigarraga, M E; Scannicchio, D A; Twomey, M S; Zaytsev, A

2014-01-01

With the LHC collider at CERN currently going through the period of Long Shutdown 1 (LS1) there is a remarkable opportunity to use the computing resources of the large trigger farms of the experiments for other data processing activities. In the case of ATLAS experiment the TDAQ farm, consisting of more than 1500 compute nodes, is particularly suitable for running Monte Carlo production jobs that are mostly CPU and not I/O bound. This contribution gives a thorough review of all the stages of Sim@P1 project dedicated to the design and deployment of a virtualized platform running on the ATLAS TDAQ computing resources and using it to run the large groups of CernVM based virtual machines operating as a single CERN-P1 WLCG site. This platform has been designed to avoid interference with TDAQ usage of the farm and to guarantee the security and the usability of the ATLAS private network; Openstack has been chosen to provide a cloud management layer. The approaches to organizing support for the sustained operation of...

Getting the Most from Distributed Resources With an Analytics Platform for ATLAS Computing Services

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00225336; The ATLAS collaboration; Gardner, Robert; Bryant, Lincoln

2016-01-01

To meet a sharply increasing demand for computing resources for LHC Run 2, ATLAS distributed computing systems reach far and wide to gather CPU resources and storage capacity to execute an evolving ecosystem of production and analysis workflow tools. Indeed more than a hundred computing sites from the Worldwide LHC Computing Grid, plus many “opportunistic” facilities at HPC centers, universities, national laboratories, and public clouds, combine to meet these requirements. These resources have characteristics (such as local queuing availability, proximity to data sources and target destinations, network latency and bandwidth capacity, etc.) affecting the overall processing efficiency and throughput. To quantitatively understand and in some instances predict behavior, we have developed a platform to aggregate, index (for user queries), and analyze the more important information streams affecting performance. These data streams come from the ATLAS production system (PanDA), the distributed data management s...

Monitoring of Computing Resource Use of Active Software Releases in ATLAS

CERN Document Server

Limosani, Antonio; The ATLAS collaboration

2016-01-01

The LHC is the world's most powerful particle accelerator, colliding protons at centre of mass energy of 13 TeV. As the energy and frequency of collisions has grown in the search for new physics, so too has demand for computing resources needed for event reconstruction. We will report on the evolution of resource usage in terms of CPU and RAM in key ATLAS offline reconstruction workflows at the Tier0 at CERN and on the WLCG. Monitoring of workflows is achieved using the ATLAS PerfMon package, which is the standard ATLAS performance monitoring system running inside Athena jobs. Systematic daily monitoring has recently been expanded to include all workflows beginning at Monte Carlo generation through to end user physics analysis, beyond that of event reconstruction. Moreover, the move to a multiprocessor mode in production jobs has facilitated the use of tools, such as "MemoryMonitor", to measure the memory shared across processors in jobs. Resource consumption is broken down into software domains and displayed...

Evolution of the ATLAS PanDA Workload Management System for Exascale Computational Science

OpenAIRE

Maeno, T; De, K; Klimentov, A; Nilsson, P; Oleynik, D; Panitkin, S; Petrosyan, A; Schovancova, J; Vaniachine, A; Wenaus, T; Yu, D

2013-01-01

An important foundation underlying the impressive success of data processing and analysis in the ATLAS experiment [1] at the LHC [2] is the Production and Distributed Analysis (PanDA) workload management system [3]. PanDA was designed specifically for ATLAS and proved to be highly successful in meeting all the distributed computing needs of the experiment. However, the core design of PanDA is not experiment specific. The PanDA workload management system is capable of meeting the needs of othe...

ATLAS cloud R and D

International Nuclear Information System (INIS)

Panitkin, Sergey; Bejar, Jose Caballero; Hover, John; Zaytsev, Alexander; Megino, Fernando Barreiro; Girolamo, Alessandro Di; Kucharczyk, Katarzyna; Llamas, Ramon Medrano; Benjamin, Doug; Gable, Ian; Paterson, Michael; Sobie, Randall; Taylor, Ryan; Hendrix, Val; Love, Peter; Ohman, Henrik; Walker, Rodney

2014-01-01

The computing model of the ATLAS experiment was designed around the concept of grid computing and, since the start of data taking, this model has proven very successful. However, new cloud computing technologies bring attractive features to improve the operations and elasticity of scientific distributed computing. ATLAS sees grid and cloud computing as complementary technologies that will coexist at different levels of resource abstraction, and two years ago created an R and D working group to investigate the different integration scenarios. The ATLAS Cloud Computing R and D has been able to demonstrate the feasibility of offloading work from grid to cloud sites and, as of today, is able to integrate transparently various cloud resources into the PanDA workload management system. The ATLAS Cloud Computing R and D is operating various PanDA queues on private and public resources and has provided several hundred thousand CPU days to the experiment. As a result, the ATLAS Cloud Computing R and D group has gained a significant insight into the cloud computing landscape and has identified points that still need to be addressed in order to fully utilize this technology. This contribution will explain the cloud integration models that are being evaluated and will discuss ATLAS' learning during the collaboration with leading commercial and academic cloud providers.

ATLAS Computing on the Swiss Cloud SWITCHengines

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00215485; The ATLAS collaboration; Sciacca, Gianfranco

2016-01-01

Consolidation towards more computing at flat budgets beyond what pure chip technology can offer, is a requirement for the full scientific exploitation of the future data from the Large Hadron Collider. One consolidation measure is to exploit cloud infrastructures whenever they are financially competitive. We report on the technical solutions and the performance used and achieved running ATLAS production on SWITCHengines. SWITCHengines is the new cloud infrastructure offered to Swiss academia by the National Research and Education Network SWITCH. While solutions and performances are general, financial considerations and policies, which we also report on, are country specific.

The ATLAS distributed analysis system

OpenAIRE

Legger, F.

2014-01-01

In the LHC operations era, analysis of the multi-petabyte ATLAS data sample by globally distributed physicists is a challenging task. To attain the required scale the ATLAS Computing Model was designed around the concept of grid computing, realized in the Worldwide LHC Computing Grid (WLCG), the largest distributed computational resource existing in the sciences. The ATLAS experiment currently stores over 140 PB of data and runs about 140,000 concurrent jobs continuously at WLCG sites. During...

The Evolving role of Tier2s in ATLAS with the new Computing and Data Distribution Model

CERN Document Server

Gonzalez de la Hoz, S; The ATLAS collaboration

2012-01-01

Originally the ATLAS computing model assumed that the Tier2s of each of the 10 clouds should keep on disk collectively at least one copy of all "active" AOD and DPD datasets. Evolution of ATLAS computing and data models requires changes in ATLAS Tier2s policy for the data replication, dynamic data caching and remote data access. Tier2 operations take place completely asynchronously with respect to data taking. Tier2s do simulation and user analysis. Large-scale reprocessing jobs on real data are at first taking place mostly at Tier1s but will progressively move to Tier2s as well. The availability of disk space at Tier2s is extremely important in the ATLAS computing model as it allows more data to be readily accessible for analysis jobs to all users, independently of their geographical location. The Tier2s disk space has been reserved for real, simulated, calibration and alignment, group, and user data. A buffer disk space is needed for input and output data for simulations jobs. Tier2s are going to be used mo...

The evolving role of Tier2s in ATLAS with the new Computing and Data Distribution model

CERN Document Server

Gonzalez de la Hoz, S

2012-01-01

Originally the ATLAS computing model assumed that the Tier2s of each of the 10 clouds should keep on disk collectively at least one copy of all "active" AOD and DPD datasets. Evolution of ATLAS computing and data models requires changes in ATLAS Tier2s policy for the data replication, dynamic data caching and remote data access. Tier2 operations take place completely asynchronously with respect to data taking. Tier2s do simulation and user analysis. Large-scale reprocessing jobs on real data are at first taking place mostly at Tier1s but will progressively move to Tier2s as well. The availability of disk space at Tier2s is extremely important in the ATLAS computing model as it allows more data to be readily accessible for analysis jobs to all users, independently of their geographical location. The Tier2s disk space has been reserved for real, simulated, calibration and alignment, group, and user data. A buffer disk space is needed for input and output data for simulations jobs. Tier2s are going to be used mo...

Brain transcriptome atlases : A computational perspective

NARCIS (Netherlands)

Mahfouz, A.M.E.T.A.; Huisman, S.M.H.; Lelieveldt, B.P.F.; Reinders, M.J.T.

2017-01-01

The immense complexity of the mammalian brain is largely reflected in the underlying molecular signatures of its billions of cells. Brain transcriptome atlases provide valuable insights into gene expression patterns across different brain areas throughout the course of development. Such atlases

Validation and computing and performance studies for the ATLAS simulation

CERN Document Server

Marshall, Z; The ATLAS collaboration

2009-01-01

We present the validation of the ATLAS simulation software pro ject. Software development is controlled by nightly builds and several levels of automatic tests to ensure stability. Computing validation, including CPU time, memory, and disk space required per event, is benchmarked for all software releases. Several different physics processes and event types are checked to thoroughly test all aspects of the detector simulation. The robustness of the simulation software is demonstrated by the production of 500 million events on the World-wide LHC Computing Grid in the last year.

Design and performance of the virtualization platform for offline computing on the ATLAS TDAQ Farm

International Nuclear Information System (INIS)

Ballestrero, S; Lee, C J; Batraneanu, S M; Scannicchio, D A; Brasolin, F; Contescu, C; Girolamo, A Di; Astigarraga, M E Pozo; Twomey, M S; Zaytsev, A

2014-01-01

With the LHC collider at CERN currently going through the period of Long Shutdown 1 there is an opportunity to use the computing resources of the experiments' large trigger farms for other data processing activities. In the case of the ATLAS experiment, the TDAQ farm, consisting of more than 1500 compute nodes, is suitable for running Monte Carlo (MC) production jobs that are mostly CPU and not I/O bound. This contribution gives a thorough review of the design and deployment of a virtualized platform running on this computing resource and of its use to run large groups of CernVM based virtual machines operating as a single CERN-P1 WLCG site. This platform has been designed to guarantee the security and the usability of the ATLAS private network, and to minimize interference with TDAQ's usage of the farm. Openstack has been chosen to provide a cloud management layer. The experience gained in the last 3.5 months shows that the use of the TDAQ farm for the MC simulation contributes to the ATLAS data processing at the level of a large Tier-1 WLCG site, despite the opportunistic nature of the underlying computing resources being used.

Atlas-based delineation of lymph node levels in head and neck computed tomography images

International Nuclear Information System (INIS)

Commowick, Olivier; Gregoire, Vincent; Malandain, Gregoire

2008-01-01

Purpose: Radiotherapy planning requires accurate delineations of the tumor and of the critical structures. Atlas-based segmentation has been shown to be very efficient to automatically delineate brain critical structures. We therefore propose to construct an anatomical atlas of the head and neck region. Methods and materials: Due to the high anatomical variability of this region, an atlas built from a single image as for the brain is not adequate. We address this issue by building a symmetric atlas from a database of manually segmented images. First, we develop an atlas construction method and apply it to a database of 45 Computed Tomography (CT) images from patients with node-negative pharyngo-laryngeal squamous cell carcinoma manually delineated for radiotherapy. Then, we qualitatively and quantitatively evaluate the results generated by the built atlas based on Leave-One-Out framework on the database. Results: We present qualitative and quantitative results using this atlas construction method. The evaluation was performed on a subset of 12 patients among the original CT database of 45 patients. Qualitative results depict visually well delineated structures. The quantitative results are also good, with an error with respect to the best achievable results ranging from 0.196 to 0.404 with a mean of 0.253. Conclusions: These results show the feasibility of using such an atlas for radiotherapy planning. Many perspectives are raised from this work ranging from extensive validation to the construction of several atlases representing sub-populations, to account for large inter-patient variabilities, and populations with node-positive tumors

Multi-atlas pancreas segmentation: Atlas selection based on vessel structure.

Science.gov (United States)

Karasawa, Ken'ichi; Oda, Masahiro; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Chu, Chengwen; Zheng, Guoyan; Rueckert, Daniel; Mori, Kensaku

2017-07-01

Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%. Copyright © 2017 Elsevier B.V. All rights reserved.

Monitoring of computing resource use of active software releases at ATLAS

Science.gov (United States)

Limosani, Antonio; ATLAS Collaboration

2017-10-01

The LHC is the world’s most powerful particle accelerator, colliding protons at centre of mass energy of 13 TeV. As the energy and frequency of collisions has grown in the search for new physics, so too has demand for computing resources needed for event reconstruction. We will report on the evolution of resource usage in terms of CPU and RAM in key ATLAS offline reconstruction workflows at the TierO at CERN and on the WLCG. Monitoring of workflows is achieved using the ATLAS PerfMon package, which is the standard ATLAS performance monitoring system running inside Athena jobs. Systematic daily monitoring has recently been expanded to include all workflows beginning at Monte Carlo generation through to end-user physics analysis, beyond that of event reconstruction. Moreover, the move to a multiprocessor mode in production jobs has facilitated the use of tools, such as “MemoryMonitor”, to measure the memory shared across processors in jobs. Resource consumption is broken down into software domains and displayed in plots generated using Python visualization libraries and collected into pre-formatted auto-generated Web pages, which allow the ATLAS developer community to track the performance of their algorithms. This information is however preferentially filtered to domain leaders and developers through the use of JIRA and via reports given at ATLAS software meetings. Finally, we take a glimpse of the future by reporting on the expected CPU and RAM usage in benchmark workflows associated with the High Luminosity LHC and anticipate the ways performance monitoring will evolve to understand and benchmark future workflows.

Monitoring of computing resource use of active software releases at ATLAS

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00219183; The ATLAS collaboration

2017-01-01

The LHC is the world’s most powerful particle accelerator, colliding protons at centre of mass energy of 13 TeV. As the energy and frequency of collisions has grown in the search for new physics, so too has demand for computing resources needed for event reconstruction. We will report on the evolution of resource usage in terms of CPU and RAM in key ATLAS offline reconstruction workflows at the TierO at CERN and on the WLCG. Monitoring of workflows is achieved using the ATLAS PerfMon package, which is the standard ATLAS performance monitoring system running inside Athena jobs. Systematic daily monitoring has recently been expanded to include all workflows beginning at Monte Carlo generation through to end-user physics analysis, beyond that of event reconstruction. Moreover, the move to a multiprocessor mode in production jobs has facilitated the use of tools, such as “MemoryMonitor”, to measure the memory shared across processors in jobs. Resource consumption is broken down into software domains and dis...

ATLAS & Google - The Data Ocean Project

CERN Document Server

Lassnig, Mario; The ATLAS collaboration

2018-01-01

With the LHC High Luminosity upgrade the workload and data management systems are facing new major challenges. To address those challenges ATLAS and Google agreed to cooperate on a project to connect Google Cloud Storage and Compute Engine to the ATLAS computing environment. The idea is to allow ATLAS to explore the use of different computing models, to allow ATLAS user analysis to benefit from the Google infrastructure, and to give Google real science use cases to improve their cloud platform. Making the output of a distributed analysis from the grid quickly available to the analyst is a difficult problem. Redirecting the analysis output to Google Cloud Storage can provide an alternative, faster solution for the analyst. First, Google's Cloud Storage will be connected to the ATLAS Data Management System Rucio. The second part aims to let jobs run on Google Compute Engine, accessing data from either ATLAS storage or Google Cloud Storage. The third part involves Google implementing a global redirection between...

Evolution of the ATLAS data and computing model for a Tier2 in the EGI infrastructure

CERN Document Server

Fernández Casaní, A; The ATLAS collaboration; González de la Hoz, S; Salt Cairols, J; Fassi, F; Kaci, M; Lamas, A; Oliver, E; Sánchez, J; Sánchez, V

2012-01-01

Since the start of the LHC pp collisions in 2010, the ATLAS computing model has moved from a more strict design, where every Tier2 had a liaison and a network dependence from a Tier1, to a more meshed approach where every cloud could be connected. Evolution of ATLAS data models requires changes in ATLAS Tier2s policy for the data replication, dynamic data caching and remote data access. It also requires rethinking the network infrastructure to enable any Tier2 and associated Tier3 to easily connect to any Tier1 or Tier2. Tier2s are becoming more and more important in the ATLAS computing model as it allows more data to be readily accessible for analysis jobs to all users, independently of their geographical location. The Tier2s disk space has been reserved for real, simulated, calibration and alignment, group, and user data. A buffer disk space is needed for input and output data for simulations jobs. Tier2s are going to be used more efficiently. In this way Tier1s and Tier2s are becoming more equivalent for t...

Reconstruction and identification of electrons in the Atlas experiment. Setup of a Tier 2 of the computing grid

International Nuclear Information System (INIS)

Derue, F.

2008-03-01

The origin of the mass of elementary particles is linked to the electroweak symmetry breaking mechanism. Its study will be one of the main efforts of the Atlas experiment at the Large Hadron Collider of CERN, starting in 2008. In most cases, studies will be limited by our knowledge of the detector performances, as the precision of the energy reconstruction or the efficiency to identify particles. This manuscript presents a work dedicated to the reconstruction of electrons in the Atlas experiment with simulated data and data taken during the combined test beam of 2004. The analysis of the Atlas data implies the use of a huge amount of computing and storage resources which brought to the development of a world computing grid. (author)

AGIS: The ATLAS Grid Information System

OpenAIRE

Anisenkov, Alexey; Belov, Sergey; Di Girolamo, Alessandro; Gayazov, Stavro; Klimentov, Alexei; Oleynik, Danila; Senchenko, Alexander

2012-01-01

ATLAS is a particle physics experiment at the Large Hadron Collider at CERN. The experiment produces petabytes of data annually through simulation production and tens petabytes of data per year from the detector itself. The ATLAS Computing model embraces the Grid paradigm and a high degree of decentralization and computing resources able to meet ATLAS requirements of petabytes scale data operations. In this paper we present ATLAS Grid Information System (AGIS) designed to integrate configurat...

ATLAS computing on CSCS HPC

Science.gov (United States)

Filipcic, A.; Haug, S.; Hostettler, M.; Walker, R.; Weber, M.

2015-12-01

The Piz Daint Cray XC30 HPC system at CSCS, the Swiss National Supercomputing centre, was the highest ranked European system on TOP500 in 2014, also featuring GPU accelerators. Event generation and detector simulation for the ATLAS experiment have been enabled for this machine. We report on the technical solutions, performance, HPC policy challenges and possible future opportunities for HEP on extreme HPC systems. In particular a custom made integration to the ATLAS job submission system has been developed via the Advanced Resource Connector (ARC) middleware. Furthermore, a partial GPU acceleration of the Geant4 detector simulations has been implemented.

ATLAS distributed computing operation shift teams experience during the discovery year and beginning of the long shutdown 1

International Nuclear Information System (INIS)

Sedov, Alexey; Girolamo, Alessandro Di; Negri, Guidone; Sakamoto, Hiroshi; Schovancová, Jaroslava; Smirnov, Iouri; Vartapetian, Armen; Yu, Jaehoon

2014-01-01

ATLAS Distributed Computing Operation Shifts evolve to meet new requirements. New monitoring tools as well as operational changes lead to modifications in organization of shifts. In this paper we describe the structure of shifts, the roles of different shifts in ATLAS computing grid operation, the influence of a Higgs-like particle discovery on shift operation, the achievements in monitoring and automation that allowed extra focus on the experiment priority tasks, and the influence of the Long Shutdown 1 and operational changes related to the no beam period.

The ATLAS Analysis Model

CERN Multimedia

Amir Farbin

The ATLAS Analysis Model is a continually developing vision of how to reconcile physics analysis requirements with the ATLAS offline software and computing model constraints. In the past year this vision has influenced the evolution of the ATLAS Event Data Model, the Athena software framework, and physics analysis tools. These developments, along with the October Analysis Model Workshop and the planning for CSC analyses have led to a rapid refinement of the ATLAS Analysis Model in the past few months. This article introduces some of the relevant issues and presents the current vision of the future ATLAS Analysis Model. Event Data Model The ATLAS Event Data Model (EDM) consists of several levels of details, each targeted for a specific set of tasks. For example the Event Summary Data (ESD) stores calorimeter cells and tracking system hits thereby permitting many calibration and alignment tasks, but will be only accessible at particular computing sites with potentially large latency. In contrast, the Analysis...

ATLAS computing on CSCS HPC

CERN Document Server

Hostettler, Michael Artur; The ATLAS collaboration; Haug, Sigve; Walker, Rodney; Weber, Michele

2015-01-01

The Piz Daint Cray XC30 HPC system at CSCS, the Swiss National Supercomputing centre, was in 2014 the highest ranked European system on TOP500, also featuring GPU accelerators. Event generation and detector simulation for the ATLAS experiment have been enabled for this machine. We report on the technical solutions, performance, HPC policy challenges and possible future opportunities for HEP on extreme HPC systems. In particular a custom made integration to the ATLAS job submission system has been developed via the Advanced Resource Connector (ARC) middleware. Furthermore, some GPU acceleration of the Geant4 detector simulations has been implemented to justify the allocation request for this machine.

ATLAS computing on CSCS HPC

CERN Document Server

Filipcic, Andrej; The ATLAS collaboration; Weber, Michele; Walker, Rodney; Hostettler, Michael Artur

2015-01-01

The Piz Daint Cray XC30 HPC system at CSCS, the Swiss National Supercomputing centre, is in 2014 the highest ranked European system on TOP500, also featuring GPU accelerators. Event generation and detector simulation for the ATLAS experiment has been enabled for this machine. We report on the technical solutions, performance, HPC policy challenges and possible future opportunities for HEP on extreme HPC systems. In particular a custom made integration to the ATLAS job submission system has been developed via the Advanced Resource Connector (ARC) middleware. Further, some GPU acceleration of the Geant4 detector simulations were implemented to justify the allocation request for this machine.

Advances in service and operations for ATLAS data management

International Nuclear Information System (INIS)

Stewart, Graeme A; Garonne, Vincent; Lassnig, Mario; Molfetas, Angelos; Barisits, Martin; Calvet, Ivan; Beermann, Thomas; Megino, Fernando Barreiro; Campana, Simone; Zhang, Donal; Tykhonov, Andrii; Serfon, Cedric; Oleynik, Danila; Petrosyan, Artem

2012-01-01

ATLAS has recorded almost 5PB of RAW data since the LHC started running at the end of 2009. Many more derived data products and complimentary simulation data have also been produced by the collaboration and, in total, 70PB is currently stored in the Worldwide LHC Computing Grid by ATLAS. All of this data is managed by the ATLAS Distributed Data Management system, called Don Quixote 2 (DQ2). DQ2 has evolved rapidly to help ATLAS Computing operations manage these large quantities of data across the many grid sites at which ATLAS runs and to help ATLAS physicists get access to this data. In this paper we describe new and improved DQ2 services: popularity; space monitoring and accounting; exclusion service; cleaning agents; deletion agents. We describe the experience of data management operation in ATLAS computing, showing how these services enable management of petabyte scale computing operations. We illustrate the coupling of data management services to other parts of the ATLAS computing infrastructure, in particular showing how feedback from the distributed analysis system in ATLAS has enabled dynamic placement of the most popular data, helping users and groups to analyse the increasing data volumes on the grid.

Advances in service and operations for ATLAS data management

Science.gov (United States)

Stewart, Graeme A.; Garonne, Vincent; Lassnig, Mario; Molfetas, Angelos; Barisits, Martin; Zhang, Donal; Calvet, Ivan; Beermann, Thomas; Barreiro Megino, Fernando; Tykhonov, Andrii; Campana, Simone; Serfon, Cedric; Oleynik, Danila; Petrosyan, Artem; ATLAS Collaboration

2012-06-01

ATLAS has recorded almost 5PB of RAW data since the LHC started running at the end of 2009. Many more derived data products and complimentary simulation data have also been produced by the collaboration and, in total, 70PB is currently stored in the Worldwide LHC Computing Grid by ATLAS. All of this data is managed by the ATLAS Distributed Data Management system, called Don Quixote 2 (DQ2). DQ2 has evolved rapidly to help ATLAS Computing operations manage these large quantities of data across the many grid sites at which ATLAS runs and to help ATLAS physicists get access to this data. In this paper we describe new and improved DQ2 services: popularity; space monitoring and accounting; exclusion service; cleaning agents; deletion agents. We describe the experience of data management operation in ATLAS computing, showing how these services enable management of petabyte scale computing operations. We illustrate the coupling of data management services to other parts of the ATLAS computing infrastructure, in particular showing how feedback from the distributed analysis system in ATLAS has enabled dynamic placement of the most popular data, helping users and groups to analyse the increasing data volumes on the grid.

ATLAS & Google — "Data Ocean" R&D Project

CERN Document Server

The ATLAS collaboration

2017-01-01

ATLAS is facing several challenges with respect to their computing requirements for LHC Run-3 (2020-2023) and HL-LHC runs (2025-2034). The challenges are not specific for ATLAS or/and LHC, but common for HENP computing community. Most importantly, storage continues to be the driving cost factor and at the current growth rate cannot absorb the increased physics output of the experiment. Novel computing models with a more dynamic use of storage and computing resources need to be considered. This project aims to start an R&D project for evaluating and adopting novel IT technologies for HENP computing. ATLAS and Google plan to launch an R&D project to integrate Google cloud resources (Storage and Compute) to the ATLAS distributed computing environment. After a series of teleconferences, a face-to-face brainstorming meeting in Denver, CO at the Supercomputing 2017 conference resulted in this proposal for a first prototype of the "Data Ocean" project. The idea is threefold: (a) to allow ATLAS to explore the...

The December 2006 ATLAS Computing & Software Workshop

CERN Multimedia

Fred Luehring

The 29th ATLAS Computing & Software Workshop was held on December 11-15 at CERN. With the rapidly approaching onset of data taking, the workshop participants had an air of urgency about them. There was considerable discussion on hot topics such as physics validation of the software, data analysis, actual software production on the GRID, and the schedule of work for 2007 including the Final Dress Rehearsal (FDR). However don't be fooled, the workshop was not all work - there were also two social events which were greatly enjoyed by the attendees. The workshop welcomed Wouter Verkerke as the new Physics Validation Coordinator (replacing Davide Costanzo). Most recent validation work has centered on the 12.0.X release series that will be used for the Computing System Commissioning (CSC) exercise. The validation is now a big job because it needs to be done over a variety of conditions (magnetic field on/off, aligned/misaligned geometry) for every candidate release. Luckily there have been a large number of pe...

The ATLAS Distributed Data Management project: Past and Future

CERN Document Server

Garonne, V; The ATLAS collaboration; Lassnig, M; Molfetas, A; Barisits, M; Beermann, T; Nairz, A; Goossens, L; Barreiro Megino, F; Serfon, C; Oleynik, D; Petrosyan, A

2012-01-01

ATLAS has recorded almost 8PB of RAW data since the LHC started running at the end of 2009. Many more derived data products and complimentary simulation data have also been produced by the collaboration and, in total, 90PB is currently stored in the Worldwide LHC Computing Grid by ATLAS. All this data is managed by the ATLAS Distributed Data Management system, called Don Quijote 2 (DQ2). DQ2 has evolved rapidly to help ATLAS Computing operations manage these large quantities of data across the many grid sites at which ATLAS runs, and to help ATLAS physicists get access to this data. In this paper, we describe new and improved DQ2 services, and the experience of data management operation in ATLAS computing, showing how these services enable the management of petabyte scale computing operations. We also present the concepts of the new version of the ATLAS Distributed Data Management (DDM) system, Rucio.

The ATLAS Distributed Data Management project: Past and Future

International Nuclear Information System (INIS)

Garonne, Vincent; Stewart, Graeme A; Lassnig, Mario; Molfetas, Angelos; Barisits, Martin; Beermann, Thomas; Nairz, Armin; Goossens, Luc; Barreiro Megino, Fernando; Serfon, Cedric; Oleynik, Danila; Petrosyan, Artem

2012-01-01

ATLAS has recorded more than 8 petabyte(PB) of RAW data since the LHC started running at the end of 2009. Many more derived data products and complimentary simulation data have also been produced by the collaboration and, in total, 90PB are currently stored in the Worldwide LHC Computing Grid by ATLAS. All these data are managed by the ATLAS Distributed Data Management system, called Don Quijote 2 (DQ2). DQ2 has evolved rapidly to help ATLAS Computing operations manage these large quantities of data across the many grid sites at which ATLAS runs, and to help ATLAS physicists get access to these data. In this paper, we describe new and improved DQ2 services, and the experience of data management operation in ATLAS computing, showing how these services enable the management of PB scale computing operations. We also present the concepts of the new version of the ATLAS Distributed Data Management (DDM) system, Rucio.

The ATLAS Distributed Data Management project: Past and Future

Science.gov (United States)

Garonne, Vincent; Stewart, Graeme A.; Lassnig, Mario; Molfetas, Angelos; Barisits, Martin; Beermann, Thomas; Nairz, Armin; Goossens, Luc; Barreiro Megino, Fernando; Serfon, Cedric; Oleynik, Danila; Petrosyan, Artem

2012-12-01

ATLAS has recorded more than 8 petabyte(PB) of RAW data since the LHC started running at the end of 2009. Many more derived data products and complimentary simulation data have also been produced by the collaboration and, in total, 90PB are currently stored in the Worldwide LHC Computing Grid by ATLAS. All these data are managed by the ATLAS Distributed Data Management system, called Don Quijote 2 (DQ2). DQ2 has evolved rapidly to help ATLAS Computing operations manage these large quantities of data across the many grid sites at which ATLAS runs, and to help ATLAS physicists get access to these data. In this paper, we describe new and improved DQ2 services, and the experience of data management operation in ATLAS computing, showing how these services enable the management of PB scale computing operations. We also present the concepts of the new version of the ATLAS Distributed Data Management (DDM) system, Rucio.

AGIS: The ATLAS Grid Information System

CERN Document Server

Anisenkov, A; The ATLAS collaboration; Klimentov, A; Oleynik, D; Petrosyan, A

2014-01-01

In this paper we describe ATLAS Grid Information System (AGIS), the system designed to integrate configuration and status information about resources, services and topology of the computing infrastructure used by ATLAS Distributed Computing applications and services.

AGIS: The ATLAS Grid Information System

OpenAIRE

Anisenkov, A; Di Girolamo, A; Klimentov, A; Oleynik, D; Petrosyan, A

2013-01-01

In this paper we describe ATLAS Grid Information System (AGIS), the system designed to integrate configuration and status information about resources, services and topology of the computing infrastructure used by ATLAS Distributed Computing applications and services.

The ATLAS Distributed Analysis System

CERN Document Server

Legger, F; The ATLAS collaboration; Pacheco Pages, A; Stradling, A

2013-01-01

In the LHC operations era, analysis of the multi-petabyte ATLAS data sample by globally distributed physicists is a challenging task. To attain the required scale the ATLAS Computing Model was designed around the concept of grid computing, realized in the Worldwide LHC Computing Grid (WLCG), the largest distributed computational resource existing in the sciences. The ATLAS experiment currently stores over 140 PB of data and runs about 140,000 concurrent jobs continuously at WLCG sites. During the first run of the LHC, the ATLAS Distributed Analysis (DA) service has operated stably and scaled as planned. More than 1600 users submitted jobs in 2012, with 2 million or more analysis jobs per week, peaking at about a million jobs per day. The system dynamically distributes popular data to expedite processing and maximally utilize resources. The reliability of the DA service is high but steadily improving; grid sites are continually validated against a set of standard tests, and a dedicated team of expert shifters ...

The ATLAS Distributed Analysis System

CERN Document Server

Legger, F; The ATLAS collaboration

2014-01-01

In the LHC operations era, analysis of the multi-petabyte ATLAS data sample by globally distributed physicists is a challenging task. To attain the required scale the ATLAS Computing Model was designed around the concept of grid computing, realized in the Worldwide LHC Computing Grid (WLCG), the largest distributed computational resource existing in the sciences. The ATLAS experiment currently stores over 140 PB of data and runs about 140,000 concurrent jobs continuously at WLCG sites. During the first run of the LHC, the ATLAS Distributed Analysis (DA) service has operated stably and scaled as planned. More than 1600 users submitted jobs in 2012, with 2 million or more analysis jobs per week, peaking at about a million jobs per day. The system dynamically distributes popular data to expedite processing and maximally utilize resources. The reliability of the DA service is high but steadily improving; grid sites are continually validated against a set of standard tests, and a dedicated team of expert shifters ...

ATLAS@Home looks for CERN volunteers

CERN Multimedia

Rosaria Marraffino

2014-01-01

ATLAS@Home is a CERN volunteer computing project that runs simulated ATLAS events. As the project ramps up, the project team is looking for CERN volunteers to test the system before planning a bigger promotion for the public.   The ATLAS@home outreach website. ATLAS@Home is a large-scale research project that runs ATLAS experiment simulation software inside virtual machines hosted by volunteer computers. “People from all over the world offer up their computers’ idle time to run simulation programmes to help physicists extract information from the large amount of data collected by the detector,” explains Claire Adam Bourdarios of the ATLAS@Home project. “The ATLAS@Home project aims to extrapolate the Standard Model at a higher energy and explore what new physics may look like. Everything we’re currently running is preparation for next year's run.” ATLAS@Home became an official BOINC (Berkeley Open Infrastructure for Network ...

PanDA for ATLAS distributed computing in the next decade

Science.gov (United States)

Barreiro Megino, F. H.; De, K.; Klimentov, A.; Maeno, T.; Nilsson, P.; Oleynik, D.; Padolski, S.; Panitkin, S.; Wenaus, T.; ATLAS Collaboration

2017-10-01

The Production and Distributed Analysis (PanDA) system has been developed to meet ATLAS production and analysis requirements for a data-driven workload management system capable of operating at the Large Hadron Collider (LHC) data processing scale. Heterogeneous resources used by the ATLAS experiment are distributed worldwide at hundreds of sites, thousands of physicists analyse the data remotely, the volume of processed data is beyond the exabyte scale, dozens of scientific applications are supported, while data processing requires more than a few billion hours of computing usage per year. PanDA performed very well over the last decade including the LHC Run 1 data taking period. However, it was decided to upgrade the whole system concurrently with the LHC’s first long shutdown in order to cope with rapidly changing computing infrastructure. After two years of reengineering efforts, PanDA has embedded capabilities for fully dynamic and flexible workload management. The static batch job paradigm was discarded in favor of a more automated and scalable model. Workloads are dynamically tailored for optimal usage of resources, with the brokerage taking network traffic and forecasts into account. Computing resources are partitioned based on dynamic knowledge of their status and characteristics. The pilot has been re-factored around a plugin structure for easier development and deployment. Bookkeeping is handled with both coarse and fine granularities for efficient utilization of pledged or opportunistic resources. An in-house security mechanism authenticates the pilot and data management services in off-grid environments such as volunteer computing and private local clusters. The PanDA monitor has been extensively optimized for performance and extended with analytics to provide aggregated summaries of the system as well as drill-down to operational details. There are as well many other challenges planned or recently implemented, and adoption by non-LHC experiments such

PanDA: A New Paradigm for Distributed Computing in HEP Through the Lens of ATLAS and other Experiments

CERN Document Server

De, K; The ATLAS collaboration; Maeno, T; Nilsson, P; Wenaus, T

2014-01-01

Experiments at the Large Hadron Collider (LHC) face unprecedented computing challenges. Heterogeneous resources are distributed worldwide, thousands of physicists analyzing the data need remote access to hundreds of computing sites, the volume of processed data is beyond the exabyte scale, and data processing requires more than a billion hours of computing usage per year. The PanDA (Production and Distributed Analysis) system was developed to meet the scale and complexity of LHC distributed computing for the ATLAS experiment. In the process, the old batch job paradigm of computing in HEP was discarded in favor of a far more flexible and scalable model. The success of PanDA in ATLAS is leading to widespread adoption and testing by other experiments. PanDA is the first exascale workload management system in HEP, already operating at a million computing jobs per day, and processing over an exabyte of data in 2013. We will describe the design and implementation of PanDA, present data on the performance of PanDA a...

The ATLAS distributed analysis system

International Nuclear Information System (INIS)

Legger, F

2014-01-01

In the LHC operations era, analysis of the multi-petabyte ATLAS data sample by globally distributed physicists is a challenging task. To attain the required scale the ATLAS Computing Model was designed around the concept of Grid computing, realized in the Worldwide LHC Computing Grid (WLCG), the largest distributed computational resource existing in the sciences. The ATLAS experiment currently stores over 140 PB of data and runs about 140,000 concurrent jobs continuously at WLCG sites. During the first run of the LHC, the ATLAS Distributed Analysis (DA) service has operated stably and scaled as planned. More than 1600 users submitted jobs in 2012, with 2 million or more analysis jobs per week, peaking at about a million jobs per day. The system dynamically distributes popular data to expedite processing and maximally utilize resources. The reliability of the DA service is high and steadily improving; Grid sites are continually validated against a set of standard tests, and a dedicated team of expert shifters provides user support and communicates user problems to the sites. Both the user support techniques and the direct feedback of users have been effective in improving the success rate and user experience when utilizing the distributed computing environment. In this contribution a description of the main components, activities and achievements of ATLAS distributed analysis is given. Several future improvements being undertaken will be described.

The ATLAS distributed analysis system

Science.gov (United States)

Legger, F.; Atlas Collaboration

2014-06-01

In the LHC operations era, analysis of the multi-petabyte ATLAS data sample by globally distributed physicists is a challenging task. To attain the required scale the ATLAS Computing Model was designed around the concept of Grid computing, realized in the Worldwide LHC Computing Grid (WLCG), the largest distributed computational resource existing in the sciences. The ATLAS experiment currently stores over 140 PB of data and runs about 140,000 concurrent jobs continuously at WLCG sites. During the first run of the LHC, the ATLAS Distributed Analysis (DA) service has operated stably and scaled as planned. More than 1600 users submitted jobs in 2012, with 2 million or more analysis jobs per week, peaking at about a million jobs per day. The system dynamically distributes popular data to expedite processing and maximally utilize resources. The reliability of the DA service is high and steadily improving; Grid sites are continually validated against a set of standard tests, and a dedicated team of expert shifters provides user support and communicates user problems to the sites. Both the user support techniques and the direct feedback of users have been effective in improving the success rate and user experience when utilizing the distributed computing environment. In this contribution a description of the main components, activities and achievements of ATLAS distributed analysis is given. Several future improvements being undertaken will be described.

ATLAS-Canada Network

Energy Technology Data Exchange (ETDEWEB)

Gable, I; Sobie, R J [HEPnet/Canada, Victoria, BC (Canada); Bedinelli, M; Butterworth, S; Groer, L; Kupchinsky, V [University of Toronto, Toronto, ON (Canada); Caron, B; McDonald, S; Payne, C [TRIUMF Laboratory, Vancouver, BC (Canada); Chambers, R [University of Alberta, Edmonton, AB (Canada); Fitzgerald, B [University of Victoria, Victoria, BC (Canada); Hatem, R; Marshall, P; Pobric, D [CANARIE Inc., Ottawa, ON (Canada); Maddalena, P; Mercure, P; Robertson, S; Rochefort, M [McGill University, Montreal, QC (Canada); McWilliam, D [BCNet, Vancouver, BC (Canada); Siegert, M [Simon Fraser University, Burnaby, BC (Canada)], E-mail: igable@uvic.ca (and others)

2008-12-15

The ATLAS-Canada computing model consists of a WLCG Tier-1 computing centre located at the TRIUMF Laboratory in Vancouver, Canada, and two distributed Tier-2 computing centres in eastern and western Canadian universities. The TRIUMF Tier-1 is connected to the CERN Tier-0 via a 10G dedicated circuit provided by CANARIE. The Canadian institutions hosting Tier-2 facilities are connected to TRIUMF via 1G lightpaths, and routing between Tier-2s occurs through TRIUMF. This paper discusses the architecture of the ATLAS-Canada network, the challenges of building the network, and the future plans.

ATLAS OpenData and OpenKey: using low tech computational tools for students training in High Energy Physics

CERN Document Server

Sanchez Pineda, Arturos; The ATLAS collaboration

2018-01-01

One of the big challenges in High Energy Physics development is the fact that many potential -and very valuable- students and young researchers live in countries where internet access and computational infrastructure are poor compared to institutions already participating. In order to accelerate the process, the ATLAS Open Data project releases useful and meaningful data and tools using standard and easy-to-deploy computational means, such as custom and light Linux Virtual Machines, open source technologies, web and desktop applications. The ATLAS Open Key, a simple USB pen, allows transporting all those resources around the globe. As simple as it sounds, this approach is helping to train students that are now PhD candidates and to integrate HEP educational programs at Master level in universities where did not exist before. The software tools and resources used will be presented, as well as results and stories, ideas and next steps of the ATLAS Open Data project.

SU-E-J-128: Two-Stage Atlas Selection in Multi-Atlas-Based Image Segmentation

International Nuclear Information System (INIS)

Zhao, T; Ruan, D

2015-01-01

Purpose: In the new era of big data, multi-atlas-based image segmentation is challenged by heterogeneous atlas quality and high computation burden from extensive atlas collection, demanding efficient identification of the most relevant atlases. This study aims to develop a two-stage atlas selection scheme to achieve computational economy with performance guarantee. Methods: We develop a low-cost fusion set selection scheme by introducing a preliminary selection to trim full atlas collection into an augmented subset, alleviating the need for extensive full-fledged registrations. More specifically, fusion set selection is performed in two successive steps: preliminary selection and refinement. An augmented subset is first roughly selected from the whole atlas collection with a simple registration scheme and the corresponding preliminary relevance metric; the augmented subset is further refined into the desired fusion set size, using full-fledged registration and the associated relevance metric. The main novelty of this work is the introduction of an inference model to relate the preliminary and refined relevance metrics, based on which the augmented subset size is rigorously derived to ensure the desired atlases survive the preliminary selection with high probability. Results: The performance and complexity of the proposed two-stage atlas selection method were assessed using a collection of 30 prostate MR images. It achieved comparable segmentation accuracy as the conventional one-stage method with full-fledged registration, but significantly reduced computation time to 1/3 (from 30.82 to 11.04 min per segmentation). Compared with alternative one-stage cost-saving approach, the proposed scheme yielded superior performance with mean and medium DSC of (0.83, 0.85) compared to (0.74, 0.78). Conclusion: This work has developed a model-guided two-stage atlas selection scheme to achieve significant cost reduction while guaranteeing high segmentation accuracy. The benefit

ATLAS Software Installation on Supercomputers

CERN Document Server

Undrus, Alexander; The ATLAS collaboration

2018-01-01

PowerPC and high performance computers (HPC) are important resources for computing in the ATLAS experiment. The future LHC data processing will require more resources than Grid computing, currently using approximately 100,000 cores at well over 100 sites, can provide. Supercomputers are extremely powerful as they use resources of hundreds of thousands CPUs joined together. However their architectures have different instruction sets. ATLAS binary software distributions for x86 chipsets do not fit these architectures, as emulation of these chipsets results in huge performance loss. This presentation describes the methodology of ATLAS software installation from source code on supercomputers. The installation procedure includes downloading the ATLAS code base as well as the source of about 50 external packages, such as ROOT and Geant4, followed by compilation, and rigorous unit and integration testing. The presentation reports the application of this procedure at Titan HPC and Summit PowerPC at Oak Ridge Computin...

Computation of a high-resolution MRI 3D stereotaxic atlas of the sheep brain.

Science.gov (United States)

Ella, Arsène; Delgadillo, José A; Chemineau, Philippe; Keller, Matthieu

2017-02-15

The sheep model was first used in the fields of animal reproduction and veterinary sciences and then was utilized in fundamental and preclinical studies. For more than a decade, magnetic resonance (MR) studies performed on this model have been increasingly reported, especially in the field of neuroscience. To contribute to MR translational neuroscience research, a brain template and an atlas are necessary. We have recently generated the first complete T1-weighted (T1W) and T2W MR population average images (or templates) of in vivo sheep brains. In this study, we 1) defined a 3D stereotaxic coordinate system for previously established in vivo population average templates; 2) used deformation fields obtained during optimized nonlinear registrations to compute nonlinear tissues or prior probability maps (nlTPMs) of cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) tissues; 3) delineated 25 external and 28 internal sheep brain structures by segmenting both templates and nlTPMs; and 4) annotated and labeled these structures using an existing histological atlas. We built a quality high-resolution 3D atlas of average in vivo sheep brains linked to a reference stereotaxic space. The atlas and nlTPMs, associated with previously computed T1W and T2W in vivo sheep brain templates and nlTPMs, provide a complete set of imaging space that are able to be imported into other imaging software programs and could be used as standardized tools for neuroimaging studies or other neuroscience methods, such as image registration, image segmentation, identification of brain structures, implementation of recording devices, or neuronavigation. J. Comp. Neurol. 525:676-692, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

ATLAS computing operations within the GridKa Cloud

International Nuclear Information System (INIS)

Kennedy, J; Walker, R; Olszewski, A; Nderitu, S; Serfon, C; Duckeck, G

2010-01-01

The organisation and operations model of the ATLAS T1-T2 federation/Cloud associated to the GridKa T1 in Karlsruhe is described. Attention is paid to Cloud level services and the experience gained during the last years of operation. The ATLAS GridKa Cloud is large and divers spanning 5 countries, 2 ROC's and is currently comprised of 13 core sites. A well defined and tested operations model in such a Cloud is of the utmost importance. We have defined the core Cloud services required by the ATLAS experiment and ensured that they are performed in a managed and sustainable manner. Services such as Distributed Data Management involving data replication,deletion and consistency checks, Monte Carlo Production, software installation and data reprocessing are described in greater detail. In addition to providing these central services we have undertaken several Cloud level stress tests and developed monitoring tools to aid with Cloud diagnostics. Furthermore we have defined good channels of communication between ATLAS, the T1 and the T2's and have pro-active contributions from the T2 manpower. A brief introduction to the GridKa Cloud is provided followed by a more detailed discussion of the operations model and ATLAS services within the Cloud.

Remote data access in computational jobs on the ATLAS data grid

CERN Document Server

Begy, Volodimir; The ATLAS collaboration; Lassnig, Mario

2018-01-01

This work describes the technique of remote data access from computational jobs on the ATLAS data grid. In comparison to traditional data movement and stage-in approaches it is well suited for data transfers which are asynchronous with respect to the job execution. Hence, it can be used for optimization of data access patterns based on various policies. In this study, remote data access is realized with the HTTP and WebDAV protocols, and is investigated in the context of intra- and inter-computing site data transfers. In both cases, the typical scenarios for application of remote data access are identified. The paper also presents an analysis of parameters influencing the data goodput between heterogeneous storage element - worker node pairs on the grid.

Analysis facility infrastructure (Tier-3) for ATLAS experiment

International Nuclear Information System (INIS)

Gonzalez de la Hoz, S.; March, L.; Ros, E.; Sanchez, J.; Amoros, G.; Fassi, F.; Fernandez, A.; Kaci, M.; Lamas, A.; Salt, J.

2008-01-01

In the ATLAS computing model the tiered hierarchy ranged from the Tier-0 (CERN) down to desktops or workstations (Tier-3). The focus on defining the roles of each tiered component has evolved with the initial emphasis on the Tier-0 and Tier-1 definition and roles. The various LHC (Large Hadron Collider) projects, including ATLAS, then evolved the tiered hierarchy to include Tier-2's (Regional centers) as part of their projects. Tier-3 centres, on the other hand, have been defined as whatever an institution could construct to support their Physics goals using institutional and otherwise leveraged resources and therefore have not been considered to be part of the official ATLAS computing resources. However, Tier-3 centres are going to exist and will have implications on how the computing model should support ATLAS physicists. Tier-3 users will want to access LHC data and simulations and will want to enable their resources to support their analysis and simulation work. This document will define how IFIC (Instituto de Fisica Corpuscular de Valencia), after discussing with the ATLAS Tier-3 task force, should interact with the ATLAS computing model, detail the conditions under which Tier-3 centres can expect some level of support and set reasonable expectations for the scope and support of ATLAS Tier-3 sites. (orig.)

Spanish ATLAS Tier-2: facing up to LHC Run 2

CERN Document Server

Gonzalez de la Hoz, Santiago; Fassi, Farida; Fernandez Casani, Alvaro; Kaci, Mohammed; Lacort Pellicer, Victor Ruben; Montiel Gonzalez, Almudena Del Rocio; Oliver Garcia, Elena; Pacheco Pages, Andres; Sánchez, Javier; Sanchez Martinez, Victoria; Salt, José; Villaplana Perez, Miguel

2015-01-01

The goal of this work is to describe the way of addressing the main challenges of Run-2 by the Spanish ATLAS Tier-2. The considerable increase of energy and luminosity for the upcoming Run-2 with respect to Run-1 has led to a revision of the ATLAS computing model as well as some of the main ATLAS computing tools. The adaptation on these changes will be shown, with the peculiarities that it is a distributed Tier-2 composed of three sites and its members are involved on ATLAS computing tasks with a hub of research, innovation and education.

ATLAS Open Data project

CERN Document Server

The ATLAS collaboration

2018-01-01

The current ATLAS model of Open Access to recorded and simulated data offers the opportunity to access datasets with a focus on education, training and outreach. This mandate supports the creation of platforms, projects, software, and educational products used all over the planet. We describe the overall status of ATLAS Open Data (http://opendata.atlas.cern) activities, from core ATLAS activities and releases to individual and group efforts, as well as educational programs, and final web or software-based (and hard-copy) products that have been produced or are under development. The relatively large number and heterogeneous use cases currently documented is driving an upcoming release of more data and resources for the ATLAS Community and anyone interested to explore the world of experimental particle physics and the computer sciences through data analysis.

ATLAS production system

CERN Document Server

Borodin, Mikhail; The ATLAS collaboration; De, Kaushik; Klimentov, Alexei; Golubkov, Dmitry; Maeno, Tadashi; Mashinistov, Ruslan; Wenaus, Torre; Padolski, Siarhei

2016-01-01

The second generation of the ATLAS production system called ProdSys2 is a distributed workload manager which used by thousands of physicists to analyze the data remotely, with the volume of processed data is beyond the exabyte scale, across a more than hundred heterogeneous sites. It achieves high utilization by combining dynamic job definition based on many criterias, such as input and output size, memory requirements and CPU consumption with manageable scheduling policies and by supporting different kind of computational resources, such as GRID, clouds, supercomputers and volunteering computers. Besides jobs definition Production System also includes flexible web user interface, which implements user-friendly environment for main ATLAS workflows, e.g. simple way of combining different data flows, and real-time monitoring, optimised for using with huge amount of information to present. We present an overview of the ATLAS Production System major components: job and task definition, workflow manager web user i...

ATLAS Fact Sheet : To raise awareness of the ATLAS detector and collaboration on the LHC

CERN Multimedia

ATLAS Outreach

2010-01-01

Facts on the Detector, Calorimeters, Muon System, Inner Detector, Pixel Detector, Semiconductor Tracker, Transition Radiation Tracker,, Surface hall, Cavern, Detector, Magnet system, Solenoid, Toroid, Event rates, Physics processes, Supersymmetric particles, Comparing LHC with Cosmic rays, Heavy ion collisions, Trigger and Data Acquisition TDAQ, Computing, the LHC and the ATLAS collaboration. This fact sheet also contains images of ATLAS and the collaboration as well as a short list of videos on ATLAS available for viewing.

Distributed processing and analysis of ATLAS experimental data

CERN Document Server

Barberis, D; The ATLAS collaboration

2011-01-01

The ATLAS experiment is taking data steadily since Autumn 2009, collecting close to 1 fb-1 of data (several petabytes of raw and reconstructed data per year of data-taking). Data are calibrated, reconstructed, distributed and analysed at over 100 different sites using the World-wide LHC Computing Grid and the tools produced by the ATLAS Distributed Computing project. In addition to event data, ATLAS produces a wealth of information on detector status, luminosity, calibrations, alignments, and data processing conditions. This information is stored in relational databases, online and offline, and made transparently available to analysers of ATLAS data world-wide through an infrastructure consisting of distributed database replicas and web servers that exploit caching technologies. This paper reports on the experience of using this distributed computing infrastructure with real data and in real time, on the evolution of the computing model driven by this experience, and on the system performance during the first...

Distributed processing and analysis of ATLAS experimental data

CERN Document Server

Barberis, D; The ATLAS collaboration

2011-01-01

The ATLAS experiment is taking data steadily since Autumn 2009, and collected so far over 5 fb-1 of data (several petabytes of raw and reconstructed data per year of data-taking). Data are calibrated, reconstructed, distributed and analysed at over 100 different sites using the World-wide LHC Computing Grid and the tools produced by the ATLAS Distributed Computing project. In addition to event data, ATLAS produces a wealth of information on detector status, luminosity, calibrations, alignments, and data processing conditions. This information is stored in relational databases, online and offline, and made transparently available to analysers of ATLAS data world-wide through an infrastructure consisting of distributed database replicas and web servers that exploit caching technologies. This paper reports on the experience of using this distributed computing infrastructure with real data and in real time, on the evolution of the computing model driven by this experience, and on the system performance during the...

ATLAS Tier-2 at the Compute Resource Center GoeGrid in Göttingen

Science.gov (United States)

Meyer, Jörg; Quadt, Arnulf; Weber, Pavel; ATLAS Collaboration

2011-12-01

GoeGrid is a grid resource center located in Göttingen, Germany. The resources are commonly used, funded, and maintained by communities doing research in the fields of grid development, computer science, biomedicine, high energy physics, theoretical physics, astrophysics, and the humanities. For the high energy physics community, GoeGrid serves as a Tier-2 center for the ATLAS experiment as part of the world-wide LHC computing grid (WLCG). The status and performance of the Tier-2 center is presented with a focus on the interdisciplinary setup and administration of the cluster. Given the various requirements of the different communities on the hardware and software setup the challenge of the common operation of the cluster is detailed. The benefits are an efficient use of computer and personpower resources.

Evolution of the ATLAS PanDA Workload Management System for Exascale Computational Science

CERN Document Server

Maeno, T; The ATLAS collaboration; Klimentov, A; Nilsson, P; Oleynik, D; Panitkin, S; Petrosyan, A; Schovancova, J; Vaniachine, A; Wenaus, T; Yu, D

2013-01-01

An important foundation underlying the impressive success of data processing and analysis in the ATLAS experiment [1] at the LHC [2] is the Production and Distributed Analysis (PanDA) workload management system [3]. PanDA was designed specifically for ATLAS and proved to be highly successful in meeting all the distributed computing needs of the experiment. However, the core design of PanDA is not experiment specific. The PanDA workload management system is capable of meeting the needs of other data intensive scientific applications. Alpha-Magnetic Spectrometer [4], an astro-particle experiment on the International Space Station, and the Compact Muon Solenoid [5], an LHC experiment, have successfully evaluated PanDA and are pursuing its adoption. In this paper, a description of the new program of work to develop a generic version of PanDA will be given, as well as the progress in extending PanDA's capabilities to support supercomputers and clouds and to leverage intelligent networking. PanDA has demonstrated a...

Evolution of the ATLAS PanDA Workload Management System for Exascale Computational Science

CERN Document Server

Maeno, T; The ATLAS collaboration; Klimentov, A; Nilsson, P; Oleynik, D; Panitkin, S; Petrosyan, A; Schovancova, J; Vaniachine, A; Wenaus, T; Yu, D

2014-01-01

An important foundation underlying the impressive success of data processing and analysis in the ATLAS experiment [1] at the LHC [2] is the Production and Distributed Analysis (PanDA) workload management system [3]. PanDA was designed specifically for ATLAS and proved to be highly successful in meeting all the distributed computing needs of the experiment. However, the core design of PanDA is not experiment specific. The PanDA workload management system is capable of meeting the needs of other data intensive scientific applications. Alpha-Magnetic Spectrometer [4], an astro-particle experiment on the International Space Station, and the Compact Muon Solenoid [5], an LHC experiment, have successfully evaluated PanDA and are pursuing its adoption. In this paper, a description of the new program of work to develop a generic version of PanDA will be given, as well as the progress in extending PanDA's capabilities to support supercomputers and clouds and to leverage intelligent networking. PanDA has demonstrated a...

ATLAS-AWS

International Nuclear Information System (INIS)

Gehrcke, Jan-Philip; Stonjek, Stefan; Kluth, Stefan

2010-01-01

We show how the ATLAS offline software is ported on the Amazon Elastic Compute Cloud (EC2). We prepare an Amazon Machine Image (AMI) on the basis of the standard ATLAS platform Scientific Linux 4 (SL4). Then an instance of the SLC4 AMI is started on EC2 and we install and validate a recent release of the ATLAS offline software distribution kit. The installed software is archived as an image on the Amazon Simple Storage Service (S3) and can be quickly retrieved and connected to new SL4 AMI instances using the Amazon Elastic Block Store (EBS). ATLAS jobs can then configure against the release kit using the ATLAS configuration management tool (cmt) in the standard way. The output of jobs is exported to S3 before the SL4 AMI is terminated. Job status information is transferred to the Amazon SimpleDB service. The whole process of launching instances of our AMI, starting, monitoring and stopping jobs and retrieving job output from S3 is controlled from a client machine using python scripts implementing the Amazon EC2/S3 API via the boto library working together with small scripts embedded in the SL4 AMI. We report our experience with setting up and operating the system using standard ATLAS job transforms.

ATLAS DDM integration in ARC

DEFF Research Database (Denmark)

Behrmann, Gerd; Cameron, David; Ellert, Mattias

by the DQ2 software. Managing ATLAS data within NDGF and between NDGF and other Grids used by ATLAS (the LHC Computing Grid and the Open Science Grid) presents a unique challenge for several reasons. Firstly, the entry point for data, the Tier 1 centre, is physically distributed among heterogeneous...

Alignment of the ATLAS Inner Detector Tracking System

CERN Document Server

Moles-Valls, R

2008-01-01

The ATLAS experiment is equipped with a tracking system for c harged particles built on two technologies: silicon and drift tube base detectors. These kind of detectors compose the ATLAS Inner Detector (ID). The Alignment of the ATLAS ID tracking s ystem requires the determination of almost 36000 degrees of freedom. From the tracking point o f view, the alignment parameters should be know to a few microns precision. This permits to att ain optimal measurements of the parameters of the charged particles trajectories, thus ena bling ATLAS to achieve its physics goals. The implementation of the alignment software, its framewor k and the data flow will be discussed. Special attention will be paid to the recent challenges wher e large scale computing simulation of the ATLAS detector has been performed, mimicking the ATLAS o peration, which is going to be very important for the LHC startup scenario. The alignment r esult for several challenges (real cosmic ray data taking and computing system commissioning) will be...

The effect of morphometric atlas selection on multi-atlas-based automatic brachial plexus segmentation

International Nuclear Information System (INIS)

Van de Velde, Joris; Wouters, Johan; Vercauteren, Tom; De Gersem, Werner; Achten, Eric; De Neve, Wilfried; Van Hoof, Tom

2015-01-01

The present study aimed to measure the effect of a morphometric atlas selection strategy on the accuracy of multi-atlas-based BP autosegmentation using the commercially available software package ADMIRE® and to determine the optimal number of selected atlases to use. Autosegmentation accuracy was measured by comparing all generated automatic BP segmentations with anatomically validated gold standard segmentations that were developed using cadavers. Twelve cadaver computed tomography (CT) atlases were included in the study. One atlas was selected as a patient in ADMIRE®, and multi-atlas-based BP autosegmentation was first performed with a group of morphometrically preselected atlases. In this group, the atlases were selected on the basis of similarity in the shoulder protraction position with the patient. The number of selected atlases used started at two and increased up to eight. Subsequently, a group of randomly chosen, non-selected atlases were taken. In this second group, every possible combination of 2 to 8 random atlases was used for multi-atlas-based BP autosegmentation. For both groups, the average Dice similarity coefficient (DSC), Jaccard index (JI) and Inclusion index (INI) were calculated, measuring the similarity of the generated automatic BP segmentations and the gold standard segmentation. Similarity indices of both groups were compared using an independent sample t-test, and the optimal number of selected atlases was investigated using an equivalence trial. For each number of atlases, average similarity indices of the morphometrically selected atlas group were significantly higher than the random group (p < 0,05). In this study, the highest similarity indices were achieved using multi-atlas autosegmentation with 6 selected atlases (average DSC = 0,598; average JI = 0,434; average INI = 0,733). Morphometric atlas selection on the basis of the protraction position of the patient significantly improves multi-atlas-based BP autosegmentation accuracy

Overview of ATLAS PanDA Workload Management

Science.gov (United States)

Maeno, T.; De, K.; Wenaus, T.; Nilsson, P.; Stewart, G. A.; Walker, R.; Stradling, A.; Caballero, J.; Potekhin, M.; Smith, D.; ATLAS Collaboration

2011-12-01

The Production and Distributed Analysis System (PanDA) plays a key role in the ATLAS distributed computing infrastructure. All ATLAS Monte-Carlo simulation and data reprocessing jobs pass through the PanDA system. We will describe how PanDA manages job execution on the grid using dynamic resource estimation and data replication together with intelligent brokerage in order to meet the scaling and automation requirements of ATLAS distributed computing. PanDA is also the primary ATLAS system for processing user and group analysis jobs, bringing further requirements for quick, flexible adaptation to the rapidly evolving analysis use cases of the early datataking phase, in addition to the high reliability, robustness and usability needed to provide efficient and transparent utilization of the grid for analysis users. We will describe how PanDA meets ATLAS requirements, the evolution of the system in light of operational experience, how the system has performed during the first LHC data-taking phase and plans for the future.

Overview of ATLAS PanDA Workload Management

International Nuclear Information System (INIS)

Maeno, T.; De, K.; Wenaus, T.; Nilsson, P.; Stewart, G.A.; Walker, R.; Stradling, A.; Caballero, J.; Potekhin, M.; Smith, D.

2011-01-01

The Production and Distributed Analysis System (PanDA) plays a key role in the ATLAS distributed computing infrastructure. All ATLAS Monte-Carlo simulation and data reprocessing jobs pass through the PanDA system. We will describe how PanDA manages job execution on the grid using dynamic resource estimation and data replication together with intelligent brokerage in order to meet the scaling and automation requirements of ATLAS distributed computing. PanDA is also the primary ATLAS system for processing user and group analysis jobs, bringing further requirements for quick, flexible adaptation to the rapidly evolving analysis use cases of the early datataking phase, in addition to the high reliability, robustness and usability needed to provide efficient and transparent utilization of the grid for analysis users. We will describe how PanDA meets ATLAS requirements, the evolution of the system in light of operational experience, how the system has performed during the first LHC data-taking phase and plans for the future.

Encoding atlases by randomized classification forests for efficient multi-atlas label propagation.

Science.gov (United States)

Zikic, D; Glocker, B; Criminisi, A

2014-12-01

We propose a method for multi-atlas label propagation (MALP) based on encoding the individual atlases by randomized classification forests. Most current approaches perform a non-linear registration between all atlases and the target image, followed by a sophisticated fusion scheme. While these approaches can achieve high accuracy, in general they do so at high computational cost. This might negatively affect the scalability to large databases and experimentation. To tackle this issue, we propose to use a small and deep classification forest to encode each atlas individually in reference to an aligned probabilistic atlas, resulting in an Atlas Forest (AF). Our classifier-based encoding differs from current MALP approaches, which represent each point in the atlas either directly as a single image/label value pair, or by a set of corresponding patches. At test time, each AF produces one probabilistic label estimate, and their fusion is done by averaging. Our scheme performs only one registration per target image, achieves good results with a simple fusion scheme, and allows for efficient experimentation. In contrast to standard forest schemes, in which each tree would be trained on all atlases, our approach retains the advantages of the standard MALP framework. The target-specific selection of atlases remains possible, and incorporation of new scans is straightforward without retraining. The evaluation on four different databases shows accuracy within the range of the state of the art at a significantly lower running time. Copyright © 2014 Elsevier B.V. All rights reserved.

A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI.

Science.gov (United States)

Iglesias, Juan Eugenio; Augustinack, Jean C; Nguyen, Khoa; Player, Christopher M; Player, Allison; Wright, Michelle; Roy, Nicole; Frosch, Matthew P; McKee, Ann C; Wald, Lawrence L; Fischl, Bruce; Van Leemput, Koen

2015-07-15

Automated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the hippocampal formation at the subregion level using ultra-high resolution, ex vivo MRI. Fifteen autopsy samples were scanned at 0.13 mm isotropic resolution (on average) using customized hardware. The images were manually segmented into 13 different hippocampal substructures using a protocol specifically designed for this study; precise delineations were made possible by the extraordinary resolution of the scans. In addition to the subregions, manual annotations for neighboring structures (e.g., amygdala, cortex) were obtained from a separate dataset of in vivo, T1-weighted MRI scans of the whole brain (1mm resolution). The manual labels from the in vivo and ex vivo data were combined into a single computational atlas of the hippocampal formation with a novel atlas building algorithm based on Bayesian inference. The resulting atlas can be used to automatically segment the hippocampal subregions in structural MRI images, using an algorithm that can analyze multimodal data and adapt to variations in MRI contrast due to differences in acquisition hardware or pulse sequences. The applicability of the atlas, which we are releasing as part of FreeSurfer (version 6.0), is demonstrated with experiments on three different publicly available datasets with different types of MRI contrast. The results show that the atlas and companion segmentation method: 1) can segment T1 and T2 images, as well as their combination, 2) replicate findings on mild cognitive impairment based on high-resolution T2 data, and 3) can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy in standard resolution (1mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5.3 (86% accuracy) and

Integrating Network Awareness in ATLAS Distributed Computing Using the ANSE Project

CERN Document Server

Klimentov, Alexei; The ATLAS collaboration; Petrosyan, Artem; Batista, Jorge Horacio; Mc Kee, Shawn Patrick

2015-01-01

A crucial contributor to the success of the massively scaled global computing system that delivers the analysis needs of the LHC experiments is the networking infrastructure upon which the system is built. The experiments have been able to exploit excellent high-bandwidth networking in adapting their computing models for the most efficient utilization of resources. New advanced networking technologies now becoming available such as software defined networking hold the potential of further leveraging the network to optimize workflows and dataflows, through proactive control of the network fabric on the part of high level applications such as experiment workload management and data management systems. End to end monitoring of networks using perfSONAR combined with data flow performance metrics further allows applications to adapt based on real time conditions. We will describe efforts underway in ATLAS on integrating network awareness at the application level, particularly in workload management, building upon ...

Spanish ATLAS Tier-2 facing up to Run-2 period of LHC

CERN Document Server

Gonzalez de la Hoz, Santiago; The ATLAS collaboration; Fassi, Farida; Fernandez Casani, Alvaro; Kaci, Mohammed; Lacort Pellicer, Victor Ruben; Montiel Gonzalez, Almudena Del Rocio; Oliver Garcia, Elena; Pacheco Pages, Andres; Salt, José; Villaplana Perez, Miguel; Sanchez Martinez, Victoria; Sánchez, Javier

2015-01-01

The goal of this work is to describe the way of addressing the main challenges of Run-2 by the Spanish ATLAS Tier-2. The considerable increase of energy and luminosity for the upcoming Run-2 w.r.t. Run-1 has led to a revision of the ATLAS computing model as well as some of the main ATLAS computing tools. The adaptation to these changes will be shown, with the peculiarities that it is a distributed Tier-2 composed of three sites and its members are involved on ATLAS computing tasks with a hub of research, innovation and education.

Migration of ATLAS PanDA to CERN

International Nuclear Information System (INIS)

Stewart, Graeme Andrew; Klimentov, Alexei; Maeno, Tadashi; Nevski, Pavel; Nowak, Marcin; De Castro Faria Salgado, Pedro Emanuel; Wenaus, Torre; Koblitz, Birger; Lamanna, Massimo

2010-01-01

The ATLAS Production and Distributed Analysis System (PanDA) is a key component of the ATLAS distributed computing infrastructure. All ATLAS production jobs, and a substantial amount of user and group analysis jobs, pass through the PanDA system, which manages their execution on the grid. PanDA also plays a key role in production task definition and the data set replication request system. PanDA has recently been migrated from Brookhaven National Laboratory (BNL) to the European Organization for Nuclear Research (CERN), a process we describe here. We discuss how the new infrastructure for PanDA, which relies heavily on services provided by CERN IT, was introduced in order to make the service as reliable as possible and to allow it to be scaled to ATLAS's increasing need for distributed computing. The migration involved changing the backend database for PanDA from MySQL to Oracle, which impacted upon the database schemas. The process by which the client code was optimised for the new database backend is discussed. We describe the procedure by which the new database infrastructure was tested and commissioned for production use. Operations during the migration had to be planned carefully to minimise disruption to ongoing ATLAS offline computing. All parts of the migration were fully tested before commissioning the new infrastructure and the gradual migration of computing resources to the new system allowed any problems of scaling to be addressed.

Analysis of Craniofacial Images using Computational Atlases and Deformation Fields

DEFF Research Database (Denmark)

Ólafsdóttir, Hildur

2008-01-01

purposes. The basis for most of the applications is non-rigid image registration. This approach brings one image into the coordinate system of another resulting in a deformation field describing the anatomical correspondence between the two images. A computational atlas representing the average anatomy...... of asymmetry. The analyses are applied to the study of three different craniofacial anomalies. The craniofacial applications include studies of Crouzon syndrome (in mice), unicoronal synostosis plagiocephaly and deformational plagiocephaly. Using the proposed methods, the thesis reveals novel findings about...... the craniofacial morphology and asymmetry of Crouzon mice. Moreover, a method to plan and evaluate treatment of children with deformational plagiocephaly, based on asymmetry assessment, is established. Finally, asymmetry in children with unicoronal synostosis is automatically assessed, confirming previous results...

Implementation of the ATLAS trigger within the ATLAS Multi­Threaded Software Framework AthenaMT

CERN Document Server

Wynne, Benjamin; The ATLAS collaboration

2016-01-01

We present an implementation of the ATLAS High Level Trigger that provides parallel execution of trigger algorithms within the ATLAS multi­threaded software framework, AthenaMT. This development will enable the ATLAS High Level Trigger to meet future challenges due to the evolution of computing hardware and upgrades of the Large Hadron Collider, LHC, and ATLAS Detector. During the LHC data­taking period starting in 2021, luminosity will reach up to three times the original design value. Luminosity will increase further, to up to 7.5 times the design value, in 2026 following LHC and ATLAS upgrades. This includes an upgrade of the ATLAS trigger architecture that will result in an increase in the High Level Trigger input rate by a factor of 4 to 10 compared to the current maximum rate of 100 kHz. The current ATLAS multiprocess framework, AthenaMP, manages a number of processes that process events independently, executing algorithms sequentially in each process. AthenaMT will provide a fully multi­threaded env...

Production Experience with the ATLAS Event Service

CERN Document Server

Benjamin, Douglas; The ATLAS collaboration

2016-01-01

The ATLAS Event Service (ES) has been designed and implemented for efficient running of ATLAS production workflows on a variety of computing platforms, ranging from conventional Grid sites to opportunistic, often short-lived resources, such as spot market commercial clouds, supercomputers and volunteer computing. The Event Service architecture allows real time delivery of fine grained workloads to running payload applications which process dispatched events or event ranges and immediately stream the outputs to highly scalable Object Stores. Thanks to its agile and flexible architecture the ES is currently being used by grid sites for assigning low priority workloads to otherwise idle computing resources; similarly harvesting HPC resources in an efficient back-fill mode; and massively scaling out to the 50-100k concurrent core level on the Amazon spot market to efficiently utilize those transient resources for peak production needs. Platform ports in development include ATLAS@Home (BOINC) and the Goggle Comput...

TU-AB-BRA-02: An Efficient Atlas-Based Synthetic CT Generation Method

International Nuclear Information System (INIS)

Han, X

2016-01-01

Purpose: A major obstacle for MR-only radiotherapy is the need to generate an accurate synthetic CT (sCT) from MR image(s) of a patient for the purposes of dose calculation and DRR generation. We propose here an accurate and efficient atlas-based sCT generation method, which has a computation speed largely independent of the number of atlases used. Methods: Atlas-based sCT generation requires a set of atlases with co-registered CT and MR images. Unlike existing methods that align each atlas to the new patient independently, we first create an average atlas and pre-align every atlas to the average atlas space. When a new patient arrives, we compute only one deformable image registration to align the patient MR image to the average atlas, which indirectly aligns the patient to all pre-aligned atlases. A patch-based non-local weighted fusion is performed in the average atlas space to generate the sCT for the patient, which is then warped back to the original patient space. We further adapt a PatchMatch algorithm that can quickly find top matches between patches of the patient image and all atlas images, which makes the patch fusion step also independent of the number of atlases used. Results: Nineteen brain tumour patients with both CT and T1-weighted MR images are used as testing data and a leave-one-out validation is performed. Each sCT generated is compared against the original CT image of the same patient on a voxel-by-voxel basis. The proposed method produces a mean absolute error (MAE) of 98.6±26.9 HU overall. The accuracy is comparable with a conventional implementation scheme, but the computation time is reduced from over an hour to four minutes. Conclusion: An average atlas space patch fusion approach can produce highly accurate sCT estimations very efficiently. Further validation on dose computation accuracy and using a larger patient cohort is warranted. The author is a full time employee of Elekta, Inc.

ATLAS PhD Grants 2015

CERN Multimedia

Marcelloni De Oliveira, Claudia

2015-01-01

ATLAS PHd Grants - We are excited to announce the creation of a dedicated grant scheme (thanks to a donation from Fabiola Gianotti and Peter Jenni following their award from the Fundamental Physics Prize foundation) to encourage young and high-caliber doctoral students in particle physics research (including computing for physics) and permit them to obtain world class exposure, supervision and training within the ATLAS collaboration. This special PhD Grant is aimed at graduate students preparing a doctoral thesis in particle physics (incl. computing for physics) to spend one year at CERN followed by one year support also at the home Institute.

ATLAS Grid Workflow Performance Optimization

CERN Document Server

Elmsheuser, Johannes; The ATLAS collaboration

2018-01-01

The CERN ATLAS experiment grid workflow system manages routinely 250 to 500 thousand concurrently running production and analysis jobs to process simulation and detector data. In total more than 300 PB of data is distributed over more than 150 sites in the WLCG. At this scale small improvements in the software and computing performance and workflows can lead to significant resource usage gains. ATLAS is reviewing together with CERN IT experts several typical simulation and data processing workloads for potential performance improvements in terms of memory and CPU usage, disk and network I/O. All ATLAS production and analysis grid jobs are instrumented to collect many performance metrics for detailed statistical studies using modern data analytics tools like ElasticSearch and Kibana. This presentation will review and explain the performance gains of several ATLAS simulation and data processing workflows and present analytics studies of the ATLAS grid workflows.

Renewable Energy Atlas of the United States

Energy Technology Data Exchange (ETDEWEB)

Kuiper, J. [Environmental Science Division; Hlava, K. [Environmental Science Division; Greenwood, H. [Environmentall Science Division; Carr, A. [Environmental Science Division

2013-12-13

The Renewable Energy Atlas (Atlas) of the United States is a compilation of geospatial data focused on renewable energy resources, federal land ownership, and base map reference information. This report explains how to add the Atlas to your computer and install the associated software. The report also includes: A description of each of the components of the Atlas; Lists of the Geographic Information System (GIS) database content and sources; and A brief introduction to the major renewable energy technologies. The Atlas includes the following: A GIS database organized as a set of Environmental Systems Research Institute (ESRI) ArcGIS Personal GeoDatabases, and ESRI ArcReader and ArcGIS project files providing an interactive map visualization and analysis interface.

Analysis facility infrastructure (Tier-3) for ATLAS experiment

CERN Document Server

González de la Hoza, S; Ros, E; Sánchez, J; Amorós, G; Fassi, F; Fernández, A; Kaci, M; Lamas, A; Salt, J

2008-01-01

In the ATLAS computing model the tiered hierarchy ranged from the Tier-0 (CERN) down to desktops or workstations (Tier-3). The focus on defining the roles of each tiered component has evolved with the initial emphasis on the Tier-0 and Tier-1 definition and roles. The various LHC (Large Hadron Collider) projects, including ATLAS, then evolved the tiered hierarchy to include Tier-2’s (Regional centers) as part of their projects. Tier-3 centres, on the other hand, have been defined as whatever an institution could construct to support their Physics goals using institutional and otherwise leveraged resources and therefore have not been considered to be part of the official ATLAS computing resources. However, Tier-3 centres are going to exist and will have implications on how the computing model should support ATLAS physicists. Tier-3 users will want to access LHC data and simulations and will want to enable their resources to support their analysis and simulation work. This document will define how IFIC (Insti...

Networks in ATLAS

Science.gov (United States)

McKee, Shawn; ATLAS Collaboration

2017-10-01

Networks have played a critical role in high-energy physics (HEP), enabling us to access and effectively utilize globally distributed resources to meet the needs of our physicists. Because of their importance in enabling our grid computing infrastructure many physicists have taken leading roles in research and education (R&E) networking, participating in, and even convening, network related meetings and research programs with the broader networking community worldwide. This has led to HEP benefiting from excellent global networking capabilities for little to no direct cost. However, as other science domains ramp-up their need for similar networking it becomes less clear that this situation will continue unchanged. What this means for ATLAS in particular needs to be understood. ATLAS has evolved its computing model since the LHC started based upon its experience with using globally distributed resources. The most significant theme of those changes has been increased reliance upon, and use of, its networks. We will report on a number of networking initiatives in ATLAS including participation in the global perfSONAR network monitoring and measuring efforts of WLCG and OSG, the collaboration with the LHCOPN/LHCONE effort, the integration of network awareness into PanDA, the use of the evolving ATLAS analytics framework to better understand our networks and the changes in our DDM system to allow remote access to data. We will also discuss new efforts underway that are exploring the inclusion and use of software defined networks (SDN) and how ATLAS might benefit from: • Orchestration and optimization of distributed data access and data movement. • Better control of workflows, end to end. • Enabling prioritization of time-critical vs normal tasks • Improvements in the efficiency of resource usage

Data handling and processing for the ATLAS experiment

CERN Document Server

Barberis, D; The ATLAS collaboration

2011-01-01

The ATLAS experiment has taken data steadily since Autumn 2009, collecting close to 1 fm-1 of data (several petabytes of raw and reconstructed data per year of data-taking). Data are calibrated, reconstructed, distributed and analysed at over 100 different sites using the World-wide LHC Computing Grid and the tools produced by the ATLAS Distributed Computing project. This paper reports on the experience of using this distributed computing infrastructure with real data and in real time, on the evolution of the computing model driven by this experience, and on the system performance during the first two years of operation.

ProstAtlas: A digital morphologic atlas of the prostate

International Nuclear Information System (INIS)

Betrouni, N.; Iancu, A.; Puech, P.; Mordon, S.; Makni, N.

2012-01-01

Computer-aided medical interventions and medical robotics for prostate cancer have known an increasing interest and research activity. However before the routine deployment of these procedures in clinical practice becomes a reality, in vivo and in silico validations must be undertaken. In this study, we developed a digital morphologic atlas of the prostate. We were interested by the gland, the peripheral zone and the central gland. Starting from an image base collected from 30 selected patients, a mean shape and most important deformations for each structure were deduced using principal component analysis. The usefulness of this atlas was highlighted in two applications: image simulation and physical phantom design

Reconstruction and identification of electrons in the Atlas experiment. Setup of a Tier 2 of the computing grid; Reconstruction et identification des electrons dans l'experience Atlas. Participation a la mise en place d'un Tier 2 de la grille de calcul

Energy Technology Data Exchange (ETDEWEB)

Derue, F

2008-03-15

The origin of the mass of elementary particles is linked to the electroweak symmetry breaking mechanism. Its study will be one of the main efforts of the Atlas experiment at the Large Hadron Collider of CERN, starting in 2008. In most cases, studies will be limited by our knowledge of the detector performances, as the precision of the energy reconstruction or the efficiency to identify particles. This manuscript presents a work dedicated to the reconstruction of electrons in the Atlas experiment with simulated data and data taken during the combined test beam of 2004. The analysis of the Atlas data implies the use of a huge amount of computing and storage resources which brought to the development of a world computing grid. (author)

Glance Information System for ATLAS Management

International Nuclear Information System (INIS)

Grael, F F; Maidantchik, C; Évora, L H R A; Karam, K; Moraes, L O F; Cirilli, M; Nessi, M; Pommès, K

2011-01-01

ATLAS Experiment is an international collaboration where more than 37 countries, 172 institutes and laboratories, 2900 physicists, engineers, and computer scientists plus 700 students participate. The management of this teamwork involves several aspects such as institute contribution, employment records, members' appointment, authors' list, preparation and publication of papers and speakers nomination. Previously, most of the information was accessible by a limited group and developers had to face problems such as different terminology, diverse data modeling, heterogeneous databases and unlike users needs. Moreover, the systems were not designed to handle new requirements. The maintenance has to be an easy task due to the long lifetime experiment and professionals turnover. The Glance system, a generic mechanism for accessing any database, acts as an intermediate layer isolating the user from the particularities of each database. It retrieves, inserts and updates the database independently of its technology and modeling. Relying on Glance, a group of systems were built to support the ATLAS management and operation aspects: ATLAS Membership, ATLAS Appointments, ATLAS Speakers, ATLAS Analysis Follow-Up, ATLAS Conference Notes, ATLAS Thesis, ATLAS Traceability and DSS Alarms Viewer. This paper presents the overview of the Glance information framework and describes the privilege mechanism developed to grant different level of access for each member and system.

Glance Information System for ATLAS Management

Science.gov (United States)

Grael, F. F.; Maidantchik, C.; Évora, L. H. R. A.; Karam, K.; Moraes, L. O. F.; Cirilli, M.; Nessi, M.; Pommès, K.; ATLAS Collaboration

2011-12-01

ATLAS Experiment is an international collaboration where more than 37 countries, 172 institutes and laboratories, 2900 physicists, engineers, and computer scientists plus 700 students participate. The management of this teamwork involves several aspects such as institute contribution, employment records, members' appointment, authors' list, preparation and publication of papers and speakers nomination. Previously, most of the information was accessible by a limited group and developers had to face problems such as different terminology, diverse data modeling, heterogeneous databases and unlike users needs. Moreover, the systems were not designed to handle new requirements. The maintenance has to be an easy task due to the long lifetime experiment and professionals turnover. The Glance system, a generic mechanism for accessing any database, acts as an intermediate layer isolating the user from the particularities of each database. It retrieves, inserts and updates the database independently of its technology and modeling. Relying on Glance, a group of systems were built to support the ATLAS management and operation aspects: ATLAS Membership, ATLAS Appointments, ATLAS Speakers, ATLAS Analysis Follow-Up, ATLAS Conference Notes, ATLAS Thesis, ATLAS Traceability and DSS Alarms Viewer. This paper presents the overview of the Glance information framework and describes the privilege mechanism developed to grant different level of access for each member and system.

The ATLAS online High Level Trigger framework: Experience reusing offline software components in the ATLAS trigger

International Nuclear Information System (INIS)

Wiedenmann, Werner

2010-01-01

Event selection in the ATLAS High Level Trigger is accomplished to a large extent by reusing software components and event selection algorithms developed and tested in an offline environment. Many of these offline software modules are not specifically designed to run in a heavily multi-threaded online data flow environment. The ATLAS High Level Trigger (HLT) framework based on the GAUDI and ATLAS ATHENA frameworks, forms the interface layer, which allows the execution of the HLT selection and monitoring code within the online run control and data flow software. While such an approach provides a unified environment for trigger event selection across all of ATLAS, it also poses strict requirements on the reused software components in terms of performance, memory usage and stability. Experience of running the HLT selection software in the different environments and especially on large multi-node trigger farms has been gained in several commissioning periods using preloaded Monte Carlo events, in data taking periods with cosmic events and in a short period with proton beams from LHC. The contribution discusses the architectural aspects of the HLT framework, its performance and its software environment within the ATLAS computing, trigger and data flow projects. Emphasis is also put on the architectural implications for the software by the use of multi-core processors in the computing farms and the experiences gained with multi-threading and multi-process technologies.

The Next Generation ATLAS Production System

CERN Document Server

Borodin, Mikhail; The ATLAS collaboration; Golubkov, Dmitry; Klimentov, Alexei; Maeno, Tadashi; Mashinistov, Ruslan; Vaniachine, Alexandre

2015-01-01

The ATLAS experiment at LHC data processing and simulation grows continuously, as more data and more use cases emerge. For data processing the ATLAS experiment adopted the data transformation approach, where software applications transform the input data into outputs. In the ATLAS production system, each data transformation is represented by a task, a collection of many jobs, dynamically submitted by the ATLAS workload management system (PanDA/JEDI) and executed on the Grid, clouds and supercomputers. Patterns in ATLAS data transformation workflows composed of many tasks provided a scalable production system framework for template definitions of the many-tasks workflows. User interface and system logic of these workflows are being implemented in the Database Engine for Tasks (DEFT). Such development required using modern computing technologies and approaches. We report technical details of this development: database implementation, server logic and Web user interface technologies.

An automated meta-monitoring mobile application and front-end interface for the ATLAS computing model

Energy Technology Data Exchange (ETDEWEB)

Kawamura, Gen; Quadt, Arnulf [II. Physikalisches Institut, Georg-August-Universitaet Goettingen (Germany)

2016-07-01

Efficient administration of computing centres requires advanced tools for the monitoring and front-end interface of the infrastructure. Providing the large-scale distributed systems as a global grid infrastructure, like the Worldwide LHC Computing Grid (WLCG) and ATLAS computing, is offering many existing web pages and information sources indicating the status of the services, systems and user jobs at grid sites. A meta-monitoring mobile application which automatically collects the information could give every administrator a sophisticated and flexible interface of the infrastructure. We describe such a solution; the MadFace mobile application developed at Goettingen. It is a HappyFace compatible mobile application which has a user-friendly interface. It also becomes very feasible to automatically investigate the status and problem from different sources and provides access of the administration roles for non-experts.

Production experience with the ATLAS Event Service

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00066086; The ATLAS collaboration; Calafiura, Paolo; Childers, John Taylor; De, Kaushik; Guan, Wen; Maeno, Tadashi; Nilsson, Paul; Tsulaia, Vakhtang; van Gemmeren, Peter; Wenaus, Torre

2017-01-01

The ATLAS Event Service (AES) has been designed and implemented for efficient running of ATLAS production workflows on a variety of computing platforms, ranging from conventional Grid sites to opportunistic, often short-lived resources, such as spot market commercial clouds, supercomputers and volunteer computing. The Event Service architecture allows real time delivery of fine grained workloads to running payload applications which process dispatched events or event ranges and immediately stream the outputs to highly scalable Object Stores. Thanks to its agile and flexible architecture the AES is currently being used by grid sites for assigning low priority workloads to otherwise idle computing resources; similarly harvesting HPC resources in an efficient back-fill mode; and massively scaling out to the 50-100k concurrent core level on the Amazon spot market to efficiently utilize those transient resources for peak production needs. Platform ports in development include ATLAS@Home (BOINC) and the Google Comp...

Integration of Titan supercomputer at OLCF with ATLAS Production System

CERN Document Server

AUTHOR|(SzGeCERN)643806; The ATLAS collaboration; De, Kaushik; Klimentov, Alexei; Nilsson, Paul; Oleynik, Danila; Padolski, Siarhei; Panitkin, Sergey; Wenaus, Torre

2017-01-01

The PanDA (Production and Distributed Analysis) workload management system was developed to meet the scale and complexity of distributed computing for the ATLAS experiment. PanDA managed resources are distributed worldwide, on hundreds of computing sites, with thousands of physicists accessing hundreds of Petabytes of data and the rate of data processing already exceeds Exabyte per year. While PanDA currently uses more than 200,000 cores at well over 100 Grid sites, future LHC data taking runs will require more resources than Grid computing can possibly provide. Additional computing and storage resources are required. Therefore ATLAS is engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. In this paper we will describe a project aimed at integration of ATLAS Production System with Titan supercomputer at Oak Ridge Leadership Computing Facility (OLCF). Current approach utilizes modified PanDA Pilot framework for jo...

Integration of Titan supercomputer at OLCF with ATLAS production system

CERN Document Server

Panitkin, Sergey; The ATLAS collaboration

2016-01-01

The PanDA (Production and Distributed Analysis) workload management system was developed to meet the scale and complexity of distributed computing for the ATLAS experiment. PanDA managed resources are distributed worldwide, on hundreds of computing sites, with thousands of physicists accessing hundreds of Petabytes of data and the rate of data processing already exceeds Exabyte per year. While PanDA currently uses more than 200,000 cores at well over 100 Grid sites, future LHC data taking runs will require more resources than Grid computing can possibly provide. Additional computing and storage resources are required. Therefore ATLAS is engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. In this talk we will describe a project aimed at integration of ATLAS Production System with Titan supercomputer at Oak Ridge Leadership Computing Facility (OLCF). Current approach utilizes modified PanDA Pilot framework for job...

Data handling and processing for the ATLAS experiment

CERN Document Server

Barberis, D; The ATLAS collaboration

2011-01-01

The ATLAS experiment is taking data steadily since Autumn 2009, collecting so far over 2.5 fm-1 of data (several petabytes of raw and reconstructed data per year of data-taking). Data are calibrated, reconstructed, distributed and analysed at over 100 different sites using the World-wide LHC Computing Grid and the tools produced by the ATLAS Distributed Computing project. This paper reports on the experience of setting up and operating this distributed computing infrastructure with real data and in real time, on the evolution of the computing model driven by this experience, and on the system performance during the first two years of operation.

A novel computed method to reconstruct the bilateral digital interarticular channel of atlas and its use on the anterior upper cervical screw fixation

Directory of Open Access Journals (Sweden)

Ai-Min Wu

2016-02-01

Full Text Available Purpose. To investigate a novel computed method to reconstruct the bilateral digital interarticular channel of atlas and its potential use on the anterior upper cervical screw fixation. Methods. We have used the reverse engineering software (image-processing software and computer-aided design software to create the approximate and optimal digital interarticular channel of atlas for 60 participants. Angles of channels, diameters of inscribed circles, long and short axes of ellipses were measured and recorded, and gender-specific analysis was also performed. Results. The channels provided sufficient space for one or two screws, and the parameters of channels are described. While the channels of females were smaller than that of males, no significant difference of angles between males and females were observed. Conclusion. Our study demonstrates the radiological features of approximate digital interarticular channels, optimal digital interarticular channels of atlas, and provides the reference trajectory of anterior transarticular screws and anterior occiput-to-axis screws. Additionally, we provide a protocol that can help make a pre-operative plan for accurate placement of anterior transarticular screws and anterior occiput-to-axis screws.

Scaling up ATLAS Event Service to production levels on opportunistic computing platforms

CERN Document Server

Benjamin, Douglas; The ATLAS collaboration; Ernst, Michael; Guan, Wen; Hover, John; Lesny, David; Maeno, Tadashi; Nilsson, Paul; Tsulaia, Vakhtang; van Gemmeren, Peter; Vaniachine, Alexandre; Wang, Fuquan; Wenaus, Torre

2016-01-01

Continued growth in public cloud and HPC resources is on track to overcome the dedicated resources available for ATLAS on the WLCG. Example of such platforms are Amazon AWS EC2 Spot Instances, Edison Cray XC30 supercomputer, backfill at the Tier-2 and Tier-3 sites, opportunistic resources at the Open Science Grid, and ATLAS High Level Trigger farm between the data taking periods. Because of opportunistic resources specifics such as preemptive job scheduling and data I/O, their efficient usage requires workflow innovations provided by the ATLAS Event Service. Thanks to the finer granularity of the Event Service data processing workflow, the opportunistic resources are used more efficiently. We report on our progress in scaling opportunistic resource usage to double-digit levels in ATLAS production.

Energy Frontier Research With ATLAS: Final Report

Energy Technology Data Exchange (ETDEWEB)

Butler, John [Boston Univ., MA (United States); Black, Kevin [Boston Univ., MA (United States); Ahlen, Steve [Boston Univ., MA (United States)

2016-06-14

The Boston University (BU) group is playing key roles across the ATLAS experiment: in detector operations, the online trigger, the upgrade, computing, and physics analysis. Our team has been critical to the maintenance and operations of the muon system since its installation. During Run 1 we led the muon trigger group and that responsibility continues into Run 2. BU maintains and operates the ATLAS Northeast Tier 2 computing center. We are actively engaged in the analysis of ATLAS data from Run 1 and Run 2. Physics analyses we have contributed to include Standard Model measurements (W and Z cross sections, t\\bar{t} differential cross sections, WWW^* production), evidence for the Higgs decaying to \\tau^+\\tau^-, and searches for new phenomena (technicolor, Z' and W', vector-like quarks, dark matter).

Class Generation for Numerical Wind Atlases

DEFF Research Database (Denmark)

Cutler, N.J.; Jørgensen, B.H.; Ersbøll, Bjarne Kjær

2006-01-01

A new optimised clustering method is presented for generating wind classes for mesoscale modelling to produce numerical wind atlases. It is compared with the existing method of dividing the data in 12 to 16 sectors, 3 to 7 wind-speed bins and dividing again according to the stability...... of the atmosphere. Wind atlases are typically produced using many years of on-site wind observations at many locations. Numerical wind atlases are the result of mesoscale model integrations based on synoptic scale wind climates and can be produced in a number of hours of computation. 40 years of twice daily NCEP...... adapting to the local topography. The purpose of forming classes is to minimise the computational time for the mesoscale model while still representing the synoptic climate features. Only tried briefly in the past, clustering has traits that can be used to improve the existing class generation method...

Trigger Menu-aware Monitoring for the ATLAS experiment

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00441925; The ATLAS collaboration

2017-01-01

Changes in the trigger menu, the online algorithmic event-selection of the ATLAS experiment at the LHC, are followed by adjustments to the ATLAS trigger monitoring systems. During Run 1, and so far in Run 2, ATLAS has deployed monitoring updates with the installation of new software releases at Tier-0, the first level of the ATLAS computing grid. Having to wait for a new software release to be installed at Tier-0, in order to update ATLAS offline trigger monitoring configurations, results in a lag with respect to the modification of the trigger menu. We present the design and implementation of a `trigger menu-aware' monitoring system that aims to simplify the ATLAS operational workflows by allowing monitoring configuration changes to be made at the Tier-0 site by utilising an Oracle SQL database.

ATLAS BigPanDA Monitoring

CERN Document Server

Padolski, Siarhei; The ATLAS collaboration; Klimentov, Alexei; Korchuganova, Tatiana

2017-01-01

BigPanDA monitoring is a web based application which provides various processing and representation of the Production and Distributed Analysis (PanDA) system objects states. Analyzing hundreds of millions of computation entities such as an event or a job BigPanDA monitoring builds different scale and levels of abstraction reports in real time mode. Provided information allows users to drill down into the reason of a concrete event failure or observe system bigger picture such as tracking the computation nucleus and satellites performance or the progress of whole production campaign. PanDA system was originally developed for the Atlas experiment and today effectively managing more than 2 million jobs per day distributed over 170 computing centers worldwide. BigPanDA is its core component commissioned in the middle of 2014 and now is the primary source of information for ATLAS users about state of their computations and the source of decision support information for shifters, operators and managers. In this wor...

ATLAS BigPanDA Monitoring

CERN Document Server

Padolski, Siarhei; The ATLAS collaboration

2017-01-01

BigPanDA monitoring is a web-based application that provides various processing and representation of the Production and Distributed Analysis (PanDA) system objects states. Analysing hundreds of millions of computation entities such as an event or a job BigPanDA monitoring builds different scale and levels of abstraction reports in real time mode. Provided information allows users to drill down into the reason of a concrete event failure or observe system bigger picture such as tracking the computation nucleus and satellites performance or the progress of whole production campaign. PanDA system was originally developed for the Atlas experiment and today effectively managing more than 2 million jobs per day distributed over 170 computing centers worldwide. BigPanDA is its core component commissioned in the middle of 2014 and now is the primary source of information for ATLAS users about state of their computations and the source of decision support information for shifters, operators and managers. In this work...

Scaling up ATLAS Event Service to production levels on opportunistic computing platforms

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00066086; The ATLAS collaboration; Caballero, Jose; Ernst, Michael; Guan, Wen; Hover, John; Lesny, David; Maeno, Tadashi; Nilsson, Paul; Tsulaia, Vakhtang; van Gemmeren, Peter; Vaniachine, Alexandre; Wang, Fuquan; Wenaus, Torre

2016-01-01

Continued growth in public cloud and HPC resources is on track to exceed the dedicated resources available for ATLAS on the WLCG. Examples of such platforms are Amazon AWS EC2 Spot Instances, Edison Cray XC30 supercomputer, backfill at Tier 2 and Tier 3 sites, opportunistic resources at the Open Science Grid (OSG), and ATLAS High Level Trigger farm between the data taking periods. Because of specific aspects of opportunistic resources such as preemptive job scheduling and data I/O, their efficient usage requires workflow innovations provided by the ATLAS Event Service. Thanks to the finer granularity of the Event Service data processing workflow, the opportunistic resources are used more efficiently. We report on our progress in scaling opportunistic resource usage to double-digit levels in ATLAS production.

Atlas of Skeletal SPECT/CT Clinical Images

International Nuclear Information System (INIS)

2016-01-01

The atlas focuses specifically on single photon emission computed tomography/computed tomography (SPECT/CT) in musculoskeletal imaging, and thus illustrates the inherent advantages of the combination of the metabolic and anatomical component in a single procedure. In addition, the atlas provides information on the usefulness of several sets of specific indications. The publication, which serves more as a training tool rather than a textbook, will help to further integrate the SPECT and CT experience in clinical practice by presenting a series of typical cases with many different patterns of SPECT/CT seen in bone scintigraphy

Analysis Facility infrastructure (TIER3) for ATLAS High Energy physics experiment

International Nuclear Information System (INIS)

Gonzalez de la Hoz, S.; March, L.; Ros, E.; Sanchez, J.; Amoros, G.; Fassi, F.; Fernandez, A.; Kaci, M.; Lamas, A.; Salt, J.

2007-01-01

ATLAS project has been asked to define the scope and role of Tier-3 resources (facilities or centres) within the existing ATLAS computing model, activities and facilities. This document attempts to address these questions by describing Tier-3 resources generally, and their relationship to the ATLAS Software and Computing Project. Originally the tiered computing model came out of MONARC (see http://monarc.web.cern.ch/MONARC/) work and was predicated upon the network being a scarce resource. In this model the tiered hierarchy ranged from the Tier-0 (CERN) down to the desktop or workstation (Tier 3). The focus on defining the roles of each tiered component has evolved with the initial emphasis on the Tier-0 (CERN) and Tier-1 (National centres) definition and roles. The various LHC projects, including ATLAS, then evolved the tiered hierarchy to include Tier-2s (Regional centers) as part of their projects. Tier-3s, on the other hand, have (implicitly and sometime explicitly) been defined as whatever an institution could construct to support their Physics goals using institutional and otherwise leveraged resources and therefore have not been considered to be part of the official ATLAS Research Program computing resources nor under their control, meaning there is no formal MOU process to designate sites as Tier-3s and no formal control of the program over the Tier-3 resources. Tier-3s are the responsibility of individual institutions to define, fund, deploy and support. However, having noted this, we must also recognize that Tier-3s must exist and will have implications for how our computing model should support ATLAS physicists. Tier-3 users will want to access data and simulations and will want to enable their Tier-3 resources to support their analysis and simulation work. Tiers 3s are an important resource for physicists to analyze LHC (Large Hadron Collider) data. This document will define how Tier-3s should best interact with the ATLAS computing model, detail the

Multi-threaded ATLAS simulation on Intel Knights Landing processors

Science.gov (United States)

Farrell, Steven; Calafiura, Paolo; Leggett, Charles; Tsulaia, Vakhtang; Dotti, Andrea; ATLAS Collaboration

2017-10-01

The Knights Landing (KNL) release of the Intel Many Integrated Core (MIC) Xeon Phi line of processors is a potential game changer for HEP computing. With 72 cores and deep vector registers, the KNL cards promise significant performance benefits for highly-parallel, compute-heavy applications. Cori, the newest supercomputer at the National Energy Research Scientific Computing Center (NERSC), was delivered to its users in two phases with the first phase online at the end of 2015 and the second phase now online at the end of 2016. Cori Phase 2 is based on the KNL architecture and contains over 9000 compute nodes with 96GB DDR4 memory. ATLAS simulation with the multithreaded Athena Framework (AthenaMT) is a good potential use-case for the KNL architecture and supercomputers like Cori. ATLAS simulation jobs have a high ratio of CPU computation to disk I/O and have been shown to scale well in multi-threading and across many nodes. In this paper we will give an overview of the ATLAS simulation application with details on its multi-threaded design. Then, we will present a performance analysis of the application on KNL devices and compare it to a traditional x86 platform to demonstrate the capabilities of the architecture and evaluate the benefits of utilizing KNL platforms like Cori for ATLAS production.

ATLAS BigPanDA Monitoring and Its Evolution

CERN Document Server

Wenaus, Torre; The ATLAS collaboration; Korchuganova, Tatiana

2016-01-01

BigPanDA is the latest generation of the monitoring system for the Production and Distributed Analysis (PanDA) system. The BigPanDA monitor is a core component of PanDA and also serves the monitoring needs of the new ATLAS Production System Prodsys-2. BigPanDA has been developed to serve the growing computation needs of the ATLAS Experiment and the wider applications of PanDA beyond ATLAS. Through a system-wide job database, the BigPanDA monitor provides a comprehensive and coherent view of the tasks and jobs executed by the system, from high level summaries to detailed drill-down job diagnostics. The system has been in production and has remained in continuous development since mid 2014, today effectively managing more than 2 million jobs per day distributed over 150 computing centers worldwide. BigPanDA also delivers web-based analytics and system state views to groups of users including distributed computing systems operators, shifters, physicist end-users, computing managers and accounting services. Provi...

Multi-threaded ATLAS simulation on Intel Knights Landing processors

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00014247; The ATLAS collaboration; Calafiura, Paolo; Leggett, Charles; Tsulaia, Vakhtang; Dotti, Andrea

2017-01-01

The Knights Landing (KNL) release of the Intel Many Integrated Core (MIC) Xeon Phi line of processors is a potential game changer for HEP computing. With 72 cores and deep vector registers, the KNL cards promise significant performance benefits for highly-parallel, compute-heavy applications. Cori, the newest supercomputer at the National Energy Research Scientific Computing Center (NERSC), was delivered to its users in two phases with the first phase online at the end of 2015 and the second phase now online at the end of 2016. Cori Phase 2 is based on the KNL architecture and contains over 9000 compute nodes with 96GB DDR4 memory. ATLAS simulation with the multithreaded Athena Framework (AthenaMT) is a good potential use-case for the KNL architecture and supercomputers like Cori. ATLAS simulation jobs have a high ratio of CPU computation to disk I/O and have been shown to scale well in multi-threading and across many nodes. In this paper we will give an overview of the ATLAS simulation application with detai...

Development, deployment and operations of ATLAS databases

International Nuclear Information System (INIS)

Vaniachine, A. V.; von der Schmitt, J. G.

2008-01-01

In preparation for ATLAS data taking, a coordinated shift from development towards operations has occurred in ATLAS database activities. In addition to development and commissioning activities in databases, ATLAS is active in the development and deployment (in collaboration with the WLCG 3D project) of the tools that allow the worldwide distribution and installation of databases and related datasets, as well as the actual operation of this system on ATLAS multi-grid infrastructure. We describe development and commissioning of major ATLAS database applications for online and offline. We present the first scalability test results and ramp-up schedule over the initial LHC years of operations towards the nominal year of ATLAS running, when the database storage volumes are expected to reach 6.1 TB for the Tag DB and 1.0 TB for the Conditions DB. ATLAS database applications require robust operational infrastructure for data replication between online and offline at Tier-0, and for the distribution of the offline data to Tier-1 and Tier-2 computing centers. We describe ATLAS experience with Oracle Streams and other technologies for coordinated replication of databases in the framework of the WLCG 3D services

Global Data Grid Efforts for ATLAS

CERN Multimedia

Gardner, R.

2001-01-01

Over the past two years computational data grids have emerged as a promising new technology for large scale, data-intensive computing required by the LHC experiments, as outlined by the recent "Hoffman" review panel that addressed the LHC computing challenge. The problem essentially is to seamlessly link physicists to petabyte-scale data and computing resources, distributed worldwide, and connected by high-bandwidth research networks. Several new collaborative initiatives in Europe, the United States, and Asia have formed to address the problem. These projects are of great interest to ATLAS physicists and software developers since their objective is to offer tools that can be integrated into the core ATLAS application framework for distributed event reconstruction, Monte Carlo simulation, and data analysis, making it possible for individuals and groups of physicists to share information, data, and computing resources in new ways and at scales not previously attempted. In addition, much of the distributed IT...

PanDA: distributed production and distributed analysis system for ATLAS

International Nuclear Information System (INIS)

Maeno, T

2008-01-01

A new distributed software system was developed in the fall of 2005 for the ATLAS experiment at the LHC. This system, called PANDA, provides an integrated service architecture with late binding of jobs, maximal automation through layered services, tight binding with ATLAS Distributed Data Management system [1], advanced error discovery and recovery procedures, and other features. In this talk, we will describe the PANDA software system. Special emphasis will be placed on the evolution of PANDA based on one and half year of real experience in carrying out Computer System Commissioning data production [2] for ATLAS. The architecture of PANDA is well suited for the computing needs of the ATLAS experiment, which is expected to be one of the first HEP experiments to operate at the petabyte scale

Grid production with the ATLAS Event Service

CERN Document Server

Benjamin, Douglas; The ATLAS collaboration

2018-01-01

ATLAS has developed and previously presented a new computing architecture, the Event Service, that allows real time delivery of fine grained workloads which process dispatched events (or event ranges) and immediately streams outputs. The principal aim was to profit from opportunistic resources such as commercial cloud, supercomputing, and volunteer computing, and otherwise unused cycles on clusters and grids. During the development and deployment phase, its utility also on the grid and conventional clusters for the exploitation of otherwise unused cycles became apparent. Here we describe our experience commissioning the Event Service on the grid in the ATLAS production system. We study the performance compared with standard simulation production. We describe the integration with the ATLAS data management system to ensure scalability and compatibility with object stores. Finally, we outline the remaining steps towards a fully commissioned system.

Evolution of the Argonne Tandem Linear Accelerator System (ATLAS) control system

International Nuclear Information System (INIS)

Power, M.; Munson, F.

2012-01-01

Given that the Argonne Tandem Linear Accelerator System (ATLAS) recently celebrated its 25. anniversary, this paper will explore the past, present, and future of the ATLAS Control System, and how it has evolved along with the accelerator and control system technology. ATLAS as we know it today, originated with a Tandem Van de Graff in the sixties. With the addition of the Booster section in the late seventies, came the first computerized control. ATLAS itself was placed into service on June 25, 1985, and was the world's first superconducting linear accelerator for ions. Since its dedication as a National User Facility, more than a thousand experiments by more than 2,000 users worldwide, have taken advantage of the unique capabilities it provides. Today, ATLAS continues to be a user facility for physicists who study the particles that form the heart of atoms. Its most recent addition, CARIBU (Californium Rare Isotope Breeder Upgrade), creates special beams that feed into ATLAS. ATLAS is similar to a living organism, changing and responding to new technological challenges and research needs. As it continues to evolve, so does the control system: from the original days using a DEC PDP-11/34 computer and two CAMAC crates, to a DEC Alpha computer running Vsystem software and more than twenty CAMAC crates, to distributed computers and VME systems. Future upgrades are also in the planning stages that will continue to evolve the control system. (authors)

Networks in ATLAS

CERN Document Server

Mc Kee, Shawn Patrick; The ATLAS collaboration

2016-01-01

Networks have played a critical role in high-energy physics (HEP), enabling us to access and effectively utilize globally distributed resources to meet the needs of our physicists. Because of their importance in enabling our grid computing infrastructure many physicists have taken leading roles in research and education (R&E) networking, participating in, and even convening, network related meetings and research programs with the broader networking community worldwide. This has led to HEP benefiting from excellent global networking capabilities for little to no direct cost. However, as other science domains ramp-up their need for similar networking it becomes less clear that this situation will continue unchanged. What this means for ATLAS in particular needs to be understood. ATLAS has evolved its computing model since the LHC started based upon its experience with using globally distributed resources. The most significant theme of those changes has been increased reliance upon, and use of, its networks....

Networks in ATLAS

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00260714; The ATLAS collaboration

2017-01-01

Networks have played a critical role in high-energy physics (HEP), enabling us to access and effectively utilize globally distributed resources to meet the needs of our physicists. Because of their importance in enabling our grid computing infrastructure many physicists have taken leading roles in research and education (R&E) networking, participating in, and even convening, network related meetings and research programs with the broader networking community worldwide. This has led to HEP benefiting from excellent global networking capabilities for little to no direct cost. However, as other science domains ramp-up their need for similar networking it becomes less clear that this situation will continue unchanged. What this means for ATLAS in particular needs to be understood. ATLAS has evolved its computing model since the LHC started based upon its experience with using globally distributed resources. The most significant theme of those changes has been increased reliance upon, and use of, its networks....

The ATLAS Production System Evolution

CERN Document Server

Borodin, Mikhail; The ATLAS collaboration

2017-01-01

The second generation of the ATLAS Production System called ProdSys2 is a distributed workload manager that runs daily hundreds of thousands of jobs, from dozens of different ATLAS-specific workflows, across more than a hundred heterogeneous sites. It achieves high utilization by combining dynamic job definition based upon many criteria, such as input and output size, memory requirements and CPU consumption, with manageable scheduling policies and by supporting different kinds of computational resources, such as GRID, clouds, supercomputers and volunteer computers. The system dynamically assigns a group of jobs (task) to a group of geographically distributed computing resources. Dynamic assignment and resource utilization is one of the major features of the system. The Production System has a sophisticated job fault recovery mechanism, which efficiently allows running multi-terabyte tasks without human intervention. We have implemented new features which allow automatic task submission and chaining of differe...

IT Infrastructure Design and Implementation Considerations for the ATLAS TDAQ System

CERN Document Server

Dobson, M; The ATLAS collaboration; Caramarcu, C; Dumitru, I; Valsan, L; Darlea, G L; Bujor, F; Bogdanchikov, A G; Korol, A A; Zaytsev, A S; Ballestrero, S

2013-01-01

This paper gives a thorough overview of the ATLAS TDAQ SysAdmin group activities which deals with administration of the TDAQ computing environment supporting Front End detector hardware, Data Flow, Event Filter and other subsystems of the ATLAS detector operating on the LHC accelerator at CERN. The current installation consists of approximately 1500 netbooted nodes managed by more than 60 dedicated servers, a high performance centralized storage system, about 50 multi-screen user interface systems installed in the control rooms and various hardware and critical service monitoring machines. In the final configuration, the online computer farm will be capable of hosting tens of thousands applications running simultaneously. The ATLAS TDAQ computing environment is now serving more than 3000 users subdivided into approximately 300 categories in correspondence with their roles in the system. The access and role management system is custom built on top of an LDAP schema. The engineering infrastructure of the ATLAS ...

ATLAS copies its first PetaByte out of CERN

CERN Multimedia

M. Branco; P. Salgado; L. Goossens; A. Nairz

2006-01-01

On 6th August ATLAS reached a major milestone for its Distributed Data Management project - copying its first PetaByte (1015 Bytes) of data out from CERN to computing centers around the world. This achievement is part of the so-called 'Tier-0 exercise' running since 19th June, where simulated fake data is used to exercise the expected data flow within the CERN computing centre and out over the Grid to the Tier-1 computing centers as would happen during the real data taking. The expected rate of data output from CERN when the detector is running at full trigger rate is 780 MB/s shared among 10 external Tier-1 sites(*), amounting to around 8 PetaBytes per year. The idea of the exercise was to try to reach this data rate and sustain it for as long as possible. The exercise was run as part of the LCG's Service Challenges and allowed ATLAS to test successfully the integration of ATLAS software with the LCG middleware services that are used for low level cataloging and the actual data movement. When ATLAS is produ...

Performance and impact of dynamic data placement in ATLAS

CERN Document Server

Maier, Thomas; The ATLAS collaboration

2018-01-01

For high-throughput computing the efficient use of distributed computing resources relies on an evenly distributed workload, which in turn requires wide availability of input data that is used in physics analysis. In ATLAS, the dynamic data placement agent C3PO was implemented in the ATLAS distributed data management system Rucio which identifies popular data and creates additional, transient replicas to make data more widely and more reliably available. This contribution presents studies on the performance of C3PO and the impact it has on throughput rates of distributed computing in ATLAS. This includes analysis of the placement algorithm selection behaviour regarding the data considered for replication and destination storage elements, usage after the placement decision of the chosen datasets in general and the newly created copies in particular, and the impact on metrics like job waiting times, task completion times and failure rates of tasks.

Visits to Tier-1 Computing Centres

CERN Multimedia

Dario Barberis

At the beginning of 2007 it became clear that an enhanced level of communication is needed between the ATLAS computing organisation and the Tier-1 centres. Most usual meetings are ATLAS-centric and cannot address the issues of each Tier-1; therefore we decided to organise a series of visits to the Tier-1 centres and focus on site issues. For us, ATLAS computing management, it is most useful to realize how each Tier-1 centre is organised, and its relation to the associated Tier-2s; indeed their presence at these visits is also very useful. We hope it is also useful for sites... at least, we are told so! The usual participation includes, from the ATLAS side: computing management, operations, data placement, resources, accounting and database deployment coordinators; and from the Tier-1 side: computer centre management, system managers, Grid infrastructure people, network, storage and database experts, local ATLAS liaison people and representatives of the associated Tier-2s. Visiting Tier-1 centres (1-4). ...

Multilevel Workflow System in the ATLAS Experiment

International Nuclear Information System (INIS)

Borodin, M; De, K; Navarro, J Garcia; Golubkov, D; Klimentov, A; Maeno, T; Vaniachine, A

2015-01-01

The ATLAS experiment is scaling up Big Data processing for the next LHC run using a multilevel workflow system comprised of many layers. In Big Data processing ATLAS deals with datasets, not individual files. Similarly a task (comprised of many jobs) has become a unit of the ATLAS workflow in distributed computing, with about 0.8M tasks processed per year. In order to manage the diversity of LHC physics (exceeding 35K physics samples per year), the individual data processing tasks are organized into workflows. For example, the Monte Carlo workflow is composed of many steps: generate or configure hard-processes, hadronize signal and minimum-bias (pileup) events, simulate energy deposition in the ATLAS detector, digitize electronics response, simulate triggers, reconstruct data, convert the reconstructed data into ROOT ntuples for physics analysis, etc. Outputs are merged and/or filtered as necessary to optimize the chain. The bi-level workflow manager - ProdSys2 - generates actual workflow tasks and their jobs are executed across more than a hundred distributed computing sites by PanDA - the ATLAS job-level workload management system. On the outer level, the Database Engine for Tasks (DEfT) empowers production managers with templated workflow definitions. On the next level, the Job Execution and Definition Interface (JEDI) is integrated with PanDA to provide dynamic job definition tailored to the sites capabilities. We report on scaling up the production system to accommodate a growing number of requirements from main ATLAS areas: Trigger, Physics and Data Preparation. (paper)

The Architecture and Administration of the ATLAS Online Computing System

CERN Document Server

Dobson, M; Ertorer, E; Garitaonandia, H; Leahu, L; Leahu, M; Malciu, I M; Panikashvili, E; Topurov, A; Ünel, G; Computing In High Energy and Nuclear Physics

2006-01-01

The needs of ATLAS experiment at the upcoming LHC accelerator, CERN, in terms of data transmission rates and processing power require a large cluster of computers (of the order of thousands) administrated and exploited in a coherent and optimal manner. Requirements like stability, robustness and fast recovery in case of failure impose a server-client system architecture with servers distributed in a tree like structure and clients booted from the network. For security reasons, the system should be accessible only through an application gateway and, also to ensure the autonomy of the system, the network services should be provided internally by dedicated machines in synchronization with CERN IT department's central services. The paper describes a small scale implementation of the system architecture that fits the given requirements and constraints. Emphasis will be put on the mechanisms and tools used to net boot the clients via the "Boot With Me" project and to synchronize information within the cluster via t...

High-performance scalable Information Service for the ATLAS experiment

CERN Document Server

Kolos, S; The ATLAS collaboration; Hauser, R

2012-01-01

The ATLAS experiment is being operated by highly distributed computing system which is constantly producing a lot of status information which is used to monitor the experiment operational conditions as well as to access the quality of the physics data being taken. For example the ATLAS High Level Trigger(HLT) algorithms are executed on the online computing farm consisting from about 1500 nodes. Each HLT algorithm is producing few thousands histograms, which have to be integrated over the whole farm and carefully analyzed in order to properly tune the event rejection. In order to handle such non-physics data the Information Service (IS) facility has been developed in the scope of the ATLAS TDAQ project. The IS provides high-performance scalable solution for information exchange in distributed environment. In the course of an ATLAS data taking session the IS handles about hundred gigabytes of information which is being constantly updated with the update interval varying from a second to few tens of seconds. IS ...

Production experience with the ATLAS Event Service

Science.gov (United States)

Benjamin, D.; Calafiura, P.; Childers, T.; De, K.; Guan, W.; Maeno, T.; Nilsson, P.; Tsulaia, V.; Van Gemmeren, P.; Wenaus, T.; ATLAS Collaboration

2017-10-01

The ATLAS Event Service (AES) has been designed and implemented for efficient running of ATLAS production workflows on a variety of computing platforms, ranging from conventional Grid sites to opportunistic, often short-lived resources, such as spot market commercial clouds, supercomputers and volunteer computing. The Event Service architecture allows real time delivery of fine grained workloads to running payload applications which process dispatched events or event ranges and immediately stream the outputs to highly scalable Object Stores. Thanks to its agile and flexible architecture the AES is currently being used by grid sites for assigning low priority workloads to otherwise idle computing resources; similarly harvesting HPC resources in an efficient back-fill mode; and massively scaling out to the 50-100k concurrent core level on the Amazon spot market to efficiently utilize those transient resources for peak production needs. Platform ports in development include ATLAS@Home (BOINC) and the Google Compute Engine, and a growing number of HPC platforms. After briefly reviewing the concept and the architecture of the Event Service, we will report the status and experience gained in AES commissioning and production operations on supercomputers, and our plans for extending ES application beyond Geant4 simulation to other workflows, such as reconstruction and data analysis.

MRI-based treatment planning with pseudo CT generated through atlas registration.

Science.gov (United States)

Uh, Jinsoo; Merchant, Thomas E; Li, Yimei; Li, Xingyu; Hua, Chiaho

2014-05-01

To evaluate the feasibility and accuracy of magnetic resonance imaging (MRI)-based treatment planning using pseudo CTs generated through atlas registration. A pseudo CT, providing electron density information for dose calculation, was generated by deforming atlas CT images previously acquired on other patients. The authors tested 4 schemes of synthesizing a pseudo CT from single or multiple deformed atlas images: use of a single arbitrarily selected atlas, arithmetic mean process using 6 atlases, and pattern recognition with Gaussian process (PRGP) using 6 or 12 atlases. The required deformation for atlas CT images was derived from a nonlinear registration of conjugated atlas MR images to that of the patient of interest. The contrasts of atlas MR images were adjusted by histogram matching to reduce the effect of different sets of acquisition parameters. For comparison, the authors also tested a simple scheme assigning the Hounsfield unit of water to the entire patient volume. All pseudo CT generating schemes were applied to 14 patients with common pediatric brain tumors. The image similarity of real patient-specific CT and pseudo CTs constructed by different schemes was compared. Differences in computation times were also calculated. The real CT in the treatment planning system was replaced with the pseudo CT, and the dose distribution was recalculated to determine the difference. The atlas approach generally performed better than assigning a bulk CT number to the entire patient volume. Comparing atlas-based schemes, those using multiple atlases outperformed the single atlas scheme. For multiple atlas schemes, the pseudo CTs were similar to the real CTs (correlation coefficient, 0.787-0.819). The calculated dose distribution was in close agreement with the original dose. Nearly the entire patient volume (98.3%-98.7%) satisfied the criteria of chi-evaluation (pediatric brain tumor patients. The doses calculated from pseudo CTs agreed well with those from real CTs

ATLAS FTK a – very complex – custom super computer

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00008600; The ATLAS collaboration

2016-01-01

In the ever increasing pile-up LHC environment advanced techniques of analyzing the data are implemented in order to increase the rate of relevant physics processes with respect to background processes. The Fast TracKer (FTK) is a track finding implementation at hardware level that is designed to deliver full-scan tracks with PT above 1GeV to the ATLAS trigger system for every L1 accept (at a maximum rate of 100kHz). In order to achieve this performance a highly parallel system was designed and now it is under installation in ATLAS. In the beginning of 2016 it will provide tracks for the trigger system in a region covering the central part of the ATLAS detector, and during the year it's coverage will be extended to the full detector coverage. The system relies on matching hits coming from the silicon tracking detectors against 1 billion patterns stored in specially designed ASICS chips (Associative memory - AM06). In a first stage coarse resolution hits are matched against the patterns and the accepted hits u...

Development and Implementation of a Corriedale Ovine Brain Atlas for Use in Atlas-Based Segmentation.

Directory of Open Access Journals (Sweden)

Kishan Andre Liyanage

Full Text Available Segmentation is the process of partitioning an image into subdivisions and can be applied to medical images to isolate anatomical or pathological areas for further analysis. This process can be done manually or automated by the use of image processing computer packages. Atlas-based segmentation automates this process by the use of a pre-labelled template and a registration algorithm. We developed an ovine brain atlas that can be used as a model for neurological conditions such as Parkinson's disease and focal epilepsy. 17 female Corriedale ovine brains were imaged in-vivo in a 1.5T (low-resolution MRI scanner. 13 of the low-resolution images were combined using a template construction algorithm to form a low-resolution template. The template was labelled to form an atlas and tested by comparing manual with atlas-based segmentations against the remaining four low-resolution images. The comparisons were in the form of similarity metrics used in previous segmentation research. Dice Similarity Coefficients were utilised to determine the degree of overlap between eight independent, manual and atlas-based segmentations, with values ranging from 0 (no overlap to 1 (complete overlap. For 7 of these 8 segmented areas, we achieved a Dice Similarity Coefficient of 0.5-0.8. The amygdala was difficult to segment due to its variable location and similar intensity to surrounding tissues resulting in Dice Coefficients of 0.0-0.2. We developed a low resolution ovine brain atlas with eight clinically relevant areas labelled. This brain atlas performed comparably to prior human atlases described in the literature and to intra-observer error providing an atlas that can be used to guide further research using ovine brains as a model and is hosted online for public access.

Grid site testing for ATLAS with HammerCloud

International Nuclear Information System (INIS)

Elmsheuser, J; Hönig, F; Legger, F; LLamas, R Medrano; Sciacca, F G; Ster, D van der

2014-01-01

With the exponential growth of LHC (Large Hadron Collider) data in 2012, distributed computing has become the established way to analyze collider data. The ATLAS grid infrastructure includes more than 130 sites worldwide, ranging from large national computing centers to smaller university clusters. HammerCloud was previously introduced with the goals of enabling virtual organisations (VO) and site-administrators to run validation tests of the site and software infrastructure in an automated or on-demand manner. The HammerCloud infrastructure has been constantly improved to support the addition of new test workflows. These new workflows comprise e.g. tests of the ATLAS nightly build system, ATLAS Monte Carlo production system, XRootD federation (FAX) and new site stress test workflows. We report on the development, optimization and results of the various components in the HammerCloud framework.

Grid Site Testing for ATLAS with HammerCloud

CERN Document Server

Elmsheuser, J; The ATLAS collaboration; Legger, F; Medrano LLamas, R; Sciacca, G; van der Ster, D

2014-01-01

With the exponential growth of LHC (Large Hadron Collider) data in 2012, distributed computing has become the established way to analyze collider data. The ATLAS grid infrastructure includes more than 130 sites worldwide, ranging from large national computing centers to smaller university clusters. HammerCloud was previously introduced with the goals of enabling VO- and site-administrators to run validation tests of the site and software infrastructure in an automated or on-demand manner. The HammerCloud infrastructure has been constantly improved to support the addition of new test work-flows. These new work-flows comprise e.g. tests of the ATLAS nightly build system, ATLAS MC production system, XRootD federation FAX and new site stress test work-flows. We report on the development, optimization and results of the various components in the HammerCloud framework.

Grid Site Testing for ATLAS with HammerCloud

CERN Document Server

Elmsheuser, J; The ATLAS collaboration; Legger, F; Medrano LLamas, R; Sciacca, G; van der Ster, D

2013-01-01

With the exponential growth of LHC (Large Hadron Collider) data in 2012, distributed computing has become the established way to analyze collider data. The ATLAS grid infrastructure includes more than 130 sites worldwide, ranging from large national computing centers to smaller university clusters. HammerCloud was previously introduced with the goals of enabling VO- and site-administrators to run validation tests of the site and software infrastructure in an automated or on-demand manner. The HammerCloud infrastructure has been constantly improved to support the addition of new test work-flows. These new work-flows comprise e.g. tests of the ATLAS nightly build system, ATLAS MC production system, XRootD federation FAX and new site stress test work-flows. We report on the development, optimization and results of the various components in the HammerCloud framework.

ATLAS Grid Data Processing: system evolution and scalability

CERN Document Server

Golubkov, D; The ATLAS collaboration; Klimentov, A; Minaenko, A; Nevski, P; Vaniachine, A; Walker, R

2012-01-01

The production system for Grid Data Processing handles petascale ATLAS data reprocessing and Monte Carlo activities. The production system empowered further data processing steps on the Grid performed by dozens of ATLAS physics groups with coordinated access to computing resources worldwide, including additional resources sponsored by regional facilities. The system provides knowledge management of configuration parameters for massive data processing tasks, reproducibility of results, scalable database access, orchestrated workflow and performance monitoring, dynamic workload sharing, automated fault tolerance and petascale data integrity control. The system evolves to accommodate a growing number of users and new requirements from our contacts in ATLAS main areas: Trigger, Physics, Data Preparation and Software & Computing. To assure scalability, the next generation production system architecture development is in progress. We report on scaling up the production system for a growing number of users provi...

Study of ATLAS TRT performance with GRID and supercomputers.

CERN Document Server

Krasnopevtsev, Dimitriy; The ATLAS collaboration; Mashinistov, Ruslan; Belyaev, Nikita; Ryabinkin, Evgeny

2015-01-01

After the early success in discovering a new particle consistent with the long awaited Higgs boson, Large Hadron Collider experiments are ready for the precision measurements and further discoveries that will be made possible by much higher LHC collision rates from spring 2015. A proper understanding of the detectors performance at high occupancy conditions is important for many on-going physics analyses. The ATLAS Transition Radiation Tracker (TRT) is one of these detectors. TRT is a large straw tube tracking system that is the outermost of the three subsystems of the ATLAS Inner Detector (ID). TRT contributes significantly to the resolution for high-pT tracks in the ID providing excellent particle identification capabilities and electron-pion separation. ATLAS experiment is using Worldwide LHC Computing Grid. WLCG is a global collaboration of computer centers and provides seamless access to computing resources which include data storage capacity, processing power, sensors, visualisation tools and more. WLCG...

Study of ATLAS TRT performance with GRID and supercomputers.

CERN Document Server

Krasnopevtsev, Dimitriy; The ATLAS collaboration; Belyaev, Nikita; Mashinistov, Ruslan; Ryabinkin, Evgeny

2015-01-01

After the early success in discovering a new particle consistent with the long awaited Higgs boson, Large Hadron Collider experiments are ready for the precision measurements and further discoveries that will be made possible by much higher LHC collision rates from spring 2015. A proper understanding of the detectors performance at highoccupancy conditions is important for many on-going physics analyses. The ATLAS Transition Radiation Tracker (TRT) is one of these detectors. TRT is a large straw tube tracking system that is the outermost of the three subsystems of the ATLAS Inner Detector (ID). TRT contributes significantly to the resolution for high-pT tracks in the ID providing excellent particle identification capabilities and electron-pion separation. ATLAS experiment is using Worldwide LHC Computing Grid. WLCG is a global collaboration of computer centers and provides seamless access to computing resources which include data storage capacity, processing power, sensors, visualization tools and more. WLCG ...

Bone age assessment in Hispanic children: digital hand atlas compared with the Greulich and Pyle (G&P) atlas

Science.gov (United States)

Fernandez, James Reza; Zhang, Aifeng; Vachon, Linda; Tsao, Sinchai

2008-03-01

Bone age assessment is most commonly performed with the use of the Greulich and Pyle (G&P) book atlas, which was developed in the 1950s. The population of theUnited States is not as homogenous as the Caucasian population in the Greulich and Pyle in the 1950s, especially in the Los Angeles, California area. A digital hand atlas (DHA) based on 1,390 hand images of children of different racial backgrounds (Caucasian, African American, Hispanic, and Asian) aged 0-18 years was collected from Children's Hospital Los Angeles. Statistical analysis discovered significant discrepancies exist between Hispanic and the G&P atlas standard. To validate the usage of DHA as a clinical standard, diagnostic radiologists performed reads on Hispanic pediatric hand and wrist computed radiography images using either the G&P pediatric radiographic atlas or the Children's Hospital Los Angeles Digital Hand Atlas (DHA) as reference. The order in which the atlas is used (G&P followed by DHA or vice versa) for each image was prepared before actual reading begins. Statistical analysis of the results was then performed to determine if a discrepancy exists between the two readings.

Iberian ATLAS Cloud response during the first LHC collisions

CERN Document Server

Villaplana, M; The ATLAS collaboration; Borges, G; Borrego, C; Carvalho, J; David, M; Espinal, X; Fernández, A; Gomes, J; González de la Hoz, S; Kaci, M; Lamas, A; Nadal, J; Oliveira, M; Oliver, E; Osuna, C; Pacheco, A; Pardo, JJ; del Peso, J; Salt, J; Sánchez, J; Wolters, H

2011-01-01

The computing model of the ATLAS experiment at the LHC (Large Hadron Collider) is based on a tiered hierarchy that ranges from Tier0 (CERN) down to end-user's own resources (Tier3). According to the same computing model, the role of the Tier2s is to provide computing resources for event simulation processing and distributed data analysis. Tier3 centers, on the other hand, are the responsibility of individual institutions to define, fund, deploy and support. In this contribution we report on the operations of the ATLAS Iberian Cloud centers facing data taking and we describe some of the Tier3 facilities currently deployed at the Cloud.

ATLAS FTK a - very complex - custom super computer

International Nuclear Information System (INIS)

Kimura, N

2016-01-01

In the LHC environment for high interaction pile-up, advanced techniques of analysing the data in real time are required in order to maximize the rate of physics processes of interest with respect to background processes. The Fast TracKer (FTK) is a track finding implementation at the hardware level that is designed to deliver full-scan tracks with p T above 1 GeV to the ATLAS trigger system for events passing the Level-1 accept (at a maximum rate of 100 kHz). In order to achieve this performance, a highly parallel system was designed and currently it is being commissioned within in ATLAS. Starting in 2016 it will provide tracks for the trigger system in a region covering the central part of the ATLAS detector, and will be extended to the full detector coverage. The system relies on matching hits coming from the silicon tracking detectors against one billion patterns stored in custom ASIC chips (Associative memory chip - AM06). In a first stage, coarse resolution hits are matched against the patterns and the accepted hits undergo track fitting implemented in FPGAs. Tracks with p T > 1GeV are delivered to the High Level Trigger within about 100 ps. Resolution of the tracks coming from FTK is close to the offline tracking and it will allow for reliable detection of primary and secondary vertexes at trigger level and improved trigger performance for b-jets and tau leptons. This contribution will give an overview of the FTK system and present the status of commissioning of the system. Additionally, the expected FTK performance will be briefly described. (paper)

ATLAS Job Transforms

CERN Document Server

Stewart, G A; The ATLAS collaboration; Maddocks, H J; Harenberg, T; Sandhoff, M; Sarrazin, B

2013-01-01

The need to run complex workflows for a high energy physics experiment such as ATLAS has always been present. However, as computing resources have become even more constrained, compared to the wealth of data generated by the LHC, the need to use resources efficiently and manage complex workflows within a single grid job have increased. In ATLAS, a new Job Transform framework has been developed that we describe in this paper. This framework manages the multiple execution steps needed to `transform' one data type into another (e.g., RAW data to ESD to AOD to final ntuple) and also provides a consistent interface for the ATLAS production system. The new framework uses a data driven workflow definition which is both easy to manage and powerful. After a transform is defined, jobs are expressed simply by specifying the input data and the desired output data. The transform infrastructure then executes only the necessary substeps to produce the final data products. The global execution cost of running the job is mini...

ATLAS Job Transforms

CERN Document Server

Stewart, G A; The ATLAS collaboration; Maddocks, H J; Harenberg, T; Sandhoff, M; Sarrazin, B

2013-01-01

The need to run complex workflows for a high energy physics experiment such as ATLAS has always been present. However, as computing resources have become even more constrained, compared to the wealth of data generated by the LHC, the need to use resources efficiently and manage complex workflows within a single grid job have increased. In ATLAS, a new Job Transform framework has been developed that we describe in this paper. This framework manages the multiple execution steps needed to 'transform' one data type into another (e.g., RAW data to ESD to AOD to final ntuple) and also provides a consistent interface for the ATLAS production system. The new framework uses a data driven workflow definition which is both easy to manage and powerful. After a transform is defined, jobs are expressed simply by specifying the input data and the desired output data. The transform infrastructure then executes only the necessary substeps to produce the final data products. The global execution cost of running the job is mini...

Pseudospread of the atlas: false sign of Jefferson fracture in young children

International Nuclear Information System (INIS)

Suss, R.A.; Zimmerman, R.D.; Leeds, N.E.

1983-01-01

Jefferson fractures are rare prior to teen-age. Three young children examined after trauma exhibited the characteristic spread appearance of the atlas, but fractures were excluded radiographically and clinically. A retrospective study demonstrated a similar appearance, termed pseudospread, in most children aged 3 months to 4 years, including over 90% during the second year. Pseudospread results from a discrepancy between the neural growth pattern of the atlas and the somatic pattern of the axis. An atlas spread index is defined and a normal range presented. When an atlas fracture is suggested by apparent lateral spread of the lateral atlas masses, computed tomography is useful to demonstrate an intact atlas ring

Illustrated computer tomography

International Nuclear Information System (INIS)

Takahashi, S.

1983-01-01

This book provides the following information: basic aspects of computed tomography; atlas of computed tomography of the normal adult; clinical application of computed tomography; and radiotherapy planning and computed tomography

A Lego version of ATLAS

CERN Multimedia

Laëtitia Pedroso

2010-01-01

There's nothing very unusual about a small child making simple objects out of Lego. But wouldn't you be surprised to learn that one six-year old has just made a life-like model of the ATLAS detector?   Bastian with his Lego ATLAS detector. © Photo provided by Kai Nicklas, Bastian's father. It all began a month ago when the boy's father was watching a video about the construction of the ATLAS detector on the Internet. He hadn't noticed that his son was watching it over his shoulder. The small boy was fascinated by what he was seeing on the computer screen and his first reaction was to exclaim: "Wow! That's a terrific machine! I think the people who built it must be really clever." The detector must have really fired his imagination because, after asking his father a few questions, he decided to make a Lego model of it. Look at the photo and you will see how closely the model he produced resembles the actual ATLAS detector. Is the little boy in question, Bastia...

Experience running a distributed Tier-2 in Spain for the ATLAS experiment

International Nuclear Information System (INIS)

March, L; Hoz, S Gonzales de la; Kaci, M; Fassi, F; Fernandez, A; Lamas, A; Salt, J; Sanchez, J; Peso, J del; Fernandez, P; Munoz, L; Pardo, J; Espinal, X; Garitaonandia, H; Mir, M L; Nadal, J; Pacheco, A; Shuskov, S

2008-01-01

The main role of the Tier-2s is to provide computing resources for production of physics simulated events and distributed data analysis. The Spanish ATLAS Tier-2 is geographically distributed among three HEP institutes: IFAE (Barcelona), IFIC (Valencia) and UAM (Madrid). Currently it has a computing power of 430 kSI2K CPU, a disk storage capacity of 87 TB and a network bandwidth, connecting the three sites and the nearest Tier-1 (PIC), of 1 Gb/s. These resources will be increased according to the ATLAS Computing Model with time in parallel to those of all ATLAS Tier-2s. Since 2002, it has been participating into the different Data Challenge exercises. Currently, it is achieving around 1.5% of the whole ATLAS collaboration production in the framework of the Computing System Commissioning exercise. A distributed data management is also arising as an important issue in the daily activities of the Tier-2. The distribution in three sites has shown to be useful due to an increasing service redundancy, a faster solution of problems, the share of computing expertise and know-how. Experience gained running the distributed Tier-2 in order to be ready at the LHC start-up will be presented

Illustrative Example of Distributed Analysis in ATLAS Spanish Tier-2 and Tier-3 centers

CERN Document Server

Oliver, E; The ATLAS collaboration; González de la Hoz, S; Kaci, M; Lamas, A; Salt, J; Sánchez, J; Villaplana, M

2011-01-01

Data taking in ATLAS has been going on for more than one year. The necessity of a computing infrastructure for data storage, access for thousands of users and process of hundreds of million of events has been confirmed in this period. Fortunately, this task has been managed by the GRID infrastructure and the manpower that also has been developing specific GRID tools for the ATLAS community. An example of a physics analysis, searches for the decay of a heavy resonance into a ttbar pair, using this infrastructure is shown. Concretely using the ATLAS Spanish Tier-2 and the IFIC Tier-3. In this moment, the ATLAS Distributed Computing group is working to improve the connectivity among centers in order to be ready for the foreseen increase on the ATLAS activity in the next years.

ATLAS TDAQ application gateway upgrade during LS1

CERN Document Server

KOROL, A; The ATLAS collaboration; BOGDANCHIKOV, A; BRASOLIN, F; CONTESCU, A C; DUBROV, S; HAFEEZ, M; LEE, C J; SCANNICCHIO, D A; TWOMEY, M; VORONKOV, A; ZAYTSEV, A

2014-01-01

The ATLAS Gateway service is implemented with a set of dedicated computer nodes to provide a fine-grained access control between CERN General Public Network (GPN) and ATLAS Technical Control Network (ATCN). ATCN connects the ATLAS online farm used for ATLAS Operations and data taking, including the ATLAS TDAQ (Trigger and Data Aquisition) and DCS (Detector Control System) nodes. In particular, it provides restricted access to the web services (proxy), general login sessions (via SSH and RDP protocols), NAT and mail relay from ATCN. At the Operating System level the implementation is based on virtualization technologies. Here we report on the Gateway upgrade during Long Shutdown 1 (LS1) period: it includes the transition to the last production release of the CERN Linux distribution (SLC6), the migration to the centralized configuration management system (based on Puppet) and the redesign of the internal system architecture.

The Japan Lung Cancer Society–Japanese Society for Radiation Oncology consensus-based computed tomographic atlas for defining regional lymph node stations in radiotherapy for lung cancer

International Nuclear Information System (INIS)

Itazawa, Tomoko; Tamaki, Yukihisa; Komiyama, Takafumi; Nishimura, Yasumasa; Nakayama, Yuko; Ito, Hiroyuki; Ohde, Yasuhisa; Kusumoto, Masahiko; Sakai, Shuji; Suzuki, Kenji; Watanabe, Hirokazu; Asamura, Hisao

2017-01-01

The purpose of this study was to develop a consensus-based computed tomographic (CT) atlas that defines lymph node stations in radiotherapy for lung cancer based on the lymph node map of the International Association for the Study of Lung Cancer (IASLC). A project group in the Japanese Radiation Oncology Study Group (JROSG) initially prepared a draft of the atlas in which lymph node Stations 1–11 were illustrated on axial CT images. Subsequently, a joint committee of the Japan Lung Cancer Society (JLCS) and the Japanese Society for Radiation Oncology (JASTRO) was formulated to revise this draft. The committee consisted of four radiation oncologists, four thoracic surgeons and three thoracic radiologists. The draft prepared by the JROSG project group was intensively reviewed and discussed at four meetings of the committee over several months. Finally, we proposed definitions for the regional lymph node stations and the consensus-based CT atlas. This atlas was approved by the Board of Directors of JLCS and JASTRO. This resulted in the first official CT atlas for defining regional lymph node stations in radiotherapy for lung cancer authorized by the JLCS and JASTRO. In conclusion, the JLCS–JASTRO consensus-based CT atlas, which conforms to the IASLC lymph node map, was established.

Event visualization in ATLAS

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00211497; The ATLAS collaboration; Boudreau, Joseph; Konstantinidis, Nikolaos; Martyniuk, Alex; Moyse, Edward; Thomas, Juergen; Waugh, Ben; Yallup, David

2017-01-01

At the beginning, HEP experiments made use of photographical images both to record and store experimental data and to illustrate their findings. Then the experiments evolved and needed to find ways to visualize their data. With the availability of computer graphics, software packages to display event data and the detector geometry started to be developed. Here, an overview of the usage of event display tools in HEP is presented. Then the case of the ATLAS experiment is considered in more detail and two widely used event display packages are presented, Atlantis and VP1, focusing on the software technologies they employ, as well as their strengths, differences and their usage in the experiment: from physics analysis to detector development, and from online monitoring to outreach and communication. Towards the end, the other ATLAS visualization tools will be briefly presented as well. Future development plans and improvements in the ATLAS event display packages will also be discussed.

ATLAS Distributed Analysis Tools

CERN Document Server

Gonzalez de la Hoz, Santiago; Liko, Dietrich

2008-01-01

The ATLAS production system has been successfully used to run production of simulation data at an unprecedented scale. Up to 10000 jobs were processed in one day. The experiences obtained operating the system on several grid flavours was essential to perform a user analysis using grid resources. First tests of the distributed analysis system were then performed. In the preparation phase data was registered in the LHC File Catalog (LFC) and replicated in external sites. For the main test, few resources were used. All these tests are only a first step towards the validation of the computing model. The ATLAS management computing board decided to integrate the collaboration efforts in distributed analysis in only one project, GANGA. The goal is to test the reconstruction and analysis software in a large scale Data production using Grid flavors in several sites. GANGA allows trivial switching between running test jobs on a local batch system and running large-scale analyses on the Grid; it provides job splitting a...

Fast and robust multi-atlas segmentation of brain magnetic resonance images

DEFF Research Database (Denmark)

Lötjönen, Jyrki Mp; Wolz, Robin; Koikkalainen, Juha R

2010-01-01

of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity...

Integration of Titan supercomputer at OLCF with ATLAS Production System

Science.gov (United States)

Barreiro Megino, F.; De, K.; Jha, S.; Klimentov, A.; Maeno, T.; Nilsson, P.; Oleynik, D.; Padolski, S.; Panitkin, S.; Wells, J.; Wenaus, T.; ATLAS Collaboration

2017-10-01

The PanDA (Production and Distributed Analysis) workload management system was developed to meet the scale and complexity of distributed computing for the ATLAS experiment. PanDA managed resources are distributed worldwide, on hundreds of computing sites, with thousands of physicists accessing hundreds of Petabytes of data and the rate of data processing already exceeds Exabyte per year. While PanDA currently uses more than 200,000 cores at well over 100 Grid sites, future LHC data taking runs will require more resources than Grid computing can possibly provide. Additional computing and storage resources are required. Therefore ATLAS is engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. In this paper we will describe a project aimed at integration of ATLAS Production System with Titan supercomputer at Oak Ridge Leadership Computing Facility (OLCF). Current approach utilizes modified PanDA Pilot framework for job submission to Titan’s batch queues and local data management, with lightweight MPI wrappers to run single node workloads in parallel on Titan’s multi-core worker nodes. It provides for running of standard ATLAS production jobs on unused resources (backfill) on Titan. The system already allowed ATLAS to collect on Titan millions of core-hours per month, execute hundreds of thousands jobs, while simultaneously improving Titans utilization efficiency. We will discuss the details of the implementation, current experience with running the system, as well as future plans aimed at improvements in scalability and efficiency. Notice: This manuscript has been authored, by employees of Brookhaven Science Associates, LLC under Contract No. DE-AC02-98CH10886 with the U.S. Department of Energy. The publisher by accepting the manuscript for publication acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to

ATLAS data sonification : a new interface for musical expression

CERN Document Server

Hill, Ewan; The ATLAS collaboration

2016-01-01

The goal of this project is to transform ATLAS data into sound and explore how ATLAS audio can be a source of inspiration and education for musicians and for the general public. Real-time ATLAS data is sonified and streamed as music on a dedicated website. Listeners may be motivated to learn more about the ATLAS experiment and composers have the opportunity to explore the physics in the collision data through a new medium. The ATLAS collaboration has shared its expertise and access to the live data stream from which the live event displays are generated. This poster tells the story of a long journey from the hallways of CERN where the project collaboration began to the halls of the Montreux Jazz Festival where harmonies were performed. The mapping of the data to sound will be outlined and interactions with musicians and contributions to conferences dedicated to human-computer interaction will also be discussed. It is a partnership between the ATLAS collaboration and the MIT multimedia lab.

Concepts and Plans towards fast large scale Monte Carlo production for the ATLAS Experiment

Science.gov (United States)

Ritsch, E.; Atlas Collaboration

2014-06-01

The huge success of the physics program of the ATLAS experiment at the Large Hadron Collider (LHC) during Run 1 relies upon a great number of simulated Monte Carlo events. This Monte Carlo production takes the biggest part of the computing resources being in use by ATLAS as of now. In this document we describe the plans to overcome the computing resource limitations for large scale Monte Carlo production in the ATLAS Experiment for Run 2, and beyond. A number of fast detector simulation, digitization and reconstruction techniques are being discussed, based upon a new flexible detector simulation framework. To optimally benefit from these developments, a redesigned ATLAS MC production chain is presented at the end of this document.

CASTp 3.0: computed atlas of surface topography of proteins.

Science.gov (United States)

Tian, Wei; Chen, Chang; Lei, Xue; Zhao, Jieling; Liang, Jie

2018-06-01

Geometric and topological properties of protein structures, including surface pockets, interior cavities and cross channels, are of fundamental importance for proteins to carry out their functions. Computed Atlas of Surface Topography of proteins (CASTp) is a web server that provides online services for locating, delineating and measuring these geometric and topological properties of protein structures. It has been widely used since its inception in 2003. In this article, we present the latest version of the web server, CASTp 3.0. CASTp 3.0 continues to provide reliable and comprehensive identifications and quantifications of protein topography. In addition, it now provides: (i) imprints of the negative volumes of pockets, cavities and channels, (ii) topographic features of biological assemblies in the Protein Data Bank, (iii) improved visualization of protein structures and pockets, and (iv) more intuitive structural and annotated information, including information of secondary structure, functional sites, variant sites and other annotations of protein residues. The CASTp 3.0 web server is freely accessible at http://sts.bioe.uic.edu/castp/.

Atlas selection for hippocampus segmentation: Relevance evaluation of three meta-information parameters.

Science.gov (United States)

Dill, Vanderson; Klein, Pedro Costa; Franco, Alexandre Rosa; Pinho, Márcio Sarroglia

2018-04-01

Current state-of-the-art methods for whole and subfield hippocampus segmentation use pre-segmented templates, also known as atlases, in the pre-processing stages. Typically, the input image is registered to the template, which provides prior information for the segmentation process. Using a single standard atlas increases the difficulty in dealing with individuals who have a brain anatomy that is morphologically different from the atlas, especially in older brains. To increase the segmentation precision in these cases, without any manual intervention, multiple atlases can be used. However, registration to many templates leads to a high computational cost. Researchers have proposed to use an atlas pre-selection technique based on meta-information followed by the selection of an atlas based on image similarity. Unfortunately, this method also presents a high computational cost due to the image-similarity process. Thus, it is desirable to pre-select a smaller number of atlases as long as this does not impact on the segmentation quality. To pick out an atlas that provides the best registration, we evaluate the use of three meta-information parameters (medical condition, age range, and gender) to choose the atlas. In this work, 24 atlases were defined and each is based on the combination of the three meta-information parameters. These atlases were used to segment 352 vol from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Hippocampus segmentation with each of these atlases was evaluated and compared to reference segmentations of the hippocampus, which are available from ADNI. The use of atlas selection by meta-information led to a significant gain in the Dice similarity coefficient, which reached 0.68 ± 0.11, compared to 0.62 ± 0.12 when using only the standard MNI152 atlas. Statistical analysis showed that the three meta-information parameters provided a significant improvement in the segmentation accuracy. Copyright © 2018 Elsevier Ltd

ATLAS Tier-3 within IFIC-Valencia analysis facility

CERN Document Server

Villaplana, M; The ATLAS collaboration; Fernández, A; Salt, J; Lamas, A; Fassi, F; Kaci, M; Oliver, E; Sánchez, J; Sánchez-Martínez, V

2012-01-01

The ATLAS Tier-3 at IFIC-Valencia is attached to a Tier-2 that has 50% of the Spanish Federated Tier-2 resources. In its design, the Tier-3 includes a GRID-aware part that shares some of the features of IFIC Tier-2 such as using Lustre as a file system. ATLAS users, 70% of IFIC users, also have the possibility of analysing data with a PROOF farm and storing them locally. In this contribution we discuss the design of the analysis facility as well as the monitoring tools we use to control and improve its performance. We also comment on how the recent changes in the ATLAS computing GRID model affect IFIC. Finally, how this complex system can coexist with the other scientific applications running at IFIC (non-ATLAS users) is presented.

Exploiting opportunistic resources for ATLAS with ARC CE and the Event Service

Science.gov (United States)

Cameron, D.; Filipčič, A.; Guan, W.; Tsulaia, V.; Walker, R.; Wenaus, T.; ATLAS Collaboration

2017-10-01

With ever-greater computing needs and fixed budgets, big scientific experiments are turning to opportunistic resources as a means to add much-needed extra computing power. These resources can be very different in design from those that comprise the Grid computing of most experiments, therefore exploiting them requires a change in strategy for the experiment. They may be highly restrictive in what can be run or in connections to the outside world, or tolerate opportunistic usage only on condition that tasks may be terminated without warning. The Advanced Resource Connector Computing Element (ARC CE) with its nonintrusive architecture is designed to integrate resources such as High Performance Computing (HPC) systems into a computing Grid. The ATLAS experiment developed the ATLAS Event Service (AES) primarily to address the issue of jobs that can be terminated at any point when opportunistic computing capacity is needed by someone else. This paper describes the integration of these two systems in order to exploit opportunistic resources for ATLAS in a restrictive environment. In addition to the technical details, results from deployment of this solution in the SuperMUC HPC centre in Munich are shown.

The ATLAS Simulation Infrastructure

CERN Document Server

Aad, G.; Abdallah, J.; Abdelalim, A.A.; Abdesselam, A.; Abdinov, O.; Abi, B.; Abolins, M.; Abramowicz, H.; Abreu, H.; Acharya, B.S.; Adams, D.L.; Addy, T.N.; Adelman, J.; Adorisio, C.; Adragna, P.; Adye, T.; Aefsky, S.; Aguilar-Saavedra, J.A.; Aharrouche, M.; Ahlen, S.P.; Ahles, F.; Ahmad, A.; Ahmed, H.; Ahsan, M.; Aielli, G.; Akdogan, T.; Akesson, T.P.A.; Akimoto, G.; Akimov, A.V.; Aktas, A.; Alam, M.S.; Alam, M.A.; Albrand, S.; Aleksa, M.; Aleksandrov, I.N.; Alexa, C.; Alexander, G.; Alexandre, G.; Alexopoulos, T.; Alhroob, M.; Aliev, M.; Alimonti, G.; Alison, J.; Aliyev, M.; Allport, P.P.; Allwood-Spiers, S.E.; Almond, J.; Aloisio, A.; Alon, R.; Alonso, A.; Alviggi, M.G.; Amako, K.; Amelung, C.; Amorim, A.; Amoros, G.; Amram, N.; Anastopoulos, C.; Andeen, T.; Anders, C.F.; Anderson, K.J.; Andreazza, A.; Andrei, V.; Anduaga, X.S.; Angerami, A.; Anghinolfi, F.; Anjos, N.; Annovi, A.; Antonaki, A.; Antonelli, M.; Antonelli, S.; Antos, J.; Antunovic, B.; Anulli, F.; Aoun, S.; Arabidze, G.; Aracena, I.; Arai, Y.; Arce, A.T.H.; Archambault, J.P.; Arfaoui, S.; Arguin, J-F.; Argyropoulos, T.; Arik, M.; Armbruster, A.J.; Arnaez, O.; Arnault, C.; Artamonov, A.; Arutinov, D.; Asai, M.; Asai, S.; Asfandiyarov, R.; Ask, S.; Asman, B.; Asner, D.; Asquith, L.; Assamagan, K.; Astbury, A.; Astvatsatourov, A.; Atoian, G.; Auerbach, B.; Augsten, K.; Aurousseau, M.; Austin, N.; Avolio, G.; Avramidou, R.; Axen, D.; Ay, C.; Azuelos, G.; Azuma, Y.; Baak, M.A.; Bach, A.M.; Bachacou, H.; Bachas, K.; Backes, M.; Badescu, E.; Bagnaia, P.; Bai, Y.; Bain, T.; Baines, J.T.; Baker, O.K.; Baker, M.D.; Baker, S; Baltasar Dos Santos Pedrosa, F.; Banas, E.; Banerjee, P.; Banerjee, S.; Banfi, D.; Bangert, A.; Bansal, V.; Baranov, S.P.; Baranov, S.; Barashkou, A.; Barber, T.; Barberio, E.L.; Barberis, D.; Barbero, M.; Bardin, D.Y.; Barillari, T.; Barisonzi, M.; Barklow, T.; Barlow, N.; Barnett, B.M.; Barnett, R.M.; Baroncelli, A.; Barr, A.J.; Barreiro, F.; Barreiro Guimaraes da Costa, J.; Barrillon, P.; Bartoldus, R.; Bartsch, D.; Bates, R.L.; Batkova, L.; Batley, J.R.; Battaglia, A.; Battistin, M.; Bauer, F.; Bawa, H.S.; Bazalova, M.; Beare, B.; Beau, T.; Beauchemin, P.H.; Beccherle, R.; Becerici, N.; Bechtle, P.; Beck, G.A.; Beck, H.P.; Beckingham, M.; Becks, K.H.; Beddall, A.J.; Beddall, A.; Bednyakov, V.A.; Bee, C.; Begel, M.; Behar Harpaz, S.; Behera, P.K.; Beimforde, M.; Belanger-Champagne, C.; Bell, P.J.; Bell, W.H.; Bella, G.; Bellagamba, L.; Bellina, F.; Bellomo, M.; Belloni, A.; Belotskiy, K.; Beltramello, O.; Ben Ami, S.; Benary, O.; Benchekroun, D.; Bendel, M.; Benedict, B.H.; Benekos, N.; Benhammou, Y.; Benincasa, G.P.; Benjamin, D.P.; Benoit, M.; Bensinger, J.R.; Benslama, K.; Bentvelsen, S.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Berghaus, F.; Berglund, E.; Beringer, J.; Bernat, P.; Bernhard, R.; Bernius, C.; Berry, T.; Bertin, A.; Besana, M.I.; Besson, N.; Bethke, S.; Bianchi, R.M.; Bianco, M.; Biebel, O.; Biesiada, J.; Biglietti, M.; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biscarat, C.; Bitenc, U.; Black, K.M.; Blair, R.E.; Blanchard, J-B; Blanchot, G.; Blocker, C.; Blondel, A.; Blum, W.; Blumenschein, U.; Bobbink, G.J.; Bocci, A.; Boehler, M.; Boek, J.; Boelaert, N.; Boser, S.; Bogaerts, J.A.; Bogouch, A.; Bohm, C.; Bohm, J.; Boisvert, V.; Bold, T.; Boldea, V.; Bondarenko, V.G.; Bondioli, M.; Boonekamp, M.; Bordoni, S.; Borer, C.; Borisov, A.; Borissov, G.; Borjanovic, I.; Borroni, S.; Bos, K.; Boscherini, D.; Bosman, M.; Boterenbrood, H.; Bouchami, J.; Boudreau, J.; Bouhova-Thacker, E.V.; Boulahouache, C.; Bourdarios, C.; Boveia, A.; Boyd, J.; Boyko, I.R.; Bozovic-Jelisavcic, I.; Bracinik, J.; Braem, A.; Branchini, P.; Brandenburg, G.W.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J.E.; Braun, H.M.; Brelier, B.; Bremer, J.; Brenner, R.; Bressler, S.; Britton, D.; Brochu, F.M.; Brock, I.; Brock, R.; Brodet, E.; Bromberg, C.; Brooijmans, G.; Brooks, W.K.; Brown, G.; Bruckman de Renstrom, P.A.; Bruncko, D.; Bruneliere, R.; Brunet, S.; Bruni, A.; Bruni, G.; Bruschi, M.; Bucci, F.; Buchanan, J.; Buchholz, P.; Buckley, A.G.; Budagov, I.A.; Budick, B.; Buscher, V.; Bugge, L.; Bulekov, O.; Bunse, M.; Buran, T.; Burckhart, H.; Burdin, S.; Burgess, T.; Burke, S.; Busato, E.; Bussey, P.; Buszello, C.P.; Butin, F.; Butler, B.; Butler, J.M.; Buttar, C.M.; Butterworth, J.M.; Byatt, T.; Caballero, J.; Cabrera Urban, S.; Caforio, D.; Cakir, O.; Calafiura, P.; Calderini, G.; Calfayan, P.; Calkins, R.; Caloba, L.P.; Calvet, D.; Camarri, P.; Cameron, D.; Campana, S.; Campanelli, M.; Canale, V.; Canelli, F.; Canepa, A.; Cantero, J.; Capasso, L.; Capeans Garrido, M.D.M.; Caprini, I.; Caprini, M.; Capua, M.; Caputo, R.; Caramarcu, C.; Cardarelli, R.; Carli, T.; Carlino, G.; Carminati, L.; Caron, B.; Caron, S.; Carrillo Montoya, G.D.; Carron Montero, S.; Carter, A.A.; Carter, J.R.; Carvalho, J.; Casadei, D.; Casado, M.P.; Cascella, M.; Castaneda Hernandez, A.M.; Castaneda-Miranda, E.; Castillo Gimenez, V.; Castro, N.F.; 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Lumb, D.; Luminari, L.; Lund, E.; Lund-Jensen, B.; Lundberg, B.; Lundberg, J.; Lundquist, J.; Lynn, D.; Lys, J.; Lytken, E.; Ma, H.; Ma, L.L.; Macana Goia, J.A.; Maccarrone, G.; Macchiolo, A.; Macek, B.; Machado Miguens, J.; Mackeprang, R.; Madaras, R.J.; Mader, W.F.; Maenner, R.; Maeno, T.; Mattig, P.; Mattig, S.; Magalhaes Martins, P.J.; Magradze, E.; Mahalalel, Y.; Mahboubi, K.; Mahmood, A.; Maiani, C.; Maidantchik, C.; Maio, A.; Majewski, S.; Makida, Y.; Makouski, M.; Makovec, N.; Malecki, Pa.; Malecki, P.; Maleev, V.P.; Malek, F.; Mallik, U.; Malon, D.; Maltezos, S.; Malyshev, V.; Malyukov, S.; Mambelli, M.; Mameghani, R.; Mamuzic, J.; Mandelli, L.; Mandic, I.; Mandrysch, R.; Maneira, J.; Mangeard, P.S.; Manjavidze, I.D.; Manning, P.M.; Manousakis-Katsikakis, A.; Mansoulie, B.; Mapelli, A.; Mapelli, L.; March, L.; Marchand, J.F.; Marchese, F.; Marchiori, G.; Marcisovsky, M.; Marino, C.P.; Marroquim, F.; Marshall, Z.; Marti-Garcia, S.; Martin, A.J.; Martin, A.J.; Martin, B.; Martin, B.; Martin, F.F.; Martin, J.P.; Martin, T.A.; Martin dit Latour, B.; Martinez, M.; Martinez Outschoorn, V.; Martini, A.; Martyniuk, A.C.; Marzano, F.; Marzin, A.; Masetti, L.; Mashimo, T.; Mashinistov, R.; Masik, J.; Maslennikov, A.L.; Massa, I.; Massol, N.; Mastroberardino, A.; Masubuchi, T.; Matricon, P.; Matsunaga, H.; Matsushita, T.; Mattravers, C.; Maxfield, S.J.; Mayne, A.; Mazini, R.; Mazur, M.; Mazzanti, M.; Mc Donald, J.; Mc Kee, S.P.; McCarn, A.; McCarthy, R.L.; McCubbin, N.A.; McFarlane, K.W.; McGlone, H.; Mchedlidze, G.; McMahon, S.J.; McPherson, R.A.; Meade, A.; Mechnich, J.; Mechtel, M.; Medinnis, M.; Meera-Lebbai, R.; Meguro, T.M.; Mehlhase, S.; Mehta, A.; Meier, K.; Meirose, B.; Melachrinos, C.; Mellado Garcia, B.R.; Mendoza Navas, L.; Meng, Z.; Menke, S.; Meoni, E.; Mermod, P.; Merola, L.; Meroni, C.; Merritt, F.S.; Messina, A.M.; Metcalfe, J.; Mete, A.S.; Meyer, J-P.; Meyer, J.; Meyer, J.; Meyer, T.C.; Meyer, W.T.; Miao, J.; Michal, S.; Micu, L.; Middleton, R.P.; Migas, S.; Mijovic, L.; Mikenberg, G.; Mikestikova, M.; Mikuz, M.; Miller, D.W.; Mills, W.J.; Mills, C.M.; Milov, A.; Milstead, D.A.; Milstein, D.; Minaenko, A.A.; Minano, M.; Minashvili, I.A.; Mincer, A.I.; Mindur, B.; Mineev, M.; Ming, Y.; Mir, L.M.; Mirabelli, G.; Misawa, S.; Miscetti, S.; Misiejuk, A.; Mitrevski, J.; Mitsou, V.A.; Miyagawa, P.S.; Mjornmark, J.U.; Mladenov, D.; Moa, T.; Moed, S.; Moeller, V.; Monig, K.; Moser, N.; Mohr, W.; Mohrdieck-Mock, S.; Moles-Valls, R.; Molina-Perez, J.; Monk, J.; Monnier, E.; Montesano, S.; Monticelli, F.; Moore, R.W.; Mora Herrera, C.; Moraes, A.; Morais, A.; Morel, J.; Morello, G.; Moreno, D.; Moreno Llacer, M.; Morettini, P.; Morii, M.; Morley, A.K.; Mornacchi, G.; Morozov, S.V.; Morris, J.D.; Moser, H.G.; Mosidze, M.; Moss, J.; Mount, R.; Mountricha, E.; Mouraviev, S.V.; Moyse, E.J.W.; Mudrinic, M.; Mueller, F.; Mueller, J.; Mueller, K.; Muller, T.A.; Muenstermann, D.; Muir, A.; Munwes, Y.; Murillo Garcia, R.; Murray, W.J.; Mussche, I.; Musto, E.; Myagkov, A.G.; Myska, M.; Nadal, J.; Nagai, K.; Nagano, K.; Nagasaka, Y.; Nairz, A.M.; Nakamura, K.; Nakano, I.; Nakatsuka, H.; Nanava, G.; Napier, A.; Nash, M.; Nation, N.R.; Nattermann, T.; Naumann, T.; Navarro, G.; Nderitu, S.K.; Neal, H.A.; Nebot, E.; Nechaeva, P.; Negri, A.; Negri, G.; Nelson, A.; Nelson, T.K.; Nemecek, S.; Nemethy, P.; Nepomuceno, A.A.; Nessi, M.; Neubauer, M.S.; Neusiedl, A.; Neves, R.N.; Nevski, P.; Newcomer, F.M.; Nickerson, R.B.; Nicolaidou, R.; Nicolas, L.; Nicoletti, G.; Nicquevert, B.; Niedercorn, F.; Nielsen, J.; Nikiforov, A.; Nikolaev, K.; Nikolic-Audit, I.; Nikolopoulos, K.; Nilsen, H.; Nilsson, P.; Nisati, A.; Nishiyama, T.; Nisius, R.; Nodulman, L.; Nomachi, M.; Nomidis, I.; Nordberg, M.; Nordkvist, B.; Notz, D.; Novakova, J.; Nozaki, M.; Nozicka, M.; Nugent, I.M.; Nuncio-Quiroz, A.E.; Nunes Hanninger, G.; Nunnemann, T.; Nurse, E.; O'Neil, D.C.; O'Shea, V.; Oakham, F.G.; Oberlack, H.; Ochi, A.; Oda, S.; Odaka, S.; Odier, J.; Ogren, H.; Oh, A.; Oh, S.H.; Ohm, C.C.; Ohshima, T.; Ohshita, H.; Ohsugi, T.; Okada, S.; Okawa, H.; Okumura, Y.; Okuyama, T.; Olchevski, A.G.; Oliveira, M.; Oliveira Damazio, D.; Oliver, J.; Oliver Garcia, E.; Olivito, D.; Olszewski, A.; Olszowska, J.; Omachi, C.; Onofre, A.; Onyisi, P.U.E.; Oram, C.J.; Oreglia, M.J.; Oren, Y.; Orestano, D.; Orlov, I.; Oropeza Barrera, C.; Orr, R.S.; Ortega, E.O.; Osculati, B.; Ospanov, R.; Osuna, C.; Ottersbach, J.P; Ould-Saada, F.; Ouraou, A.; Ouyang, Q.; Owen, M.; Owen, S.; Oyarzun, A; Ozcan, V.E.; Ozone, K.; Ozturk, N.; Pacheco Pages, A.; Padilla Aranda, C.; Paganis, E.; Pahl, C.; Paige, F.; Pajchel, K.; Palestini, S.; Pallin, D.; Palma, A.; Palmer, J.D.; Pan, Y.B.; Panagiotopoulou, E.; Panes, B.; Panikashvili, N.; Panitkin, S.; Pantea, D.; Panuskova, M.; Paolone, V.; Papadopoulou, Th.D.; Park, S.J.; Park, W.; Parker, M.A.; Parker, S.I.; Parodi, F.; Parsons, J.A.; Parzefall, U.; Pasqualucci, E.; Passeri, A.; Pastore, F.; Pastore, Fr.; Pasztor, G.; Pataraia, S.; Pater, J.R.; Patricelli, S.; Patwa, A.; Pauly, T.; Peak, L.S.; Pecsy, M.; Pedraza Morales, M.I.; Peleganchuk, S.V.; Peng, H.; Penson, A.; Penwell, J.; Perantoni, M.; Perez, K.; Perez Codina, E.; Perez Garcia-Estan, M.T.; Perez Reale, V.; Perini, L.; Pernegger, H.; Perrino, R.; Persembe, S.; Perus, P.; Peshekhonov, V.D.; Petersen, B.A.; Petersen, T.C.; Petit, E.; Petridou, C.; Petrolo, E.; Petrucci, F.; Petschull, D; Petteni, M.; Pezoa, R.; Phan, A.; Phillips, A.W.; Piacquadio, G.; Piccinini, M.; Piegaia, R.; Pilcher, J.E.; Pilkington, A.D.; Pina, J.; Pinamonti, M.; Pinfold, J.L.; Pinto, B.; Pizio, C.; Placakyte, R.; Plamondon, M.; Pleier, M.A.; Poblaguev, A.; Poddar, S.; Podlyski, F.; Poffenberger, P.; Poggioli, L.; Pohl, M.; Polci, F.; Polesello, G.; Policicchio, A.; Polini, A.; Poll, J.; Polychronakos, V.; Pomeroy, D.; Pommes, K.; Ponsot, P.; Pontecorvo, L.; Pope, B.G.; Popeneciu, G.A.; Popovic, D.S.; Poppleton, A.; Popule, J.; Portell Bueso, X.; Porter, R.; Pospelov, G.E.; Pospisil, S.; Potekhin, M.; Potrap, I.N.; Potter, C.J.; Potter, C.T.; Potter, K.P.; Poulard, G.; Poveda, J.; Prabhu, R.; Pralavorio, P.; Prasad, S.; Pravahan, R.; Pribyl, L.; Price, D.; Price, L.E.; Prichard, P.M.; Prieur, D.; Primavera, M.; Prokofiev, K.; Prokoshin, F.; Protopopescu, S.; Proudfoot, J.; Prudent, X.; Przysiezniak, H.; Psoroulas, S.; Ptacek, E.; Puigdengoles, C.; Purdham, J.; Purohit, M.; Puzo, P.; Pylypchenko, Y.; Qi, M.; Qian, J.; Qian, W.; Qin, Z.; Quadt, A.; Quarrie, D.R.; Quayle, W.B.; Quinonez, F.; Raas, M.; Radeka, V.; Radescu, V.; Radics, B.; Rador, T.; Ragusa, F.; Rahal, G.; Rahimi, A.M.; Rajagopalan, S.; Rammensee, M.; Rammes, M.; Rauscher, F.; Rauter, E.; Raymond, M.; Read, A.L.; Rebuzzi, D.M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reeves, K.; Reinherz-Aronis, E.; Reinsch, A; Reisinger, I.; Reljic, D.; Rembser, C.; Ren, Z.L.; Renkel, P.; Rescia, S.; Rescigno, M.; Resconi, S.; Resende, B.; Reznicek, P.; Rezvani, R.; Richards, A.; Richards, R.A.; Richter, R.; Richter-Was, E.; Ridel, M.; Rijpstra, M.; Rijssenbeek, M.; Rimoldi, A.; Rinaldi, L.; Rios, R.R.; Riu, I.; Rizatdinova, F.; Rizvi, E.; Roa Romero, D.A.; Robertson, S.H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, JEM; Robinson, M.; Robson, A.; Rocha de Lima, J.G.; Roda, C.; Roda Dos Santos, D.; Rodriguez, D.; Rodriguez Garcia, Y.; Roe, S.; Rohne, O.; Rojo, V.; Rolli, S.; Romaniouk, A.; Romanov, V.M.; Romeo, G.; Romero Maltrana, D.; Roos, L.; Ros, E.; Rosati, S.; Rosenbaum, G.A.; Rosselet, L.; Rossetti, V.; Rossi, L.P.; Rotaru, M.; Rothberg, J.; Rousseau, D.; Royon, C.R.; Rozanov, A.; Rozen, Y.; Ruan, X.; Ruckert, B.; Ruckstuhl, N.; Rud, V.I.; Rudolph, G.; Ruhr, F.; Ruggieri, F.; Ruiz-Martinez, A.; Rumyantsev, L.; Rurikova, Z.; Rusakovich, N.A.; Rutherfoord, J.P.; Ruwiedel, C.; Ruzicka, P.; Ryabov, Y.F.; Ryan, P.; Rybkin, G.; Rzaeva, S.; Saavedra, A.F.; Sadrozinski, H.F-W.; Sadykov, R.; Sakamoto, H.; Salamanna, G.; Salamon, A.; Saleem, M.S.; Salihagic, D.; Salnikov, A.; Salt, J.; Salvachua Ferrando, B.M.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sampsonidis, D.; Samset, B.H.; Sandaker, H.; Sander, H.G.; Sanders, M.P.; Sandhoff, M.; Sandhu, P.; Sandstroem, R.; Sandvoss, S.; Sankey, D.P.C.; Sanny, B.; Sansoni, A.; Santamarina Rios, C.; Santoni, C.; Santonico, R.; Saraiva, J.G.; Sarangi, T.; Sarkisyan-Grinbaum, E.; Sarri, F.; Sasaki, O.; Sasao, N.; Satsounkevitch, I.; Sauvage, G.; Savard, P.; Savine, A.Y.; Savinov, V.; Sawyer, L.; Saxon, D.H.; Says, L.P.; Sbarra, C.; Sbrizzi, A.; Scannicchio, D.A.; Schaarschmidt, J.; Schacht, P.; Schafer, U.; Schaetzel, S.; Schaffer, A.C.; Schaile, D.; Schamberger, R.D.; Schamov, A.G.; Schegelsky, V.A.; Scheirich, D.; Schernau, M.; Scherzer, M.I.; Schiavi, C.; Schieck, J.; Schioppa, M.; Schlenker, S.; Schmidt, E.; Schmieden, K.; Schmitt, C.; Schmitz, M.; Schott, M.; Schouten, D.; Schovancova, J.; Schram, M.; Schreiner, A.; Schroeder, C.; Schroer, N.; Schroers, M.; Schultes, J.; Schultz-Coulon, H.C.; Schumacher, J.W.; Schumacher, M.; Schumm, B.A.; Schune, Ph.; Schwanenberger, C.; Schwartzman, A.; Schwemling, Ph.; Schwienhorst, R.; Schwierz, R.; Schwindling, J.; Scott, W.G.; Searcy, J.; Sedykh, E.; Segura, E.; Seidel, S.C.; Seiden, A.; Seifert, F.; Seixas, J.M.; Sekhniaidze, G.; Seliverstov, D.M.; Sellden, B.; Semprini-Cesari, N.; Serfon, C.; Serin, L.; Seuster, R.; Severini, H.; Sevior, M.E.; Sfyrla, A.; Shabalina, E.; Shamim, M.; Shan, L.Y.; Shank, J.T.; Shao, Q.T.; Shapiro, M.; Shatalov, P.B.; Shaw, K.; Sherman, D.; Sherwood, P.; Shibata, A.; Shimojima, M.; Shin, T.; Shmeleva, A.; Shochet, M.J.; Shupe, M.A.; Sicho, P.; Sidoti, A.; Siegert, F; Siegrist, J.; Sijacki, Dj.; Silbert, O.; Silva, J.; Silver, Y.; Silverstein, D.; Silverstein, S.B.; Simak, V.; Simic, Lj.; Simion, S.; Simmons, B.; Simonyan, M.; Sinervo, P.; Sinev, N.B.; Sipica, V.; Siragusa, G.; Sisakyan, A.N.; Sivoklokov, S.Yu.; Sjoelin, J.; Sjursen, T.B.; Skovpen, K.; Skubic, P.; Slater, M.; Slavicek, T.; Sliwa, K.; Sloper, J.; Sluka, T.; Smakhtin, V.; Smirnov, S.Yu.; Smirnov, Y.; Smirnova, L.N.; Smirnova, O.; Smith, B.C.; Smith, D.; Smith, K.M.; Smizanska, M.; Smolek, K.; Snesarev, A.A.; Snow, S.W.; Snow, J.; Snuverink, J.; Snyder, S.; Soares, M.; Sobie, R.; Sodomka, J.; Soffer, A.; Solans, C.A.; Solar, M.; Solc, J.; Solfaroli Camillocci, E.; Solodkov, A.A.; Solovyanov, O.V.; Soluk, R.; Sondericker, J.; Sopko, V.; Sopko, B.; Sosebee, M.; Soukharev, A.; Spagnolo, S.; Spano, F.; Spencer, E.; Spighi, R.; Spigo, G.; Spila, F.; Spiwoks, R.; Spousta, M.; Spreitzer, T.; Spurlock, B.; St. Denis, R.D.; Stahl, T.; Stahlman, J.; Stamen, R.; Stancu, S.N.; Stanecka, E.; Stanek, R.W.; Stanescu, C.; Stapnes, S.; Starchenko, E.A.; Stark, J.; Staroba, P.; Starovoitov, P.; Stastny, J.; Stavina, P.; Steele, G.; Steinbach, P.; Steinberg, P.; Stekl, I.; Stelzer, B.; Stelzer, H.J.; Stelzer-Chilton, O.; Stenzel, H.; Stevenson, K.; Stewart, G.A.; Stockton, M.C.; Stoerig, K.; Stoicea, G.; Stonjek, S.; Strachota, P.; Stradling, A.R.; Straessner, A.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strauss, M.; Strizenec, P.; Strohmer, R.; Strom, D.M.; Stroynowski, R.; Strube, J.; Stugu, B.; Soh, D.A.; Su, D.; Sugaya, Y.; Sugimoto, T.; Suhr, C.; Suk, M.; Sulin, V.V.; Sultansoy, S.; Sumida, T.; Sun, X.H.; Sundermann, J.E.; Suruliz, K.; Sushkov, S.; Susinno, G.; Sutton, M.R.; Suzuki, T.; Suzuki, Y.; Sykora, I.; Sykora, T.; Szymocha, T.; Sanchez, J.; Ta, D.; Tackmann, K.; Taffard, A.; Tafirout, R.; Taga, A.; Takahashi, Y.; Takai, H.; Takashima, R.; Takeda, H.; Takeshita, T.; Talby, M.; Talyshev, A.; Tamsett, M.C.; Tanaka, J.; Tanaka, R.; Tanaka, S.; Tanaka, S.; Tapprogge, S.; Tardif, D.; Tarem, S.; Tarrade, F.; Tartarelli, G.F.; Tas, P.; Tasevsky, M.; Tassi, E.; Tatarkhanov, M.; Taylor, C.; Taylor, F.E.; Taylor, G.N.; Taylor, R.P.; Taylor, W.; Teixeira-Dias, P.; Ten Kate, H.; Teng, P.K.; Tennenbaum-Katan, Y.D.; Terada, S.; Terashi, K.; Terron, J.; Terwort, M.; Testa, M.; Teuscher, R.J.; Thioye, M.; Thoma, S.; Thomas, J.P.; Thompson, E.N.; Thompson, P.D.; Thompson, P.D.; Thompson, R.J.; Thompson, A.S.; Thomson, E.; Thun, R.P.; Tic, T.; Tikhomirov, V.O.; Tikhonov, Y.A.; Tipton, P.; Tique Aires Viegas, F.J.; Tisserant, S.; Toczek, B.; Todorov, T.; Todorova-Nova, S.; Toggerson, B.; Tojo, J.; Tokar, S.; Tokushuku, K.; Tollefson, K.; Tomasek, L.; Tomasek, M.; Tomoto, M.; Tompkins, L.; Toms, K.; Tonoyan, A.; Topfel, C.; Topilin, N.D.; Torrence, E.; Torro Pastor, E.; Toth, J.; Touchard, F.; Tovey, D.R.; Trefzger, T.; Tremblet, L.; Tricoli, A.; Trigger, I.M.; Trincaz-Duvoid, S.; Trinh, T.N.; Tripiana, M.F.; Triplett, N.; Trischuk, W.; Trivedi, A.; Trocme, B.; Troncon, C.; Trzupek, A.; Tsarouchas, C.; Tseng, J.C-L.; Tsiakiris, M.; Tsiareshka, P.V.; Tsionou, D.; Tsipolitis, G.; Tsiskaridze, V.; Tskhadadze, E.G.; Tsukerman, I.I.; Tsulaia, V.; Tsung, J.W.; Tsuno, S.; Tsybychev, D.; Tuggle, J.M.; Turecek, D.; Turk Cakir, I.; Turlay, E.; Tuts, P.M.; Twomey, M.S.; Tylmad, M.; Tyndel, M.; Uchida, K.; Ueda, I.; Ugland, M.; Uhlenbrock, M.; Uhrmacher, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Unno, Y.; Urbaniec, D.; Urkovsky, E.; Urquijo, P.; Urrejola, P.; Usai, G.; Uslenghi, M.; Vacavant, L.; Vacek, V.; Vachon, B.; Vahsen, S.; Valente, P.; Valentinetti, S.; Valkar, S.; Valladolid Gallego, E.; Vallecorsa, S.; Valls Ferrer, J.A.; Van Berg, R.; van der Graaf, H.; van der Kraaij, E.; van der Poel, E.; van der Ster, D.; van Eldik, N.; van Gemmeren, P.; van Kesteren, Z.; van Vulpen, I.; Vandelli, W.; Vaniachine, A.; Vankov, P.; Vannucci, F.; Vari, R.; Varnes, E.W.; Varouchas, D.; Vartapetian, A.; Varvell, K.E.; Vasilyeva, L.; Vassilakopoulos, V.I.; Vazeille, F.; Vellidis, C.; Veloso, F.; Veneziano, S.; Ventura, A.; Ventura, D.; Venturi, M.; Venturi, N.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J.C.; Vetterli, M.C.; Vichou, I.; Vickey, T.; Viehhauser, G.H.A.; Villa, M.; Villani, E.G.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M.G.; Vinek, E.; Vinogradov, V.B.; Viret, S.; Virzi, J.; Vitale, A.; Vitells, O.; Vivarelli, I.; Vives Vaque, F.; Vlachos, S.; Vlasak, M.; Vlasov, N.; Vogel, A.; Vokac, P.; Volpi, M.; von der Schmitt, H.; von Loeben, J.; von Radziewski, H.; von Toerne, E.; Vorobel, V.; Vorwerk, V.; Vos, M.; Voss, R.; Voss, T.T.; Vossebeld, J.H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vu Anh, T.; Vudragovic, D.; Vuillermet, R.; Vukotic, I.; Wagner, P.; Walbersloh, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wall, R.; Wang, C.; Wang, H.; Wang, J.; Wang, S.M.; Warburton, A.; Ward, C.P.; Warsinsky, M.; Wastie, R.; Watkins, P.M.; Watson, A.T.; Watson, M.F.; Watts, G.; Watts, S.; Waugh, A.T.; Waugh, B.M.; Weber, M.D.; Weber, M.; Weber, M.S.; Weber, P.; Weidberg, A.R.; Weingarten, J.; Weiser, C.; Wellenstein, H.; Wells, P.S.; Wen, M.; Wenaus, T.; Wendler, S.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, P.; Werth, M.; Werthenbach, U.; Wessels, M.; Whalen, K.; White, A.; White, M.J.; White, S.; Whitehead, S.R.; Whiteson, D.; Whittington, D.; Wicek, F.; Wicke, D.; Wickens, F.J.; Wiedenmann, W.; Wielers, M.; Wienemann, P.; Wiglesworth, C.; Wiik, L.A.M.; Wildauer, A.; Wildt, M.A.; Wilkens, H.G.; Williams, E.; Williams, H.H.; Willocq, S.; Wilson, J.A.; Wilson, M.G.; Wilson, A.; Wingerter-Seez, I.; Winklmeier, F.; Wittgen, M.; Wolter, M.W.; Wolters, H.; Wosiek, B.K.; Wotschack, J.; Woudstra, M.J.; Wraight, K.; Wright, C.; Wright, D.; Wrona, B.; Wu, S.L.; Wu, X.; Wulf, E.; Wynne, B.M.; Xaplanteris, L.; Xella, S.; Xie, S.; Xu, D.; Xu, N.; Yamada, M.; Yamamoto, A.; Yamamoto, K.; Yamamoto, S.; Yamamura, T.; Yamaoka, J.; Yamazaki, T.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, U.K.; Yang, Z.; Yao, W-M.; Yao, Y.; Yasu, Y.; Ye, J.; Ye, S.; Yilmaz, M.; Yoosoofmiya, R.; Yorita, K.; Yoshida, R.; Young, C.; Youssef, S.P.; Yu, D.; Yu, J.; Yuan, L.; Yurkewicz, A.; Zaidan, R.; Zaitsev, A.M.; Zajacova, Z.; Zambrano, V.; Zanello, L.; Zaytsev, A.; Zeitnitz, C.; Zeller, M.; Zemla, A.; Zendler, C.; Zenin, O.; Zenis, T.; Zenonos, Z.; Zenz, S.; Zerwas, D.; Zevi della Porta, G.; Zhan, Z.; Zhang, H.; Zhang, J.; Zhang, Q.; Zhang, X.; Zhao, L.; Zhao, T.; Zhao, Z.; Zhemchugov, A.; Zhong, J.; Zhou, B.; Zhou, N.; Zhou, Y.; Zhu, C.G.; Zhu, H.; Zhu, Y.; Zhuang, X.; Zhuravlov, V.; Zimmermann, R.; Zimmermann, S.; Zimmermann, S.; Ziolkowski, M.; Zivkovic, L.; Zobernig, G.; Zoccoli, A.; zur Nedden, M.; Zutshi, V.

2010-01-01

The simulation software for the ATLAS Experiment at the Large Hadron Collider is being used for large-scale production of events on the LHC Computing Grid. This simulation requires many components, from the generators that simulate particle collisions, through packages simulating the response of the various detectors and triggers. All of these components come together under the ATLAS simulation infrastructure. In this paper, that infrastructure is discussed, including that supporting the detector description, interfacing the event generation, and combining the GEANT4 simulation of the response of the individual detectors. Also described are the tools allowing the software validation, performance testing, and the validation of the simulated output against known physics processes.

Canadian ATLAS data center to support CERN's LHC

CERN Multimedia

2006-01-01

"The biggest science experiment in history is currently underway at the world-famous CERN labs in Switzerland, and Canada is poised to play a critical role in its success. Thanks to a $10.5 million investment announced by the Canada Foundation for Innovation (CFI), an ultra-sophisticated computing facility -- the ATLAS Data Center -- will be created to support the ATLAS project at CERN's Large Hadron Collider (LHC)." (1 page)

ATLAS DQ2 Deletion Service

International Nuclear Information System (INIS)

Oleynik, Danila; Petrosyan, Artem; Garonne, Vincent; Campana, Simone

2012-01-01

The ATLAS Distributed Data Management project DQ2 is responsible for the replication, access and bookkeeping of ATLAS data across more than 100 distributed grid sites. It also enforces data management policies decided on by the collaboration and defined in the ATLAS computing model. The DQ2 Deletion Service is one of the most important DDM services. This distributed service interacts with 3rd party grid middleware and the DQ2 catalogues to serve data deletion requests on the grid. Furthermore, it also takes care of retry strategies, check-pointing transactions, load management and fault tolerance. In this paper special attention is paid to the technical details which are used to achieve the high performance of service, accomplished without overloading either site storage, catalogues or other DQ2 components. Special attention is also paid to the deletion monitoring service that allows operators a detailed view of the working system.

Integration Of PanDA Workload Management System With Supercomputers for ATLAS

CERN Document Server

Oleynik, Danila; The ATLAS collaboration; De, Kaushik; Wenaus, Torre; Maeno, Tadashi; Barreiro Megino, Fernando Harald; Nilsson, Paul; Guan, Wen; Panitkin, Sergey

2016-01-01

The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe, and were recently credited for the discovery of a Higgs boson. ATLAS, one of the largest collaborations ever assembled in the sciences, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production ANd Distributed Analysis system) Workload Management System for managing the workflow for all data processing on over 150 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. While PanDA currently uses more t...

The magnetically driven imploding liner parameter space of the ATLAS capacitor bank

CERN Document Server

Lindemuth, I R; Faehl, R J; Reinovsky, R E

2001-01-01

Summary form only given, as follows. The Atlas capacitor bank (23 MJ, 30 MA) is now operational at Los Alamos. Atlas was designed primarily to magnetically drive imploding liners for use as impactors in shock and hydrodynamic experiments. We have conducted a computational "mapping" of the high-performance imploding liner parameter space accessible to Atlas. The effect of charge voltage, transmission inductance, liner thickness, liner initial radius, and liner length has been investigated. One conclusion is that Atlas is ideally suited to be a liner driver for liner-on-plasma experiments in a magnetized target fusion (MTF) context . The parameter space of possible Atlas reconfigurations has also been investigated.

Concepts and Plans towards fast large scale Monte Carlo production for the ATLAS Experiment

CERN Document Server

Chapman, J; Duehrssen, M; Elsing, M; Froidevaux, D; Harrington, R; Jansky, R; Langenberg, R; Mandrysch, R; Marshall, Z; Ritsch, E; Salzburger, A

2014-01-01

The huge success of the physics program of the ATLAS experiment at the Large Hadron Collider (LHC) during run I relies upon a great number of simulated Monte Carlo events. This Monte Carlo production takes the biggest part of the computing resources being in use by ATLAS as of now. In this document we describe the plans to overcome the computing resource limitations for large scale Monte Carlo production in the ATLAS Experiment for run II, and beyond. A number of fast detector simulation, digitization and reconstruction techniques and are being discussed, based upon a new flexible detector simulation framework. To optimally benefit from these developments, a redesigned ATLAS MC production chain is presented at the end of this document.

Achievements of the ATLAS Distributed Analysis during the first run period

CERN Document Server

Farida, Fassi; The ATLAS collaboration

2013-01-01

Summary : In the LHC operations era analyzing the large data by the distributed physicists becomes a challenging task. The Computing Model of the ATLAS experiment at the LHC at CERN was designed around the concepts of grid computing. Large data volumes from the detectors and simulations require a large number of CPUs and storage space for data processing. To cope with these challenges a global network known as the Worlwide LHC Computing Grid (WLCG) was built. This is the most sophisticated data taking and analysis system ever built. Since the start of data-taking, the ATLAS Distributed Analysis (ADA) service has been running stably with the huge amount of data. The reliability of the ADA service is high but steadily improving; grid sites are continually validated against a set of standard tests, and a dedicated team of expert shifters provides user support and communicates user problems to the sites. The ATLAS Grid Computing Model is reviewed in this talk. Emphasis is given to ADA system. Description : The ce...

PD2P : PanDA Dynamic Data Placement for ATLAS

OpenAIRE

Maeno, T; De, K; Panitkin, S

2012-01-01

The PanDA (Production and Distributed Analysis) system plays a key role in the ATLAS distributed computing infrastructure. PanDA is the ATLAS workload management system for processing all Monte-Carlo (MC) simulation and data reprocessing jobs in addition to user and group analysis jobs. The PanDA Dynamic Data Placement (PD2P) system has been developed to cope with difficulties of data placement for ATLAS. We will describe the design of the new system, its performance during the past year of d...

Multilevel Workflow System in the ATLAS Experiment

CERN Document Server

Borodin, M; The ATLAS collaboration; Golubkov, D; Klimentov, A; Maeno, T; Vaniachine, A

2015-01-01

The ATLAS experiment is scaling up Big Data processing for the next LHC run using a multilevel workflow system comprised of many layers. In Big Data processing ATLAS deals with datasets, not individual files. Similarly a task (comprised of many jobs) has become a unit of the ATLAS workflow in distributed computing, with about 0.8M tasks processed per year. In order to manage the diversity of LHC physics (exceeding 35K physics samples per year), the individual data processing tasks are organized into workflows. For example, the Monte Carlo workflow is composed of many steps: generate or configure hard-processes, hadronize signal and minimum-bias (pileup) events, simulate energy deposition in the ATLAS detector, digitize electronics response, simulate triggers, reconstruct data, convert the reconstructed data into ROOT ntuples for physics analysis, etc. Outputs are merged and/or filtered as necessary to optimize the chain. The bi-level workflow manager - ProdSys2 - generates actual workflow tasks and their jobs...

Searches for beyond the Standard Model physics with boosted topologies in the ATLAS experiment using the Grid-based Tier-3 facility at IFIC-Valencia

CERN Document Server

Villaplana Pérez, Miguel; Vos, Marcel

Both the LHC and ATLAS have been performing well beyond expectation since the start of the data taking by the end of 2009. Since then, several thousands of millions of collision events have been recorded by the ATLAS experiment. With a data taking efficiency higher than 95% and more than 99% of its channels working, ATLAS supplies data with an unmatched quality. In order to analyse the data, the ATLAS Collaboration has designed a distributed computing model based on GRID technologies. The ATLAS computing model and its evolution since the start of the LHC is discussed in section 3.1. The ATLAS computing model groups the different types of computing centers of the ATLAS Collaboration in a tiered hierarchy that ranges from Tier-0 at CERN, down to the 11 Tier-1 centers and the nearly 80 Tier-2 centres distributed world wide. The Spanish Tier-2 activities during the first years of data taking are described in section 3.2. Tier-3 are institution-level non-ATLAS funded or controlled centres that participate presuma...

Sim@P1: Using Cloudscheduler for offline processing on the ATLAS HLT farm

CERN Document Server

Berghaus, Frank; The ATLAS collaboration

2018-01-01

The Simulation at Point1 (Sim@P1) project was built in 2013 to take advantage of the ATLAS Trigger and Data Acquisition High Level Trigger (HLT) farm. The HLT farm provides more than 2,000 compute nodes, which are critical to ATLAS during data taking. When ATLAS is not recording data, this large compute resource is used to generate and process simulation data for the experiment. The Sim@P1 system uses virtual machines, deployed by OpenStack, in order to isolate the resources from the ATLAS technical and control network. During the upcoming long shutdown in 2019 (LS2), the HLT farm including the Sim@P1 infrastructure will be upgraded. A previous paper on the project emphasized the need for “simple, reliable, and efficient tools” to quickly switch between data acquisition operation and offline processing.In this contribution we assess various options for updating and simplifying the provisional tools. Cloudscheduler is a tool for provisioning cloud resources for batch computing that has been managing cloud ...

Virtualization of the ATLAS software environment on a shared HPC system

CERN Document Server

Schnoor, Ulrike; The ATLAS collaboration

2017-01-01

High-Performance Computing (HPC) and other research cluster computing resources provided by universities can be useful supplements to the collaboration’s own WLCG computing resources for data analysis and production of simulated event samples. The shared HPC cluster "NEMO" at the University of Freiburg has been made available to local ATLAS users through the provisioning of virtual machines incorporating the ATLAS software environment analogously to a WLCG center. The talk describes the concept and implementation of virtualizing the ATLAS software environment to run both data analysis and production on the HPC host system which is connected to the existing Tier-3 infrastructure. Main challenges include the integration into the NEMO and Tier-3 schedulers in a dynamic, on-demand way, the scalability of the OpenStack infrastructure, as well as the automatic generation of a fully functional virtual machine image providing access to the local user environment, the dCache storage element and the parallel file sys...

The ATLAS Glasgow Overview Week

CERN Multimedia

Richard Hawkings

2007-01-01

The ATLAS Overview Weeks always provide a good opportunity to see the status and progress throughout the experiment, and the July week at Glasgow University was no exception. The setting, amidst the traditional buildings of one of the UK's oldest universities, provided a nice counterpoint to all the cutting-edge research and technology being discussed. And despite predictions to the contrary, the weather at these northern latitudes was actually a great improvement on the previous few weeks in Geneva. The meeting sessions comprehensively covered the whole ATLAS project, from the subdetector and TDAQ systems and their commissioning, through to offline computing, analysis and physics. As a long-time ATLAS member who remembers plenary meetings in 1991 with 30 people drawing detector layouts on a whiteboard, the hardware and installation sessions were particularly impressive - to see how these dreams have been translated into 7000 tons of reality (and with attendant cabling, supports and services, which certainly...

Morphometric Atlas Selection for Automatic Brachial Plexus Segmentation

International Nuclear Information System (INIS)

Van de Velde, Joris; Wouters, Johan; Vercauteren, Tom; De Gersem, Werner; Duprez, Fréderic; De Neve, Wilfried; Van Hoof, Tom

2015-01-01

Purpose: The purpose of this study was to determine the effects of atlas selection based on different morphometric parameters, on the accuracy of automatic brachial plexus (BP) segmentation for radiation therapy planning. The segmentation accuracy was measured by comparing all of the generated automatic segmentations with anatomically validated gold standard atlases developed using cadavers. Methods and Materials: Twelve cadaver computed tomography (CT) atlases (3 males, 9 females; mean age: 73 years) were included in the study. One atlas was selected to serve as a patient, and the other 11 atlases were registered separately onto this “patient” using deformable image registration. This procedure was repeated for every atlas as a patient. Next, the Dice and Jaccard similarity indices and inclusion index were calculated for every registered BP with the original gold standard BP. In parallel, differences in several morphometric parameters that may influence the BP segmentation accuracy were measured for the different atlases. Specific brachial plexus-related CT-visible bony points were used to define the morphometric parameters. Subsequently, correlations between the similarity indices and morphometric parameters were calculated. Results: A clear negative correlation between difference in protraction-retraction distance and the similarity indices was observed (mean Pearson correlation coefficient = −0.546). All of the other investigated Pearson correlation coefficients were weak. Conclusions: Differences in the shoulder protraction-retraction position between the atlas and the patient during planning CT influence the BP autosegmentation accuracy. A greater difference in the protraction-retraction distance between the atlas and the patient reduces the accuracy of the BP automatic segmentation result

TU-AB-202-10: How Effective Are Current Atlas Selection Methods for Atlas-Based Auto-Contouring in Radiotherapy Planning?

Energy Technology Data Exchange (ETDEWEB)

Peressutti, D; Schipaanboord, B; Kadir, T; Gooding, M [Mirada Medical Limited, Science and Medical Technology, Oxford (United Kingdom); Soest, J van; Lustberg, T; Elmpt, W van; Dekker, A [Maastricht University Medical Centre, Department of Radiation Oncology MAASTRO - GROW School for Oncology Developmental Biology, Maastricht (Netherlands)

2016-06-15

Purpose: To investigate the effectiveness of atlas selection methods for improving atlas-based auto-contouring in radiotherapy planning. Methods: 275 H&N clinically delineated cases were employed as an atlas database from which atlases would be selected. A further 40 previously contoured cases were used as test patients against which atlas selection could be performed and evaluated. 26 variations of selection methods proposed in the literature and used in commercial systems were investigated. Atlas selection methods comprised either global or local image similarity measures, computed after rigid or deformable registration, combined with direct atlas search or with an intermediate template image. Workflow Box (Mirada-Medical, Oxford, UK) was used for all auto-contouring. Results on brain, brainstem, parotids and spinal cord were compared to random selection, a fixed set of 10 “good” atlases, and optimal selection by an “oracle” with knowledge of the ground truth. The Dice score and the average ranking with respect to the “oracle” were employed to assess the performance of the top 10 atlases selected by each method. Results: The fixed set of “good” atlases outperformed all of the atlas-patient image similarity-based selection methods (mean Dice 0.715 c.f. 0.603 to 0.677). In general, methods based on exhaustive comparison of local similarity measures showed better average Dice scores (0.658 to 0.677) compared to the use of either template image (0.655 to 0.672) or global similarity measures (0.603 to 0.666). The performance of image-based selection methods was found to be only slightly better than a random (0.645). Dice scores given relate to the left parotid, but similar results patterns were observed for all organs. Conclusion: Intuitively, atlas selection based on the patient CT is expected to improve auto-contouring performance. However, it was found that published approaches performed marginally better than random and use of a fixed set of

PanDA: Exascale Federation of Resources for the ATLAS Experiment at the LHC

Directory of Open Access Journals (Sweden)

Megino Fernando Barreiro

2016-01-01

The PanDA (Production and Distributed Analysis system was developed in 2005 for the ATLAS experiment on top of this heterogeneous infrastructure to seamlessly integrate the computational resources and give the users the feeling of a unique system. Since its origins, PanDA has evolved together with upcoming computing paradigms in and outside HEP, such as changes in the networking model, Cloud Computing and HPC. It is currently running steadily up to 200 thousand simultaneous cores (limited by the available resources for ATLAS, up to two million aggregated jobs per day and processes over an exabyte of data per year. The success of PanDA in ATLAS is triggering the widespread adoption and testing by other experiments. In this contribution we will give an overview of the PanDA components and focus on the new features and upcoming challenges that are relevant to the next decade of distributed computing workload management using PanDA.

Big Data tools as applied to ATLAS event data

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00225336; The ATLAS collaboration; Gardner, Robert; Bryant, Lincoln

2017-01-01

Big Data technologies have proven to be very useful for storage, processing and visualization of derived metrics associated with ATLAS distributed computing (ADC) services. Logfiles, database records, and metadata from a diversity of systems have been aggregated and indexed to create an analytics platform for ATLAS ADC operations analysis. Dashboards, wide area data access cost metrics, user analysis patterns, and resource utilization efficiency charts are produced flexibly through queries against a powerful analytics cluster. Here we explore whether these techniques and associated analytics ecosystem can be applied to add new modes of open, quick, and pervasive access to ATLAS event data. Such modes would simplify access and broaden the reach of ATLAS public data to new communities of users. An ability to efficiently store, filter, search and deliver ATLAS data at the event and/or sub-event level in a widely supported format would enable or significantly simplify usage of machine learning environments and to...

Rate Predictions and Trigger/DAQ Resource Monitoring in ATLAS

CERN Document Server

Schaefer, D M; The ATLAS collaboration

2012-01-01

Since starting in 2010, the Large Hadron Collider (LHC) has pro- duced collisions at an ever increasing rate. The ATLAS experiment successfully records the collision data with high eciency and excel- lent data quality. Events are selected using a three-level trigger system, where each level makes a more re ned selection. The level-1 trigger (L1) consists of a custom-designed hardware trigger which seeds two higher software based trigger levels. Over 300 triggers compose a trig- ger menu which selects physics signatures such as electrons, muons, particle jets, etc. Each trigger consumes computing resources of the ATLAS trigger system and oine storage. The LHC instantaneous luminosity conditions, desired physics goals of the collaboration, and the limits of the trigger infrastructure determine the composition of the ATLAS trigger menu. We describe a trigger monitoring frame- work for computing the costs of individual trigger algorithms such as data request rates and CPU consumption. This framework has been used...

Evolution of User Analysis on the Grid in ATLAS

CERN Document Server

Legger, Federica; The ATLAS collaboration

2016-01-01

More than one thousand physicists analyse data collected by the ATLAS experiment at the Large Hadron Collider (LHC) at CERN through 150 computing facilities around the world. Efficient distributed analysis requires optimal resource usage and the interplay of several factors: robust grid and software infrastructures, system capability to adapt to different workloads. The continuous automatic validation of grid sites and the user support provided by a dedicated team of expert shifters have been proven to provide a solid distributed analysis system for ATLAS users. Based on the experience from the first run of the LHC, substantial improvements to the ATLAS computing system have been made to optimize both production and analysis workflows. These include the re-design of the production and data management systems, a new analysis data format and event model, and the development of common reduction and analysis frameworks. The impact of such changes on the distributed analysis system is evaluated. More than 100 mill...

A Step Towards A Computing Grid For The LHC Experiments ATLAS Data Challenge 1

CERN Document Server

Sturrock, R; Epp, B; Ghete, V M; Kuhn, D; Mello, A G; Caron, B; Vetterli, M C; Karapetian, G V; Martens, K; Agarwal, A; Poffenberger, P R; McPherson, R A; Sobie, R J; Amstrong, S; Benekos, N C; Boisvert, V; Boonekamp, M; Brandt, S; Casado, M P; Elsing, M; Gianotti, F; Goossens, L; Grote, M; Hansen, J B; Mair, K; Nairz, A; Padilla, C; Poppleton, A; Poulard, G; Richter-Was, Elzbieta; Rosati, S; Schörner-Sadenius, T; Wengler, T; Xu, G F; Ping, J L; Chudoba, J; Kosina, J; Lokajícek, M; Svec, J; Tas, P; Hansen, J R; Lytken, E; Nielsen, J L; Wäänänen, A; Tapprogge, Stefan; Calvet, D; Albrand, S; Collot, J; Fulachier, J; Ledroit-Guillon, F; Ohlsson-Malek, F; Viret, S; Wielers, M; Bernardet, K; Corréard, S; Rozanov, A; De Vivie de Régie, J B; Arnault, C; Bourdarios, C; Hrivnác, J; Lechowski, M; Parrour, G; Perus, A; Rousseau, D; Schaffer, A; Unal, G; Derue, F; Chevalier, L; Hassani, S; Laporte, J F; Nicolaidou, R; Pomarède, D; Virchaux, M; Nesvadba, N; Baranov, S; Putzer, A; Khonich, A; Duckeck, G; Schieferdecker, P; Kiryunin, A E; Schieck, J; Lagouri, T; Duchovni, E; Levinson, L; Schrager, D; Negri, G; Bilokon, H; Spogli, L; Barberis, D; Parodi, F; Cataldi, G; Gorini, E; Primavera, M; Spagnolo, S; Cavalli, D; Heldmann, M; Lari, T; Perini, L; Rebatto, D; Resconi, S; Tatarelli, F; Vaccarossa, L; Biglietti, M; Carlino, G; Conventi, F; Doria, A; Merola, L; Polesello, G; Vercesi, V; De Salvo, A; Di Mattia, A; Luminari, L; Nisati, A; Reale, M; Testa, M; Farilla, A; Verducci, M; Cobal, M; Santi, L; Hasegawa, Y; Ishino, M; Mashimo, T; Matsumoto, H; Sakamoto, H; Tanaka, J; Ueda, I; Bentvelsen, Stanislaus Cornelius Maria; Fornaini, A; Gorfine, G; Groep, D; Templon, J; Köster, L J; Konstantinov, A; Myklebust, T; Ould-Saada, F; Bold, T; Kaczmarska, A; Malecki, P; Szymocha, T; Turala, M; Kulchitskii, Yu A; Khoreauli, G; Gromova, N; Tsulaia, V; Minaenko, A A; Rudenko, R; Slabospitskaya, E; Solodkov, A; Gavrilenko, I; Nikitine, N; Sivoklokov, S Yu; Toms, K; Zalite, A; Zalite, Yu; Kervesan, B; Bosman, M; González, S; Sánchez, J; Salt, J; Andersson, N; Nixon, L; Eerola, Paule Anna Mari; Kónya, B; Smirnova, O G; Sandgren, A; Ekelöf, T J C; Ellert, M; Gollub, N; Hellman, S; Lipniacka, A; Corso-Radu, A; Pérez-Réale, V; Lee, S C; CLin, S C; Ren, Z L; Teng, P K; Faulkner, P J W; O'Neale, S W; Watson, A; Brochu, F; Lester, C; Thompson, S; Kennedy, J; Bouhova-Thacker, E; Henderson, R; Jones, R; Kartvelishvili, V G; Smizanska, M; Washbrook, A J; Drohan, J; Konstantinidis, N P; Moyse, E; Salih, S; Loken, J; Baines, J T M; Candlin, D; Candlin, R; Clifft, R; Li, W; McCubbin, N A; George, S; Lowe, A; Buttar, C; Dawson, I; Moraes, A; Tovey, Daniel R; Gieraltowski, J; Malon, D; May, E; LeCompte, T J; Vaniachine, A; Adams, D L; Assamagan, Ketevi A; Baker, R; Deng, W; Fine, V; Fisyak, Yu; Gibbard, B; Ma, H; Nevski, P; Paige, F; Rajagopalan, S; Smith, J; Undrus, A; Wenaus, T; Yu, D; Calafiura, P; Canon, S; Costanzo, D; Hinchliffe, Ian; Lavrijsen, W; Leggett, C; Marino, M; Quarrie, D R; Sakrejda, I; Stravopoulos, G; Tull, C; Loch, P; Youssef, S; Shank, J T; Engh, D; Frank, E; Sen-Gupta, A; Gardner, R; Meritt, F; Smirnov, Y; Huth, J; Grundhoefer, L; Luehring, F C; Goldfarb, S; Severini, H; Skubic, P L; Gao, Y; Ryan, T; De, K; Sosebee, M; McGuigan, P; Ozturk, N

2004-01-01

The ATLAS Collaboration at CERN is preparing for the data taking and analysis at the LHC that will start in 2007. Therefore, a series of Data Challenges was started in 2002 whose goals are the validation of the Computing Model, of the complete software suite, of the data model, and to ensure the correctness of the technical choices to be made for the final offline computing environment. A major feature of the first Data Challenge (DC1) was the preparation and the deployment of the software required for the production of large event samples as a worldwide distributed activity. It should be noted that it was not an option to "run the complete production at CERN" even if we had wanted to; the resources were not available at CERN to carry out the production on a reasonable time-scale. The great challenge of organising and carrying out this large-scale production at a significant number of sites around the world had therefore to be faced. However, the benefits of this are manifold: apart from realising the require...

Multiple brain atlas database and atlas-based neuroimaging system.

Science.gov (United States)

Nowinski, W L; Fang, A; Nguyen, B T; Raphel, J K; Jagannathan, L; Raghavan, R; Bryan, R N; Miller, G A

1997-01-01

For the purpose of developing multiple, complementary, fully labeled electronic brain atlases and an atlas-based neuroimaging system for analysis, quantification, and real-time manipulation of cerebral structures in two and three dimensions, we have digitized, enhanced, segmented, and labeled the following print brain atlases: Co-Planar Stereotaxic Atlas of the Human Brain by Talairach and Tournoux, Atlas for Stereotaxy of the Human Brain by Schaltenbrand and Wahren, Referentially Oriented Cerebral MRI Anatomy by Talairach and Tournoux, and Atlas of the Cerebral Sulci by Ono, Kubik, and Abernathey. Three-dimensional extensions of these atlases have been developed as well. All two- and three-dimensional atlases are mutually preregistered and may be interactively registered with an actual patient's data. An atlas-based neuroimaging system has been developed that provides support for reformatting, registration, visualization, navigation, image processing, and quantification of clinical data. The anatomical index contains about 1,000 structures and over 400 sulcal patterns. Several new applications of the brain atlas database also have been developed, supported by various technologies such as virtual reality, the Internet, and electronic publishing. Fusion of information from multiple atlases assists the user in comprehensively understanding brain structures and identifying and quantifying anatomical regions in clinical data. The multiple brain atlas database and atlas-based neuroimaging system have substantial potential impact in stereotactic neurosurgery and radiotherapy by assisting in visualization and real-time manipulation in three dimensions of anatomical structures, in quantitative neuroradiology by allowing interactive analysis of clinical data, in three-dimensional neuroeducation, and in brain function studies.

26th February 2009 - US Google Vice President and Chief Internet Evangelist V. Cerf signing the guest book with Director for research and Computing S. Bertolucci; visiting ATLAS control room and experimental area with Collaboration Spokesperson P. Jenni.

CERN Multimedia

Maximilien Brice

2009-01-01

HI-0902038 05: IT Department Head, F. Hemmer; US Google Vice President and Chief Internet Evangelist V. Cerf; Computing Security Officer and Colloquium Convenor D. R. Myers; Member of the Internet Society Advisory Council F. Flückiger; Director for Research and Scientific Computing, S. Bertolucci ; Honorary Staff Member, B. Segal. HI-0902038 16: Computing Security Officer and Colloquium Convenor D. R. Myers; UC Irvine, ATLAS Deputy Spokesperson elect A. J. Lankford; US Google Vice President and Chief Internet Evangelist V. Cerf; ATLAS Collaboration Spokesperson P. Jenni; IT Department Head, F. Hemmer.

Virtual Machine Logbook - Enabling virtualization for ATLAS

International Nuclear Information System (INIS)

Yao Yushu; Calafiura, Paolo; Leggett, Charles; Poffet, Julien; Cavalli, Andrea; Frederic, Bapst

2010-01-01

ATLAS software has been developed mostly on CERN linux cluster lxplus or on similar facilities at the experiment Tier 1 centers. The fast rise of virtualization technology has the potential to change this model, turning every laptop or desktop into an ATLAS analysis platform. In the context of the CernVM project we are developing a suite of tools and CernVM plug-in extensions to promote the use of virtualization for ATLAS analysis and software development. The Virtual Machine Logbook (VML), in particular, is an application to organize work of physicists on multiple projects, logging their progress, and speeding up ''context switches'' from one project to another. An important feature of VML is the ability to share with a single 'click' the status of a given project with other colleagues. VML builds upon the save and restore capabilities of mainstream virtualization software like VMware, and provides a technology-independent client interface to them. A lot of emphasis in the design and implementation has gone into optimizing the save and restore process to makepractical to store many VML entries on a typical laptop disk or to share a VML entry over the network. At the same time, taking advantage of CernVM's plugin capabilities, we are extending the CernVM platform to help increase the usability of ATLAS software. For example, we added the ability to start the ATLAS event display on any computer running CernVM simply by clicking a button in a web browser. We want to integrate seamlessly VML with CernVM unique file system design to distribute efficiently ATLAS software on every physicist computer. The CernVM File System (CVMFS) download files on-demand via HTTP, and cache it locally for future use. This reduces by one order of magnitude the download sizes, making practical for a developer to work with multiple software releases on a virtual machine.

MRI-based treatment planning with pseudo CT generated through atlas registration

International Nuclear Information System (INIS)

Uh, Jinsoo; Merchant, Thomas E.; Hua, Chiaho; Li, Yimei; Li, Xingyu

2014-01-01

Purpose: To evaluate the feasibility and accuracy of magnetic resonance imaging (MRI)-based treatment planning using pseudo CTs generated through atlas registration. Methods: A pseudo CT, providing electron density information for dose calculation, was generated by deforming atlas CT images previously acquired on other patients. The authors tested 4 schemes of synthesizing a pseudo CT from single or multiple deformed atlas images: use of a single arbitrarily selected atlas, arithmetic mean process using 6 atlases, and pattern recognition with Gaussian process (PRGP) using 6 or 12 atlases. The required deformation for atlas CT images was derived from a nonlinear registration of conjugated atlas MR images to that of the patient of interest. The contrasts of atlas MR images were adjusted by histogram matching to reduce the effect of different sets of acquisition parameters. For comparison, the authors also tested a simple scheme assigning the Hounsfield unit of water to the entire patient volume. All pseudo CT generating schemes were applied to 14 patients with common pediatric brain tumors. The image similarity of real patient-specific CT and pseudo CTs constructed by different schemes was compared. Differences in computation times were also calculated. The real CT in the treatment planning system was replaced with the pseudo CT, and the dose distribution was recalculated to determine the difference. Results: The atlas approach generally performed better than assigning a bulk CT number to the entire patient volume. Comparing atlas-based schemes, those using multiple atlases outperformed the single atlas scheme. For multiple atlas schemes, the pseudo CTs were similar to the real CTs (correlation coefficient, 0.787–0.819). The calculated dose distribution was in close agreement with the original dose. Nearly the entire patient volume (98.3%–98.7%) satisfied the criteria of chi-evaluation (<2% maximum dose and 2 mm range). The dose to 95% of the volume and the

MRI-based treatment planning with pseudo CT generated through atlas registration

Energy Technology Data Exchange (ETDEWEB)

Uh, Jinsoo, E-mail: jinsoo.uh@stjude.org; Merchant, Thomas E.; Hua, Chiaho [Department of Radiological Sciences, St. Jude Children' s Research Hospital, Memphis, Tennessee 38105 (United States); Li, Yimei; Li, Xingyu [Department of Biostatistics, St. Jude Children' s Research Hospital, Memphis, Tennessee 38105 (United States)

2014-05-15

Purpose: To evaluate the feasibility and accuracy of magnetic resonance imaging (MRI)-based treatment planning using pseudo CTs generated through atlas registration. Methods: A pseudo CT, providing electron density information for dose calculation, was generated by deforming atlas CT images previously acquired on other patients. The authors tested 4 schemes of synthesizing a pseudo CT from single or multiple deformed atlas images: use of a single arbitrarily selected atlas, arithmetic mean process using 6 atlases, and pattern recognition with Gaussian process (PRGP) using 6 or 12 atlases. The required deformation for atlas CT images was derived from a nonlinear registration of conjugated atlas MR images to that of the patient of interest. The contrasts of atlas MR images were adjusted by histogram matching to reduce the effect of different sets of acquisition parameters. For comparison, the authors also tested a simple scheme assigning the Hounsfield unit of water to the entire patient volume. All pseudo CT generating schemes were applied to 14 patients with common pediatric brain tumors. The image similarity of real patient-specific CT and pseudo CTs constructed by different schemes was compared. Differences in computation times were also calculated. The real CT in the treatment planning system was replaced with the pseudo CT, and the dose distribution was recalculated to determine the difference. Results: The atlas approach generally performed better than assigning a bulk CT number to the entire patient volume. Comparing atlas-based schemes, those using multiple atlases outperformed the single atlas scheme. For multiple atlas schemes, the pseudo CTs were similar to the real CTs (correlation coefficient, 0.787–0.819). The calculated dose distribution was in close agreement with the original dose. Nearly the entire patient volume (98.3%–98.7%) satisfied the criteria of chi-evaluation (<2% maximum dose and 2 mm range). The dose to 95% of the volume and the

The next generation PanDA Pilot for and beyond the ATLAS experiment

CERN Document Server

Nilsson, Paul; The ATLAS collaboration

2018-01-01

The Production and Distributed Analysis system (PanDA) is a pilot-based workload management system that was originally designed for the ATLAS Experiment at the LHC to operate on grid sites. Since the coming LHC data taking runs will require more resources than grid computing alone can provide, the various LHC experiments are engaged in an ambitious program to extend the computing model to include opportunistically used resources such as High Performance Computers (HPCs), clouds and volunteer computers. To this end, PanDA is being extended beyond grids and ATLAS to be used on the new types of resources as well as by other experiments. A new key component is being developed, the next generation PanDA Pilot (Pilot 2). Pilot 2 is a complete rewrite of the original PanDA Pilot which has been used in the ATLAS Experiment for over a decade. The new Pilot architecture follows a component-based approach which improves system flexibility, enables a clear workflow control, evolves the system according to modern function...

Interactive videodisk atlas of knee anatomy

International Nuclear Information System (INIS)

McEnery, K.W.; Woods, J.W.; Glenn, W.F.; Rauschning, W.

1987-01-01

An interactive, computer-assisted atlas of knee anatomy has been developed. MR and CT images from normal volunteers and cryomicrotomed anatomic images were recorded on a laser viodeodisk. Computer software allows movement through the knee and correlation of radiographic images in the coronal, axial, and sagittal planes. Computer graphics are superimposed on the videodisk images. A high-resolution color graphics, touch-screen monitor is included in the computer system. Learning modules allow for rapid identification of specific structure by touching their location on the screen. Computer-created testing modules are available that provide for self-assessment

Benefits and performance of ATLAS approaches to utilizing opportunistic resources

CERN Document Server

Filip\\v{c}i\\v{c}, Andrej; The ATLAS collaboration

2016-01-01

ATLAS has been extensively exploring possibilities of using computing resources extending beyond conventional grid sites in the WLCG fabric to deliver as many computing cycles as possible and thereby enhance the significance of the Monte-Carlo samples to deliver better physics results. The difficulties of using such opportunistic resources come from architectural differences such as unavailability of grid services, the absence of network connectivity on worker nodes or inability to use standard authorization protocols. Nevertheless, ATLAS has been extremely successful in running production payloads on a variety of sites, thanks largely to the job execution workflow design in which the job assignment, input data provisioning and execution steps are clearly separated and can be offloaded to custom services. To transparently include the opportunistic sites in the ATLAS central production system, several models with supporting services have been developed to mimic the functionality of a full WLCG site. Some are e...

Computational neuroanatomy: mapping cell-type densities in the mouse brain, simulations from the Allen Brain Atlas

Science.gov (United States)

Grange, Pascal

2015-09-01

The Allen Brain Atlas of the adult mouse (ABA) consists of digitized expression profiles of thousands of genes in the mouse brain, co-registered to a common three-dimensional template (the Allen Reference Atlas).This brain-wide, genome-wide data set has triggered a renaissance in neuroanatomy. Its voxelized version (with cubic voxels of side 200 microns) is available for desktop computation in MATLAB. On the other hand, brain cells exhibit a great phenotypic diversity (in terms of size, shape and electrophysiological activity), which has inspired the names of some well-studied cell types, such as granule cells and medium spiny neurons. However, no exhaustive taxonomy of brain cell is available. A genetic classification of brain cells is being undertaken, and some cell types have been chraracterized by their transcriptome profiles. However, given a cell type characterized by its transcriptome, it is not clear where else in the brain similar cells can be found. The ABA can been used to solve this region-specificity problem in a data-driven way: rewriting the brain-wide expression profiles of all genes in the atlas as a sum of cell-type-specific transcriptome profiles is equivalent to solving a quadratic optimization problem at each voxel in the brain. However, the estimated brain-wide densities of 64 cell types published recently were based on one series of co-registered coronal in situ hybridization (ISH) images per gene, whereas the online ABA contains several image series per gene, including sagittal ones. In the presented work, we simulate the variability of cell-type densities in a Monte Carlo way by repeatedly drawing a random image series for each gene and solving the optimization problem. This yields error bars on the region-specificity of cell types.

Atlas2 Cloud: a framework for personal genome analysis in the cloud.

Science.gov (United States)

Evani, Uday S; Challis, Danny; Yu, Jin; Jackson, Andrew R; Paithankar, Sameer; Bainbridge, Matthew N; Jakkamsetti, Adinarayana; Pham, Peter; Coarfa, Cristian; Milosavljevic, Aleksandar; Yu, Fuli

2012-01-01

Until recently, sequencing has primarily been carried out in large genome centers which have invested heavily in developing the computational infrastructure that enables genomic sequence analysis. The recent advancements in next generation sequencing (NGS) have led to a wide dissemination of sequencing technologies and data, to highly diverse research groups. It is expected that clinical sequencing will become part of diagnostic routines shortly. However, limited accessibility to computational infrastructure and high quality bioinformatic tools, and the demand for personnel skilled in data analysis and interpretation remains a serious bottleneck. To this end, the cloud computing and Software-as-a-Service (SaaS) technologies can help address these issues. We successfully enabled the Atlas2 Cloud pipeline for personal genome analysis on two different cloud service platforms: a community cloud via the Genboree Workbench, and a commercial cloud via the Amazon Web Services using Software-as-a-Service model. We report a case study of personal genome analysis using our Atlas2 Genboree pipeline. We also outline a detailed cost structure for running Atlas2 Amazon on whole exome capture data, providing cost projections in terms of storage, compute and I/O when running Atlas2 Amazon on a large data set. We find that providing a web interface and an optimized pipeline clearly facilitates usage of cloud computing for personal genome analysis, but for it to be routinely used for large scale projects there needs to be a paradigm shift in the way we develop tools, in standard operating procedures, and in funding mechanisms.

Trigger Menu-aware Monitoring for the ATLAS experiment

CERN Document Server

Hoad, Xanthe; The ATLAS collaboration

2016-01-01

Changes in the trigger menu, the online algorithmic event-selection of the ATLAS experiment at the LHC in response to luminosity and detector changes are followed by adjustments in their monitoring system. This is done to ensure that the collected data is useful, and can be properly reconstructed at Tier-0, the first level of the computing grid. During Run 1, ATLAS deployed monitoring updates with the installation of new software releases at Tier-0. This created unnecessary overhead for developers and operators, and unavoidably led to different releases for the data-taking and the monitoring setup. We present a "trigger menu-aware" monitoring system designed for the ATLAS Run 2 data-taking. The new monitoring system aims to simplify the ATLAS operational workflows, and allows for easy and flexible monitoring configuration changes at the Tier-0 site via an Oracle DB interface. We present the design and the implementation of the menu-aware monitoring, along with lessons from the operational experience of the ne...

Renewable energy atlas of the United States.

Energy Technology Data Exchange (ETDEWEB)

Kuiper, J.A.; Hlava, K.Greenwood, H.; Carr, A. (Environmental Science Division)

2012-05-01

The Renewable Energy Atlas (Atlas) of the United States is a compilation of geospatial data focused on renewable energy resources, federal land ownership, and base map reference information. It is designed for the U.S. Department of Agriculture Forest Service (USFS) and other federal land management agencies to evaluate existing and proposed renewable energy projects. Much of the content of the Atlas was compiled at Argonne National Laboratory (Argonne) to support recent and current energy-related Environmental Impact Statements and studies, including the following projects: (1) West-wide Energy Corridor Programmatic Environmental Impact Statement (PEIS) (BLM 2008); (2) Draft PEIS for Solar Energy Development in Six Southwestern States (DOE/BLM 2010); (3) Supplement to the Draft PEIS for Solar Energy Development in Six Southwestern States (DOE/BLM 2011); (4) Upper Great Plains Wind Energy PEIS (WAPA/USFWS 2012, in progress); and (5) Energy Transport Corridors: The Potential Role of Federal Lands in States Identified by the Energy Policy Act of 2005, Section 368(b) (in progress). This report explains how to add the Atlas to your computer and install the associated software; describes each of the components of the Atlas; lists the Geographic Information System (GIS) database content and sources; and provides a brief introduction to the major renewable energy technologies.

Big Data Analytics Tools as Applied to ATLAS Event Data

CERN Document Server

Vukotic, Ilija; The ATLAS collaboration

2016-01-01

Big Data technologies have proven to be very useful for storage, processing and visualization of derived metrics associated with ATLAS distributed computing (ADC) services. Log file data and database records, and metadata from a diversity of systems have been aggregated and indexed to create an analytics platform for ATLAS ADC operations analysis. Dashboards, wide area data access cost metrics, user analysis patterns, and resource utilization efficiency charts are produced flexibly through queries against a powerful analytics cluster. Here we explore whether these techniques and analytics ecosystem can be applied to add new modes of open, quick, and pervasive access to ATLAS event data so as to simplify access and broaden the reach of ATLAS public data to new communities of users. An ability to efficiently store, filter, search and deliver ATLAS data at the event and/or sub-event level in a widely supported format would enable or significantly simplify usage of big data, statistical and machine learning tools...

Computing infrastructure for ATLAS data analysis in the Italian Grid cloud

International Nuclear Information System (INIS)

Andreazza, A; Annovi, A; Martini, A; Barberis, D; Brunengo, A; Corosu, M; Campana, S; Girolamo, A Di; Carlino, G; Doria, A; Merola, L; Musto, E; Ciocca, C; Jha, M K; Cobal, M; Pascolo, F; Salvo, A De; Luminari, L; Sanctis, U De; Galeazzi, F

2011-01-01

ATLAS data are distributed centrally to Tier-1 and Tier-2 sites. The first stages of data selection and analysis take place mainly at Tier-2 centres, with the final, iterative and interactive, stages taking place mostly at Tier-3 clusters. The Italian ATLAS cloud consists of a Tier-1, four Tier-2s, and Tier-3 sites at each institute. Tier-3s that are grid-enabled are used to test code that will then be run on a larger scale at Tier-2s. All Tier-3s offer interactive data access to their users and the possibility to run PROOF. This paper describes the hardware and software infrastructure choices taken, the operational experience after 10 months of LHC data, and discusses site performances.

Multi-threaded ATLAS Simulation on Intel Knights Landing Processors

CERN Document Server

Farrell, Steven; The ATLAS collaboration; Calafiura, Paolo; Leggett, Charles

2016-01-01

The Knights Landing (KNL) release of the Intel Many Integrated Core (MIC) Xeon Phi line of processors is a potential game changer for HEP computing. With 72 cores and deep vector registers, the KNL cards promise significant performance benefits for highly-parallel, compute-heavy applications. Cori, the newest supercomputer at the National Energy Research Scientific Computing Center (NERSC), will be delivered to its users in two phases with the first phase online now and the second phase expected in mid-2016. Cori Phase 2 will be based on the KNL architecture and will contain over 9000 compute nodes with 96GB DDR4 memory. ATLAS simulation with the multithreaded Athena Framework (AthenaMT) is a great use-case for the KNL architecture and supercomputers like Cori. Simulation jobs have a high ratio of CPU computation to disk I/O and have been shown to scale well in multi-threading and across many nodes. In this presentation we will give an overview of the ATLAS simulation application with details on its multi-thr...

Analysis of empty ATLAS pilot jobs

CERN Document Server

Love, Peter; The ATLAS collaboration

2016-01-01

The pilot model used by the ATLAS production system has been in use for many years. The model has proven to be a success with many advantages over push models. However one of the negative side-effects of using a pilot model is the presence of 'empty pilots' running on sites which consume a small amount of walltime and not running a useful payload job. The impact on a site can be significant with previous studies showing a total 0.5% walltime usage with no benefit to either the site or to ATLAS. Another impact is the number of empty pilots being processed by a site's Compute Element and batch system which can be 5% of the total number of pilots being handled. In this paper we review the latest statistics using both ATLAS and site data and highlight edge cases where the number of empty pilots dominate. We also study the effect of tuning the pilot factories to reduce the number of empty pilots.

Using containers with ATLAS offline software

CERN Document Server

Vogel, Marcelo; The ATLAS collaboration

2017-01-01

This paper describes the deployment of ATLAS offline software in containers for software development. For this we are using Docker, which is a lightweight virtualization technology that encapsulates a piece of software inside a complete file system. The deployment of offline releases via containers removes the strict requirement of compatibility between the runtime environment needed for job execution and the configuration of worker nodes at computing sites. If these two are decoupled from each other, sites can upgrade their nodes whenever and however they see fit. In this work, ATLAS software is distributed in containers either via the CernVM File System (CVMFS) or by means of a full ATLAS offline release installation. In software development, separating the build and runtime environment from the development environment allows users to take advantage of many modern code development tools that may not be available in production runtime setups like SLC6. It also frees developers from depending on resources lik...

First-year experience with the ATLAS online monitoring framework

International Nuclear Information System (INIS)

Corso-Radu, A

2010-01-01

ATLAS is one of the four experiments in the Large Hadron Collider (LHC) at CERN, which has been put in operation this year. The challenging experimental environment and the extreme detector complexity required development of a highly scalable distributed monitoring framework, which is currently being used to monitor the quality of the data being taken as well as operational conditions of the hardware and software elements of the detector, trigger and data acquisition systems. At the moment the ATLAS Trigger/DAQ system is distributed over more than 1000 computers, which is about one third of the final ATLAS size. At every minute of an ATLAS data taking session the monitoring framework serves several thousands physics events to monitoring data analysis applications, handles more than 4 million histograms updates coming from more than 4 thousands applications, executes 10 thousands advanced data quality checks for a subset of those histograms, displays histograms and results of these checks on several dozens of monitors installed in main and satellite ATLAS control rooms. This note presents the overview of the online monitoring software framework, and describes the experience, which was gained during an extensive commissioning period as well as at the first phase of LHC beam in September 2008. Performance results, obtained on the current ATLAS DAQ system will also be presented, showing that the performance of the framework is adequate for the final ATLAS system.

The ATLAS Analysis Architecture

International Nuclear Information System (INIS)

Cranmer, K.S.

2008-01-01

We present an overview of the ATLAS analysis architecture including the relevant aspects of the computing model and the major architectural aspects of the Athena framework. Emphasis will be given to the interplay between the analysis use cases and the technical aspects of the architecture including the design of the event data model, transient-persistent separation, data reduction strategies, analysis tools, and ROOT interoperability

Spracovanie dát na experimente ATLAS

Czech Academy of Sciences Publication Activity Database

Marčišovský, Michal; Kubeš, T.; Chudoba, Jiří

2008-01-01

Roč. 58, č. 6 (2008), 354-359 ISSN 0009-0700 R&D Projects: GA MŠk LA08032 Institutional research plan: CEZ:AV0Z10100502 Keywords : Atlas * computing * DCS * Grid Subject RIV: BF - Elementary Particles and High Energy Physics

High-Performance Scalable Information Service for the ATLAS Experiment

International Nuclear Information System (INIS)

Kolos, S; Boutsioukis, G; Hauser, R

2012-01-01

The ATLAS[1] experiment is operated by a highly distributed computing system which is constantly producing a lot of status information which is used to monitor the experiment operational conditions as well as to assess the quality of the physics data being taken. For example the ATLAS High Level Trigger(HLT) algorithms are executed on the online computing farm consisting from about 1500 nodes. Each HLT algorithm is producing few thousands histograms, which have to be integrated over the whole farm and carefully analyzed in order to properly tune the event rejection. In order to handle such non-physics data the Information Service (IS) facility has been developed in the scope of the ATLAS Trigger and Data Acquisition (TDAQ)[2] project. The IS provides a high-performance scalable solution for information exchange in distributed environment. In the course of an ATLAS data taking session the IS handles about a hundred gigabytes of information which is being constantly updated with the update interval varying from a second to a few tens of seconds. IS provides access to any information item on request as well as distributing notification to all the information subscribers. In the latter case IS subscribers receive information within a few milliseconds after it was updated. IS can handle arbitrary types of information, including histograms produced by the HLT applications, and provides C++, Java and Python API. The Information Service is a unique source of information for the majority of the online monitoring analysis and GUI applications used to control and monitor the ATLAS experiment. Information Service provides streaming functionality allowing efficient replication of all or part of the managed information. This functionality is used to duplicate the subset of the ATLAS monitoring data to the CERN public network with a latency of a few milliseconds, allowing efficient real-time monitoring of the data taking from outside the protected ATLAS network. Each information

Implementation of the ATLAS trigger within the multi-threaded software framework AthenaMT

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00225867; The ATLAS collaboration

2017-01-01

We present an implementation of the ATLAS High Level Trigger, HLT, that provides parallel execution of trigger algorithms within the ATLAS multithreaded software framework, AthenaMT. This development will enable the ATLAS HLT to meet future challenges due to the evolution of computing hardware and upgrades of the Large Hadron Collider, LHC, and ATLAS Detector. During the LHC data-taking period starting in 2021, luminosity will reach up to three times the original design value. Luminosity will increase further, to up to 7.5 times the design value, in 2026 following LHC and ATLAS upgrades. This includes an upgrade of the ATLAS trigger architecture that will result in an increase in the HLT input rate by a factor of 4 to 10 compared to the current maximum rate of 100 kHz. The current ATLAS multiprocess framework, AthenaMP, manages a number of processes that each execute algorithms sequentially for different events. AthenaMT will provide a fully multi-threaded environment that will additionally enable concurrent ...

ATLAS Data Challenges - A Collaborative Worldwide Activity

CERN Multimedia

Poulard, G

The goals of the ATLAS Data Challenges (DC) are the validation of the Computing Model, of the complete software suite, of the data model, and to ensure the correctness of the technical choices to be made. It is understood that these Data Challenges should be of increasing complexity and that their results will be used as input for a Computing TDR and for preparing an MoU in due time. A major feature of the current computing activities (DC1) in ATLAS is the preparation and deployment of the software required for the production of large event samples for the High Level Trigger (HLT) and physics communities, and the actual production of those samples. It should be noted that it is not an option to "run everything at CERN" even if we wanted to; the resources are not available at CERN to carry out the production on a reasonable time-scale. We have therefore had to face the great challenge of organising and then carrying out this large-scale production at a significant number of sites around the world. However, th...

Advanced technologies for scalable ATLAS conditions database access on the grid

International Nuclear Information System (INIS)

Basset, R; Canali, L; Girone, M; Hawkings, R; Valassi, A; Viegas, F; Dimitrov, G; Nevski, P; Vaniachine, A; Walker, R; Wong, A

2010-01-01

During massive data reprocessing operations an ATLAS Conditions Database application must support concurrent access from numerous ATLAS data processing jobs running on the Grid. By simulating realistic work-flow, ATLAS database scalability tests provided feedback for Conditions Db software optimization and allowed precise determination of required distributed database resources. In distributed data processing one must take into account the chaotic nature of Grid computing characterized by peak loads, which can be much higher than average access rates. To validate database performance at peak loads, we tested database scalability at very high concurrent jobs rates. This has been achieved through coordinated database stress tests performed in series of ATLAS reprocessing exercises at the Tier-1 sites. The goal of database stress tests is to detect scalability limits of the hardware deployed at the Tier-1 sites, so that the server overload conditions can be safely avoided in a production environment. Our analysis of server performance under stress tests indicates that Conditions Db data access is limited by the disk I/O throughput. An unacceptable side-effect of the disk I/O saturation is a degradation of the WLCG 3D Services that update Conditions Db data at all ten ATLAS Tier-1 sites using the technology of Oracle Streams. To avoid such bottlenecks we prototyped and tested a novel approach for database peak load avoidance in Grid computing. Our approach is based upon the proven idea of pilot job submission on the Grid: instead of the actual query, an ATLAS utility library sends to the database server a pilot query first.

Status and Evolution of ATLAS Workload Management System PanDA

CERN Document Server

AUTHOR|(CDS)2067365; The ATLAS collaboration

2012-01-01

The ATLAS experiment at the LHC uses a sophisticated workload management system, PanDA, to provide access for thousands of physicists to distributed computing resources of unprecedented scale. This system has proved to be robust and scalable during three years of LHC operations. We describe the design and performance of PanDA in ATLAS. The features which make PanDA successful in ATLAS could be applicable to other exabyte scale scientific projects. We describe plans to evolve PanDA towards a general workload management system for the new BigData initiative announced by the US government. Other planned future improvements to PanDA will also be described

PD2P: PanDA Dynamic Data Placement for ATLAS

International Nuclear Information System (INIS)

Maeno, T; Panitkin, S; De, K

2012-01-01

The PanDA (Production and Distributed Analysis) system plays a key role in the ATLAS distributed computing infrastructure. PanDA is the ATLAS workload management system for processing all Monte-Carlo (MC) simulation and data reprocessing jobs in addition to user and group analysis jobs. The PanDA Dynamic Data Placement (PD2P) system has been developed to cope with difficulties of data placement for ATLAS. We will describe the design of the new system, its performance during the past year of data taking, dramatic improvements it has brought about in the efficient use of storage and processing resources, and plans for the future.

Fusion set selection with surrogate metric in multi-atlas based image segmentation

International Nuclear Information System (INIS)

Zhao, Tingting; Ruan, Dan

2016-01-01

Multi-atlas based image segmentation sees unprecedented opportunities but also demanding challenges in the big data era. Relevant atlas selection before label fusion plays a crucial role in reducing potential performance loss from heterogeneous data quality and high computation cost from extensive data. This paper starts with investigating the image similarity metric (termed ‘surrogate’), an alternative to the inaccessible geometric agreement metric (termed ‘oracle’) in atlas relevance assessment, and probes into the problem of how to select the ‘most-relevant’ atlases and how many such atlases to incorporate. We propose an inference model to relate the surrogates and the oracle geometric agreement metrics. Based on this model, we quantify the behavior of the surrogates in mimicking oracle metrics for atlas relevance ordering. Finally, analytical insights on the choice of fusion set size are presented from a probabilistic perspective, with the integrated goal of including the most relevant atlases and excluding the irrelevant ones. Empirical evidence and performance assessment are provided based on prostate and corpus callosum segmentation. (paper)

Evaluation of atlas-based auto-segmentation software in prostate cancer patients

International Nuclear Information System (INIS)

Greenham, Stuart; Dean, Jenna; Fu, Cheuk Kuen Kenneth; Goman, Joanne; Mulligan, Jeremy; Tune, Deanna; Sampson, David; Westhuyzen, Justin; McKay, Michael

2014-01-01

The performance and limitations of an atlas-based auto-segmentation software package (ABAS; Elekta Inc.) was evaluated using male pelvic anatomy as the area of interest. Contours from 10 prostate patients were selected to create atlases in ABAS. The contoured regions of interest were created manually to align with published guidelines and included the prostate, bladder, rectum, femoral heads and external patient contour. Twenty-four clinically treated prostate patients were auto-contoured using a randomised selection of two, four, six, eight or ten atlases. The concordance between the manually drawn and computer-generated contours were evaluated statistically using Pearson's product–moment correlation coefficient (r) and clinically in a validated qualitative evaluation. In the latter evaluation, six radiation therapists classified the degree of agreement for each structure using seven clinically appropriate categories. The ABAS software generated clinically acceptable contours for the bladder, rectum, femoral heads and external patient contour. For these structures, ABAS-generated volumes were highly correlated with ‘as treated’ volumes, manually drawn; for four atlases, for example, bladder r = 0.988 (P < 0.001), rectum r = 0.739 (P < 0.001) and left femoral head r = 0.560 (P < 0.001). Poorest results were seen for the prostate (r = 0.401, P < 0.05) (four atlases); however this was attributed to the comparison prostate volume being contoured on magnetic resonance imaging (MRI) rather than computed tomography (CT) data. For all structures, increasing the number of atlases did not consistently improve accuracy. ABAS-generated contours are clinically useful for a range of structures in the male pelvis. Clinically appropriate volumes were created, but editing of some contours was inevitably required. The ideal number of atlases to improve generated automatic contours is yet to be determined

Evolution of the ATLAS Nightly Build System

International Nuclear Information System (INIS)

Undrus, A

2012-01-01

The ATLAS Nightly Build System is a major component in the ATLAS collaborative software organization, validation, and code approval scheme. For over 10 years of development it has evolved into a factory for automatic release production and grid distribution. The 50 multi-platform branches of ATLAS releases provide vast opportunities for testing new packages, verification of patches to existing software, and migration to new platforms and compilers for ATLAS code that currently contains 2200 packages with 4 million C++ and 1.4 million python scripting lines written by about 1000 developers. Recent development was focused on the integration of ATLAS Nightly Build and Installation systems. The nightly releases are distributed and validated and some are transformed into stable releases used for data processing worldwide. The ATLAS Nightly System is managed by the NICOS control tool on a computing farm with 50 powerful multiprocessor nodes. NICOS provides the fully automated framework for the release builds, testing, and creation of distribution kits. The ATN testing framework of the Nightly System runs unit and integration tests in parallel suites, fully utilizing the resources of multi-core machines, and provides the first results even before compilations complete. The NICOS error detection system is based on several techniques and classifies the compilation and test errors according to their severity. It is periodically tuned to place greater emphasis on certain software defects by highlighting the problems on NICOS web pages and sending automatic e-mail notifications to responsible developers. These and other recent developments will be presented and future plans will be described.

Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science

Science.gov (United States)

Klimentov, A.; De, K.; Jha, S.; Maeno, T.; Nilsson, P.; Oleynik, D.; Panitkin, S.; Wells, J.; Wenaus, T.

2016-10-01

The.LHC, operating at CERN, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe. ATLAS, one of the largest collaborations ever assembled in the sciences, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. While PanDA currently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, LHC data taking runs require more resources than grid can possibly provide. To alleviate these challenges, LHC experiments are engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with supercomputers in United States, in particular with Titan supercomputer at Oak Ridge Leadership Computing Facility. Current approach utilizes modified PanDA pilot framework for job submission to the supercomputers batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on LCFs multi-core worker nodes. This implementation was tested with a variety of Monte-Carlo workloads on several supercomputing platforms for ALICE and ATLAS experiments and it is in full pro duction for the ATLAS since September 2015. We will present our current accomplishments with running PanDA at supercomputers and demonstrate our ability to use PanDA as a portal independent of the

Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science

International Nuclear Information System (INIS)

Klimentov, A; Maeno, T; Nilsson, P; Panitkin, S; Wenaus, T; De, K; Oleynik, D; Jha, S; Wells, J

2016-01-01

The.LHC, operating at CERN, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe. ATLAS, one of the largest collaborations ever assembled in the sciences, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. While PanDA currently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, LHC data taking runs require more resources than grid can possibly provide. To alleviate these challenges, LHC experiments are engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with supercomputers in United States, in particular with Titan supercomputer at Oak Ridge Leadership Computing Facility. Current approach utilizes modified PanDA pilot framework for job submission to the supercomputers batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on LCFs multi-core worker nodes. This implementation was tested with a variety of Monte-Carlo workloads on several supercomputing platforms for ALICE and ATLAS experiments and it is in full pro duction for the ATLAS since September 2015. We will present our current accomplishments with running PanDA at supercomputers and demonstrate our ability to use PanDA as a portal independent of the

Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection

Energy Technology Data Exchange (ETDEWEB)

Zhuang, Xiahai, E-mail: zhuangxiahai@sjtu.edu.cn; Qian, Xiaohua [SJTU-CU International Cooperative Research Center, Department of Engineering Mechanics, School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240 (China); Bai, Wenjia; Shi, Wenzhe; Rueckert, Daniel [Biomedical Image Analysis Group, Department of Computing, Imperial College London, 180 Queens Gate, London SW7 2AZ (United Kingdom); Song, Jingjing; Zhan, Songhua [Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai 201203 (China); Lian, Yanyun [Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210 (China)

2015-07-15

Purpose: Cardiac computed tomography (CT) is widely used in clinical diagnosis of cardiovascular diseases. Whole heart segmentation (WHS) plays a vital role in developing new clinical applications of cardiac CT. However, the shape and appearance of the heart can vary greatly across different scans, making the automatic segmentation particularly challenging. The objective of this work is to develop and evaluate a multiatlas segmentation (MAS) scheme using a new atlas ranking and selection algorithm for automatic WHS of CT data. Research on different MAS strategies and their influence on WHS performance are limited. This work provides a detailed comparison study evaluating the impacts of label fusion, atlas ranking, and sizes of the atlas database on the segmentation performance. Methods: Atlases in a database were registered to the target image using a hierarchical registration scheme specifically designed for cardiac images. A subset of the atlases were selected for label fusion, according to the authors’ proposed atlas ranking criterion which evaluated the performance of each atlas by computing the conditional entropy of the target image given the propagated atlas labeling. Joint label fusion was used to combine multiple label estimates to obtain the final segmentation. The authors used 30 clinical cardiac CT angiography (CTA) images to evaluate the proposed MAS scheme and to investigate different segmentation strategies. Results: The mean WHS Dice score of the proposed MAS method was 0.918 ± 0.021, and the mean runtime for one case was 13.2 min on a workstation. This MAS scheme using joint label fusion generated significantly better Dice scores than the other label fusion strategies, including majority voting (0.901 ± 0.276, p < 0.01), locally weighted voting (0.905 ± 0.0247, p < 0.01), and probabilistic patch-based fusion (0.909 ± 0.0249, p < 0.01). In the atlas ranking study, the proposed criterion based on conditional entropy yielded a performance curve

ATLAS utilisation of the Czech national HPC center

CERN Document Server

Svatos, Michal; The ATLAS collaboration

2018-01-01

The Czech national HPC center IT4Innovations located in Ostrava provides two HPC systems, Anselm and Salomon. The Salomon HPC is amongst the hundred most powerful supercomputers on Earth since its commissioning in 2015. Both clusters were tested for usage by the ATLAS experiment for running simulation jobs. Several thousand core hours were allocated to the project for tests, but the main aim is to use free resources waiting for large parallel jobs of other users. Multiple strategies for ATLAS job execution were tested on the Salomon and Anselm HPCs. The solution described herein is based on the ATLAS experience with other HPC sites. ARC Compute Element (ARC-CE) installed at the grid site in Prague is used for job submission to Salomon. The ATLAS production system submits jobs to the ARC-CE via ARC Control Tower (aCT). The ARC-CE processes job requirements from aCT and creates a script for a batch system which is then executed via ssh. Sshfs is used to share scripts and input files between the site and the HPC...

Fast and robust multi-atlas segmentation of brain magnetic resonance images

DEFF Research Database (Denmark)

Lötjönen, Jyrki Mp; Wolz, Robin; Koikkalainen, Juha R

2010-01-01

We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead of stand......We introduce an optimised pipeline for multi-atlas brain MRI segmentation. Both accuracy and speed of segmentation are considered. We study different similarity measures used in non-rigid registration. We show that intensity differences for intensity normalised images can be used instead...... of standard normalised mutual information in registration without compromising the accuracy but leading to threefold decrease in the computation time. We study and validate also different methods for atlas selection. Finally, we propose two new approaches for combining multi-atlas segmentation and intensity...

Methods and computing challenges of the realistic simulation of physics events in the presence of pile-up in the ATLAS experiment

CERN Document Server

Chapman, J D; The ATLAS collaboration

2014-01-01

We are now in a regime where we observe substantial multiple proton-proton collisions within each filled LHC bunch-crossing and also multiple filled bunch-crossings within the sensitive time window of the ATLAS detector. This will increase with increased luminosity in the near future. Including these effects in Monte Carlo simulation poses significant computing challenges. We present a description of the standard approach used by the ATLAS experiment and details of how we manage the conflicting demands of keeping the background dataset size as small as possible while minimizing the effect of background event re-use. We also present details of the methods used to minimize the memory footprint of these digitization jobs, to keep them within the grid limit, despite combining the information from thousands of simulated events at once. We also describe an alternative approach, known as Overlay. Here, the actual detector conditions are sampled from raw data using a special zero-bias trigger, and the simulated physi...

ATLAS, an integrated structural analysis and design system. Volume 4: Random access file catalog

Science.gov (United States)

Gray, F. P., Jr. (Editor)

1979-01-01

A complete catalog is presented for the random access files used by the ATLAS integrated structural analysis and design system. ATLAS consists of several technical computation modules which output data matrices to corresponding random access file. A description of the matrices written on these files is contained herein.

Improving ATLAS grid site reliability with functional tests using HammerCloud

CERN Document Server

Legger, F; The ATLAS collaboration

2012-01-01

With the exponential growth of LHC (Large Hadron Collider) data in 2011, and more coming in 2012, distributed computing has become the established way to analyse collider data. The ATLAS grid infrastructure includes almost 100 sites worldwide, ranging from large national computing centers to smaller university clusters. These facilities are used for data reconstruction and simulation, which are centrally managed by the ATLAS production system, and for distributed user analysis. To ensure the smooth operation of such a complex system, regular tests of all sites are necessary to validate the site capability of successfully executing user and production jobs. We report on the development, optimization and results of an automated functional testing suite using the HammerCloud framework. Functional tests are short light-weight applications covering typical user analysis and production schemes, which are periodically submitted to all ATLAS grid sites. Results from those tests are collected and used to evaluate site...

Improving ATLAS grid site reliability with functional tests using HammerCloud

CERN Document Server

Legger, F; The ATLAS collaboration; Medrano Llamas, R; Sciacca, G; Van der Ster, D C

2012-01-01

With the exponential growth of LHC (Large Hadron Collider) data in 2011, and more coming in 2012, distributed computing has become the established way to analyse collider data. The ATLAS grid infrastructure includes more than 80 sites worldwide, ranging from large national computing centers to smaller university clusters. These facilities are used for data reconstruction and simulation, which are centrally managed by the ATLAS production system, and for distributed user analysis. To ensure the smooth operation of such a complex system, regular tests of all sites are necessary to validate the site capability of successfully executing user and production jobs. We report on the development, optimization and results of an automated functional testing suite using the HammerCloud framework. Functional tests are short light-weight applications covering typical user analysis and production schemes, which are periodically submitted to all ATLAS grid sites. Results from those tests are collected and used to evaluate si...

Congenital bipartite atlas with hypodactyly in a dog: clinical, radiographic and CT findings.

Science.gov (United States)

Wrzosek, M; Płonek, M; Zeira, O; Bieżyński, J; Kinda, W; Guziński, M

2014-07-01

A three-year-old Border collie was diagnosed with a bipartite atlas and bilateral forelimb hypodactyly. The dog showed signs of acute, non-progressive neck pain, general stiffness and right thoracic limb non-weight-bearing lameness. Computed tomography imaging revealed a bipartite atlas with abaxial vertical bone proliferation, which was the cause of the clinical signs. In addition, bilateral hypodactyly of the second and fifth digits was incidentally found. This report suggests that hypodactyly may be associated with atlas malformations. © 2014 British Small Animal Veterinary Association.

Job optimization in ATLAS TAG-based distributed analysis

Science.gov (United States)

Mambelli, M.; Cranshaw, J.; Gardner, R.; Maeno, T.; Malon, D.; Novak, M.

2010-04-01

The ATLAS experiment is projected to collect over one billion events/year during the first few years of operation. The efficient selection of events for various physics analyses across all appropriate samples presents a significant technical challenge. ATLAS computing infrastructure leverages the Grid to tackle the analysis across large samples by organizing data into a hierarchical structure and exploiting distributed computing to churn through the computations. This includes events at different stages of processing: RAW, ESD (Event Summary Data), AOD (Analysis Object Data), DPD (Derived Physics Data). Event Level Metadata Tags (TAGs) contain information about each event stored using multiple technologies accessible by POOL and various web services. This allows users to apply selection cuts on quantities of interest across the entire sample to compile a subset of events that are appropriate for their analysis. This paper describes new methods for organizing jobs using the TAGs criteria to analyze ATLAS data. It further compares different access patterns to the event data and explores ways to partition the workload for event selection and analysis. Here analysis is defined as a broader set of event processing tasks including event selection and reduction operations ("skimming", "slimming" and "thinning") as well as DPD making. Specifically it compares analysis with direct access to the events (AOD and ESD data) to access mediated by different TAG-based event selections. We then compare different ways of splitting the processing to maximize performance.

Computerized three-dimensional normal atlas

International Nuclear Information System (INIS)

Mano, Isamu; Suto, Yasuzo; Suzuki, Masataka; Iio, Masahiro.

1990-01-01

This paper presents our ongoing project in which normal human anatomy and its quantitative data are systematically arranged in a computer. The final product, the Computerized Three-Dimensional Normal Atlas, will be able to supply tomographic images in any direction, 3-D images, and coded information on organs, e.g., anatomical names, CT numbers, and T 1 and T 2 values. (author)

Spinal canal stenosis at the level of Atlas

Directory of Open Access Journals (Sweden)

Suchanda Bhattacharjee

2011-01-01

Full Text Available We report here a rare case of high cervical stenosis at the level of atlas who presented with progressively deteriorating quadriparesis and respiratory distress. A 10-year-old boy presented with above symptoms of one-year duration with a preceding history of trivial trauma prior to onset of such symptoms. Cervical spine MRI revealed a significant stenosis at the level of atlas from the posterior side with a syrinx extending above and below. High-resolution computed tomography of the above level yielded an ill-defined osseous bar compressing the canal at the level of C 1 posterior arch, which appeared bifid in the midline. The patient was immediately taken up for surgery in view of his respiratory complaints. The child showed an excellent recovery after excision of the posterior arch of atlas and removal of the compressing osseous structure.

Improved ATLAS HammerCloud Monitoring for local Site Administration

CERN Document Server

Boehler, Michael; The ATLAS collaboration; Hoenig, Friedrich; Legger, Federica; Sciacca, Francesco Giovanni; Mancinelli, Valentina

2015-01-01

Every day hundreds of tests are run on the Worldwide LHC Computing Grid for the ATLAS, and CMS experiments in order to evaluate the performance and reliability of the different computing sites. All this activity is steered, controlled, and monitored by the HammerCloud testing infrastructure. Sites with failing functionality tests are auto-excluded from the ATLAS computing grid, therefore it is essential to provide a detailed and well organized web interface for the local site administrators such that they can easily spot and promptly solve site issues. Additional functionality has been developed to extract and visualize the most relevant information. The site administrators can now be pointed easily to major site issues which lead to site blacklisting as well as possible minor issues that are usually not conspicuous enough to warrant the blacklisting of a specific site, but can still cause undesired effects such as a non-negligible job failure rate. This paper summarizes the different developments and optimiz...

Improved ATLAS HammerCloud Monitoring for local Site Administration

CERN Document Server

Boehler, Michael; The ATLAS collaboration; Hoenig, Friedrich; Legger, Federica

2015-01-01

Every day hundreds of tests are run on the Worldwide LHC Computing Grid for the ATLAS, CMS, and LHCb experiments in order to evaluate the performance and reliability of the different computing sites. All this activity is steered, controlled, and monitored by the HammerCloud testing infrastructure. Sites with failing functionality tests are auto-excluded from the ATLAS computing grid, therefore it is essential to provide a detailed and well organized web interface for the local site administrators such that they can easily spot and promptly solve site issues. Additional functionalities have been developed to extract and visualize the most relevant information. The site administrators can now be pointed easily to major site issues which lead to site blacklisting as well as possible minor issues that are usually not conspicuous enough to warrant the blacklisting of a specific site, but can still cause undesired effects such as a non-negligible job failure rate. This contribution summarizes the different developm...

SU-E-J-132: Automated Segmentation with Post-Registration Atlas Selection Based On Mutual Information

International Nuclear Information System (INIS)

Ren, X; Gao, H; Sharp, G

2015-01-01

Purpose: The delineation of targets and organs-at-risk is a critical step during image-guided radiation therapy, for which manual contouring is the gold standard. However, it is often time-consuming and may suffer from intra- and inter-rater variability. The purpose of this work is to investigate the automated segmentation. Methods: The automatic segmentation here is based on mutual information (MI), with the atlas from Public Domain Database for Computational Anatomy (PDDCA) with manually drawn contours.Using dice coefficient (DC) as the quantitative measure of segmentation accuracy, we perform leave-one-out cross-validations for all PDDCA images sequentially, during which other images are registered to each chosen image and DC is computed between registered contour and ground truth. Meanwhile, six strategies, including MI, are selected to measure the image similarity, with MI to be the best. Then given a target image to be segmented and an atlas, automatic segmentation consists of: (a) the affine registration step for image positioning; (b) the active demons registration method to register the atlas to the target image; (c) the computation of MI values between the deformed atlas and the target image; (d) the weighted image fusion of three deformed atlas images with highest MI values to form the segmented contour. Results: MI was found to be the best among six studied strategies in the sense that it had the highest positive correlation between similarity measure (e.g., MI values) and DC. For automated segmentation, the weighted image fusion of three deformed atlas images with highest MI values provided the highest DC among four proposed strategies. Conclusion: MI has the highest correlation with DC, and therefore is an appropriate choice for post-registration atlas selection in atlas-based segmentation. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)

SU-E-J-132: Automated Segmentation with Post-Registration Atlas Selection Based On Mutual Information

Energy Technology Data Exchange (ETDEWEB)

Ren, X; Gao, H [Shanghai Jiao Tong University, Shanghai, Shanghai (China); Sharp, G [Massachusetts General Hospital, Boston, MA (United States)

2015-06-15

Purpose: The delineation of targets and organs-at-risk is a critical step during image-guided radiation therapy, for which manual contouring is the gold standard. However, it is often time-consuming and may suffer from intra- and inter-rater variability. The purpose of this work is to investigate the automated segmentation. Methods: The automatic segmentation here is based on mutual information (MI), with the atlas from Public Domain Database for Computational Anatomy (PDDCA) with manually drawn contours.Using dice coefficient (DC) as the quantitative measure of segmentation accuracy, we perform leave-one-out cross-validations for all PDDCA images sequentially, during which other images are registered to each chosen image and DC is computed between registered contour and ground truth. Meanwhile, six strategies, including MI, are selected to measure the image similarity, with MI to be the best. Then given a target image to be segmented and an atlas, automatic segmentation consists of: (a) the affine registration step for image positioning; (b) the active demons registration method to register the atlas to the target image; (c) the computation of MI values between the deformed atlas and the target image; (d) the weighted image fusion of three deformed atlas images with highest MI values to form the segmented contour. Results: MI was found to be the best among six studied strategies in the sense that it had the highest positive correlation between similarity measure (e.g., MI values) and DC. For automated segmentation, the weighted image fusion of three deformed atlas images with highest MI values provided the highest DC among four proposed strategies. Conclusion: MI has the highest correlation with DC, and therefore is an appropriate choice for post-registration atlas selection in atlas-based segmentation. Xuhua Ren and Hao Gao were partially supported by the NSFC (#11405105), the 973 Program (#2015CB856000) and the Shanghai Pujiang Talent Program (#14PJ1404500)

Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection.

Science.gov (United States)

Zhuang, Xiahai; Bai, Wenjia; Song, Jingjing; Zhan, Songhua; Qian, Xiaohua; Shi, Wenzhe; Lian, Yanyun; Rueckert, Daniel

2015-07-01

Cardiac computed tomography (CT) is widely used in clinical diagnosis of cardiovascular diseases. Whole heart segmentation (WHS) plays a vital role in developing new clinical applications of cardiac CT. However, the shape and appearance of the heart can vary greatly across different scans, making the automatic segmentation particularly challenging. The objective of this work is to develop and evaluate a multiatlas segmentation (MAS) scheme using a new atlas ranking and selection algorithm for automatic WHS of CT data. Research on different MAS strategies and their influence on WHS performance are limited. This work provides a detailed comparison study evaluating the impacts of label fusion, atlas ranking, and sizes of the atlas database on the segmentation performance. Atlases in a database were registered to the target image using a hierarchical registration scheme specifically designed for cardiac images. A subset of the atlases were selected for label fusion, according to the authors' proposed atlas ranking criterion which evaluated the performance of each atlas by computing the conditional entropy of the target image given the propagated atlas labeling. Joint label fusion was used to combine multiple label estimates to obtain the final segmentation. The authors used 30 clinical cardiac CT angiography (CTA) images to evaluate the proposed MAS scheme and to investigate different segmentation strategies. The mean WHS Dice score of the proposed MAS method was 0.918 ± 0.021, and the mean runtime for one case was 13.2 min on a workstation. This MAS scheme using joint label fusion generated significantly better Dice scores than the other label fusion strategies, including majority voting (0.901 ± 0.276, p ranking study, the proposed criterion based on conditional entropy yielded a performance curve with higher WHS Dice scores compared to the conventional schemes (p ranking algorithm and joint label fusion, the MAS scheme is able to generate accurate segmentation

Organization and management of ATLAS nightly builds

International Nuclear Information System (INIS)

Luehring, F; Obreshkov, E; Quarrie, D; Rybkine, G; Undrus, A

2010-01-01

The automated multi-platform software nightly build system is a major component in the ATLAS collaborative software organization, validation and code approval schemes. Code developers from ATLAS participating Institutes spread all around the world use about 30 branches of nightly releases for testing new packages, verification of patches to existing software, and migration to new platforms and compilers. The nightly releases lead up to, and are the basis of, stable software releases used for data processing worldwide. The ATLAS nightly builds are managed by the fully automated NICOS framework on the computing farm with 44 powerful multiprocessor nodes. The ATN test tool is embedded within the nightly system and provides results shortly after full compilations complete. Other test frameworks are synchronized with NICOS jobs and run larger scale validation jobs using the nightly releases. NICOS web pages dynamically provide information about the progress and results of the builds. For faster feedback, E-mail notifications about nightly releases problems are automatically distributed to the developers responsible.

Overview of the ATLAS Fast Tracker Project

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00025195; The ATLAS collaboration

2016-01-01

The next LHC runs, with a significant increase in instantaneous luminosity, will provide a big challenge for the trigger and data acquisition systems of all the experiments. An intensive use of the tracking information at the trigger level will be important to keep high efficiency for interesting events despite the increase in multiple collisions per bunch crossing. In order to increase the use of tracks within the High Level Trigger, the ATLAS experiment planned the installation of a hardware processor dedicated to tracking: the Fast TracKer processor. The Fast Tracker is designed to perform full scan track reconstruction of every event accepted by the ATLAS first level hardware trigger. To achieve this goal the system uses a parallel architecture, with algorithms designed to exploit the computing power of custom Associative Memory chips, and modern field programmable gate arrays. The processor will provide computing power to reconstruct tracks with transverse momentum greater than 1 GeV in the whole trackin...

Distributed Analysis Experience using Ganga on an ATLAS Tier2 infrastructure

International Nuclear Information System (INIS)

Fassi, F.; Cabrera, S.; Vives, R.; Fernandez, A.; Gonzalez de la Hoz, S.; Sanchez, J.; March, L.; Salt, J.; Kaci, M.; Lamas, A.; Amoros, G.

2007-01-01

The ATLAS detector will explore the high-energy frontier of Particle Physics collecting the proton-proton collisions delivered by the LHC (Large Hadron Collider). Starting in spring 2008, the LHC will produce more than 10 Peta bytes of data per year. The adapted tiered hierarchy for computing model at the LHC is: Tier-0 (CERN), Tiers-1 and Tiers-2 centres distributed around the word. The ATLAS Distributed Analysis (DA) system has the goal of enabling physicists to perform Grid-based analysis on distributed data using distributed computing resources. IFIC Tier-2 facility is participating in several aspects of DA. In support of the ATLAS DA activities a prototype is being tested, deployed and integrated. The analysis data processing applications are based on the Athena framework. GANGA, developed by LHCb and ATLAS experiments, allows simple switching between testing on a local batch system and large-scale processing on the Grid, hiding Grid complexities. GANGA deals with providing physicists an integrated environment for job preparation, bookkeeping and archiving, job splitting and merging. The experience with the deployment, configuration and operation of the DA prototype will be presented. Experiences gained of using DA system and GANGA in the Top physics analysis will be described. (Author)

Continuous software quality analysis for the ATLAS experiment

CERN Document Server

Washbrook, Andrew; The ATLAS collaboration

2017-01-01

The software for the ATLAS experiment on the Large Hadron Collider at CERN has evolved over many years to meet the demands of Monte Carlo simulation, particle detector reconstruction and data analysis. At present over 3.8 million lines of C++ code (and close to 6 million total lines of code) are maintained by an active worldwide developer community. In order to run the experiment software efficiently at hundreds of computing centres it is essential to maintain a high level of software quality standards. The methods proposed to improve software quality practices by incorporating checks into the new ATLAS software build infrastructure.

Integration Of PanDA Workload Management System With Supercomputers for ATLAS and Data Intensive Science

Energy Technology Data Exchange (ETDEWEB)

De, K [University of Texas at Arlington; Jha, S [Rutgers University; Klimentov, A [Brookhaven National Laboratory (BNL); Maeno, T [Brookhaven National Laboratory (BNL); Nilsson, P [Brookhaven National Laboratory (BNL); Oleynik, D [University of Texas at Arlington; Panitkin, S [Brookhaven National Laboratory (BNL); Wells, Jack C [ORNL; Wenaus, T [Brookhaven National Laboratory (BNL)

2016-01-01

The Large Hadron Collider (LHC), operating at the international CERN Laboratory in Geneva, Switzerland, is leading Big Data driven scientific explorations. Experiments at the LHC explore the fundamental nature of matter and the basic forces that shape our universe, and were recently credited for the discovery of a Higgs boson. ATLAS, one of the largest collaborations ever assembled in the sciences, is at the forefront of research at the LHC. To address an unprecedented multi-petabyte data processing challenge, the ATLAS experiment is relying on a heterogeneous distributed computational infrastructure. The ATLAS experiment uses PanDA (Production and Data Analysis) Workload Management System for managing the workflow for all data processing on over 150 data centers. Through PanDA, ATLAS physicists see a single computing facility that enables rapid scientific breakthroughs for the experiment, even though the data centers are physically scattered all over the world. While PanDA currently uses more than 250,000 cores with a peak performance of 0.3 petaFLOPS, LHC data taking runs require more resources than Grid computing can possibly provide. To alleviate these challenges, LHC experiments are engaged in an ambitious program to expand the current computing model to include additional resources such as the opportunistic use of supercomputers. We will describe a project aimed at integration of PanDA WMS with supercomputers in United States, Europe and Russia (in particular with Titan supercomputer at Oak Ridge Leadership Computing Facility (OLCF), MIRA supercomputer at Argonne Leadership Computing Facilities (ALCF), Supercomputer at the National Research Center Kurchatov Institute , IT4 in Ostrava and others). Current approach utilizes modified PanDA pilot framework for job submission to the supercomputers batch queues and local data management, with light-weight MPI wrappers to run single threaded workloads in parallel on LCFs multi-core worker nodes. This implementation

Exploiting opportunistic resources for ATLAS with ARC CE and the Event Service

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00226583; The ATLAS collaboration; Filipčič, Andrej; Guan, Wen; Tsulaia, Vakhtang; Walker, Rodney; Wenaus, Torre

2017-01-01

With ever-greater computing needs and fixed budgets, big scientific experiments are turning to opportunistic resources as a means to add much-needed extra computing power. These resources can be very different in design from the resources that comprise the Grid computing of most experiments, therefore exploiting these resources requires a change in strategy for the experiment. The resources may be highly restrictive in what can be run or in connections to the outside world, or tolerate opportunistic usage only on condition that tasks may be terminated without warning. The ARC CE with its non-intrusive architecture is designed to integrate resources such as High Performance Computing (HPC) systems into a computing Grid. The ATLAS experiment developed the Event Service primarily to address the issue of jobs that can be terminated at any point when opportunistic resources are needed by someone else. This paper describes the integration of these two systems in order to exploit opportunistic resources for ATLAS in...

Exploiting Opportunistic Resources for ATLAS with ARC CE and the Event Service

CERN Document Server

Cameron, David; The ATLAS collaboration

2016-01-01

With ever-greater computing needs and fixed budgets, big scientific experiments are turning to opportunistic resources as a means to add much-needed extra computing power. These resources can be very different in design from the resources that comprise the Grid computing of most experiments, therefore exploiting these resources requires a change in strategy for the experiment. The resources may be highly restrictive in what can be run or in connections to the outside world, or tolerate opportunistic usage only on condition that tasks may be terminated without warning. The ARC CE with its non-intrusive architecture is designed to integrate resources such as High Performance Computing (HPC) systems into a computing Grid. The ATLAS experiment developed the Event Service primarily to address the issue of jobs that can be terminated at any point when opportunistic resources are needed by someone else. This paper describes the integration of these two systems in order to exploit opportunistic resources for ATLAS in...

ATLAS data sonification: a new interface for musical expression and public interaction

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00345031; The ATLAS collaboration; Goldfarb, Steven

2017-01-01

The goal of this project is to transform ATLAS data into sound and explore how ATLAS audio can be a source of inspiration and education for musicians and for the general public. Real-time ATLAS data is sonified and streamed as music on a dedicated website. Listeners may be motivated to learn more about the ATLAS experiment and composers have the opportunity to explore the physics in the collision data through a new medium. The ATLAS collaboration has shared its expertise and access to the live data stream from which the live event displays are generated. This talk tells the story of a long journey from the hallways of CERN where the project collaboration began to the halls of the Montreux Jazz Festival where harmonies were performed. The mapping of the data to sound will be outlined and interactions with musicians and contributions to conferences dedicated to human-computer interaction will also be discussed.

The last ATLAS overview week now available on Web Lectures

CERN Multimedia

Jeremy Herr

2006-01-01

As reported in the September 2004 ATLAS eNews, the Web Lecture Archive Project, WLAP, a collaboration between the University of Michigan and CERN, has developed a synchronized system for recording and publishing educational multimedia presentations, using the Web as medium. This year, the University of Michigan team has been asked to record and publish all ATLAS Plenary sessions, as well as a large number of Physics and Computing tutorials. A significant amount of this material has already been published and can be accessed via the links below. All lectures can be viewed on any major platform with any common internet browser, either via streaming or local download (for limited bandwidth). Please enjoy the lectures and send us a note at wlap@umich.edu to tell us what you think. The newly available WLAP items relating to ATLAS is the following: ATLAS Week Plenary, CERN, 2-3 October 2006 All previous WLAP lectures are also avilable on the web.

Reliability Engineering for ATLAS Petascale Data Processing on the Grid

CERN Document Server

Golubkov, D V; The ATLAS collaboration; Vaniachine, A V

2012-01-01

The ATLAS detector is in its third year of continuous LHC running taking data for physics analysis. A starting point for ATLAS physics analysis is reconstruction of the raw data. First-pass processing takes place shortly after data taking, followed later by reprocessing of the raw data with updated software and calibrations to improve the quality of the reconstructed data for physics analysis. Data reprocessing involves a significant commitment of computing resources and is conducted on the Grid. The reconstruction of one petabyte of ATLAS data with 1B collision events from the LHC takes about three million core-hours. Petascale data processing on the Grid involves millions of data processing jobs. At such scales, the reprocessing must handle a continuous stream of failures. Automatic job resubmission recovers transient failures at the cost of CPU time used by the failed jobs. Orchestrating ATLAS data processing applications to ensure efficient usage of tens of thousands of CPU-cores, reliability engineering ...

ATLAS Outreach Highlights

CERN Document Server

Cheatham, Susan; The ATLAS collaboration

2016-01-01

The ATLAS outreach team is very active, promoting particle physics to a broad range of audiences including physicists, general public, policy makers, students and teachers, and media. A selection of current outreach activities and new projects will be presented. Recent highlights include the new ATLAS public website and ATLAS Open Data, the very recent public release of 1 fb-1 of ATLAS data.

Computer Vision Evidence Supporting Craniometric Alignment of Rat Brain Atlases to Streamline Expert-Guided, First-Order Migration of Hypothalamic Spatial Datasets Related to Behavioral Control

Science.gov (United States)

Khan, Arshad M.; Perez, Jose G.; Wells, Claire E.; Fuentes, Olac

2018-01-01

The rat has arguably the most widely studied brain among all animals, with numerous reference atlases for rat brain having been published since 1946. For example, many neuroscientists have used the atlases of Paxinos and Watson (PW, first published in 1982) or Swanson (S, first published in 1992) as guides to probe or map specific rat brain structures and their connections. Despite nearly three decades of contemporaneous publication, no independent attempt has been made to establish a basic framework that allows data mapped in PW to be placed in register with S, or vice versa. Such data migration would allow scientists to accurately contextualize neuroanatomical data mapped exclusively in only one atlas with data mapped in the other. Here, we provide a tool that allows levels from any of the seven published editions of atlases comprising three distinct PW reference spaces to be aligned to atlas levels from any of the four published editions representing S reference space. This alignment is based on registration of the anteroposterior stereotaxic coordinate (z) measured from the skull landmark, Bregma (β). Atlas level alignments performed along the z axis using one-dimensional Cleveland dot plots were in general agreement with alignments obtained independently using a custom-made computer vision application that utilized the scale-invariant feature transform (SIFT) and Random Sample Consensus (RANSAC) operation to compare regions of interest in photomicrographs of Nissl-stained tissue sections from the PW and S reference spaces. We show that z-aligned point source data (unpublished hypothalamic microinjection sites) can be migrated from PW to S space to a first-order approximation in the mediolateral and dorsoventral dimensions using anisotropic scaling of the vector-formatted atlas templates, together with expert-guided relocation of obvious outliers in the migrated datasets. The migrated data can be contextualized with other datasets mapped in S space, including

Computer Vision Evidence Supporting Craniometric Alignment of Rat Brain Atlases to Streamline Expert-Guided, First-Order Migration of Hypothalamic Spatial Datasets Related to Behavioral Control

Directory of Open Access Journals (Sweden)

Arshad M. Khan

2018-05-01

Full Text Available The rat has arguably the most widely studied brain among all animals, with numerous reference atlases for rat brain having been published since 1946. For example, many neuroscientists have used the atlases of Paxinos and Watson (PW, first published in 1982 or Swanson (S, first published in 1992 as guides to probe or map specific rat brain structures and their connections. Despite nearly three decades of contemporaneous publication, no independent attempt has been made to establish a basic framework that allows data mapped in PW to be placed in register with S, or vice versa. Such data migration would allow scientists to accurately contextualize neuroanatomical data mapped exclusively in only one atlas with data mapped in the other. Here, we provide a tool that allows levels from any of the seven published editions of atlases comprising three distinct PW reference spaces to be aligned to atlas levels from any of the four published editions representing S reference space. This alignment is based on registration of the anteroposterior stereotaxic coordinate (z measured from the skull landmark, Bregma (β. Atlas level alignments performed along the z axis using one-dimensional Cleveland dot plots were in general agreement with alignments obtained independently using a custom-made computer vision application that utilized the scale-invariant feature transform (SIFT and Random Sample Consensus (RANSAC operation to compare regions of interest in photomicrographs of Nissl-stained tissue sections from the PW and S reference spaces. We show that z-aligned point source data (unpublished hypothalamic microinjection sites can be migrated from PW to S space to a first-order approximation in the mediolateral and dorsoventral dimensions using anisotropic scaling of the vector-formatted atlas templates, together with expert-guided relocation of obvious outliers in the migrated datasets. The migrated data can be contextualized with other datasets mapped in S

The ATLAS Fast Tracker

CERN Document Server

Volpi, Guido; The ATLAS collaboration

2015-01-01

The use of tracking information at the trigger level in the LHC Run II period is crucial for the trigger an data acquisition (TDAQ) system. The tracking precision is in fact important to identify specific decay products of the Higgs boson or new phenomena, a well as to distinguish the contributions coming from many contemporary collisions that occur at every bunch crossing. However, the track reconstruction is among the most demanding tasks performed by the TDAQ computing farm; in fact, full reconstruction at full Level-1 trigger accept rate (100 KHz) is not possible. In order to overcome this limitation, the ATLAS experiment is planning the installation of a specific processor: the Fast Tracker (FTK), which is aimed at achieving this goal. The FTK is a pipeline of high performance electronic, based on custom and commercial devices, which is expected to reconstruct, with high resolution, the trajectories of charged tracks with a transverse momentum above 1 GeV, using the ATLAS inner tracker information. Patte...

CERN Open Days 2013, Point 1 - ATLAS: ATLAS Experiment

CERN Multimedia

CERN Photolab

2013-01-01

Stand description: The ATLAS Experiment at CERN is one of the largest and most complex scientific endeavours ever assembled. The detector, located at collision point 1 of the LHC, is designed to explore the fundamental components of nature and to study the forces that shape our universe. The past year’s discovery of a Higgs boson is one of the most important scientific achievements of our time, yet this is only one of many key goals of ATLAS. During a brief break in their journey, some of the 3000-member ATLAS collaboration will be taking time to share the excitement of this exploration with you. On surface no restricted access  The exhibit at Point 1 will give visitors a chance to meet these modern-day explorers and to learn from them how answers to the most fundamental questions of mankind are being sought. Activities will include a visit to the ATLAS detector, located 80m below ground; watching the prize-winning ATLAS movie in the ATLAS cinema; seeing real particle tracks in a cloud chamber and discussi...

gLExec Integration with the ATLAS PanDA Workload Management System

CERN Document Server

Edward Karavakis; The ATLAS collaboration; Campana, Simone; De, Kaushik; Di Girolamo, Alessandro; Maarten Litmaath; Maeno, Tadashi; Medrano Llamas, Ramon; Nilsson, Paul; Wenaus, Torre

2015-01-01

The ATLAS Experiment at the Large Hadron Collider has collected data during Run 1 and is ready to collect data in Run 2. The ATLAS data are distributed, processed and analysed at more than 130 grid and cloud sites across the world. At any given time, there are more than 150,000 concurrent jobs running and about a million jobs are submitted on a daily basis on behalf of thousands of physicists within the ATLAS collaboration. The Production and Distributed Analysis (PanDA) workload management system has proved to be a key component of ATLAS and plays a crucial role in the success of the large-scale distributed computing as it is the sole system for distributed processing of Grid jobs across the collaboration since October 2007. ATLAS user jobs are executed on worker nodes by pilots sent to the sites by pilot factories. This pilot architecture has greatly improved job reliability and although it has clear advantages, such as making the working environment homogeneous by hiding any potential heterogeneities, the ...

Improving vertebra segmentation through joint vertebra-rib atlases

Science.gov (United States)

Wang, Yinong; Yao, Jianhua; Roth, Holger R.; Burns, Joseph E.; Summers, Ronald M.

2016-03-01

Accurate spine segmentation allows for improved identification and quantitative characterization of abnormalities of the vertebra, such as vertebral fractures. However, in existing automated vertebra segmentation methods on computed tomography (CT) images, leakage into nearby bones such as ribs occurs due to the close proximity of these visibly intense structures in a 3D CT volume. To reduce this error, we propose the use of joint vertebra-rib atlases to improve the segmentation of vertebrae via multi-atlas joint label fusion. Segmentation was performed and evaluated on CTs containing 106 thoracic and lumbar vertebrae from 10 pathological and traumatic spine patients on an individual vertebra level basis. Vertebra atlases produced errors where the segmentation leaked into the ribs. The use of joint vertebra-rib atlases produced a statistically significant increase in the Dice coefficient from 92.5 +/- 3.1% to 93.8 +/- 2.1% for the left and right transverse processes and a decrease in the mean and max surface distance from 0.75 +/- 0.60mm and 8.63 +/- 4.44mm to 0.30 +/- 0.27mm and 3.65 +/- 2.87mm, respectively.

PanDA: Exascale Federation of Resources for the ATLAS Experiment at the LHC

Science.gov (United States)

Barreiro Megino, Fernando; Caballero Bejar, Jose; De, Kaushik; Hover, John; Klimentov, Alexei; Maeno, Tadashi; Nilsson, Paul; Oleynik, Danila; Padolski, Siarhei; Panitkin, Sergey; Petrosyan, Artem; Wenaus, Torre

2016-02-01

After a scheduled maintenance and upgrade period, the world's largest and most powerful machine - the Large Hadron Collider(LHC) - is about to enter its second run at unprecedented energies. In order to exploit the scientific potential of the machine, the experiments at the LHC face computational challenges with enormous data volumes that need to be analysed by thousand of physics users and compared to simulated data. Given diverse funding constraints, the computational resources for the LHC have been deployed in a worldwide mesh of data centres, connected to each other through Grid technologies. The PanDA (Production and Distributed Analysis) system was developed in 2005 for the ATLAS experiment on top of this heterogeneous infrastructure to seamlessly integrate the computational resources and give the users the feeling of a unique system. Since its origins, PanDA has evolved together with upcoming computing paradigms in and outside HEP, such as changes in the networking model, Cloud Computing and HPC. It is currently running steadily up to 200 thousand simultaneous cores (limited by the available resources for ATLAS), up to two million aggregated jobs per day and processes over an exabyte of data per year. The success of PanDA in ATLAS is triggering the widespread adoption and testing by other experiments. In this contribution we will give an overview of the PanDA components and focus on the new features and upcoming challenges that are relevant to the next decade of distributed computing workload management using PanDA.

Advanced technologies for scalable ATLAS conditions database access on the grid

CERN Document Server

Basset, R; Dimitrov, G; Girone, M; Hawkings, R; Nevski, P; Valassi, A; Vaniachine, A; Viegas, F; Walker, R; Wong, A

2010-01-01

During massive data reprocessing operations an ATLAS Conditions Database application must support concurrent access from numerous ATLAS data processing jobs running on the Grid. By simulating realistic work-flow, ATLAS database scalability tests provided feedback for Conditions Db software optimization and allowed precise determination of required distributed database resources. In distributed data processing one must take into account the chaotic nature of Grid computing characterized by peak loads, which can be much higher than average access rates. To validate database performance at peak loads, we tested database scalability at very high concurrent jobs rates. This has been achieved through coordinated database stress tests performed in series of ATLAS reprocessing exercises at the Tier-1 sites. The goal of database stress tests is to detect scalability limits of the hardware deployed at the Tier-1 sites, so that the server overload conditions can be safely avoided in a production environment. Our analysi...

First experience and adaptation of existing tools to ATLAS distributed analysis

International Nuclear Information System (INIS)

De La Hoz, S.G.; Ruiz, L.M.; Liko, D.

2008-01-01

The ATLAS production system has been successfully used to run production of simulation data at an unprecedented scale in ATLAS. Up to 10000 jobs were processed on about 100 sites in one day. The experiences obtained operating the system on several grid flavours was essential to perform a user analysis using grid resources. First tests of the distributed analysis system were then performed. In the preparation phase data was registered in the LHC file catalog (LFC) and replicated in external sites. For the main test, few resources were used. All these tests are only a first step towards the validation of the computing model. The ATLAS management computing board decided to integrate the collaboration efforts in distributed analysis in only one project, GANGA. The goal is to test the reconstruction and analysis software in a large scale Data production using grid flavors in several sites. GANGA allows trivial switching between running test jobs on a local batch system and running large-scale analyses on the grid; it provides job splitting and merging, and includes automated job monitoring and output retrieval. (orig.)

Improving ATLAS grid site reliability with functional tests using HammerCloud

Science.gov (United States)

Elmsheuser, Johannes; Legger, Federica; Medrano Llamas, Ramon; Sciacca, Gianfranco; van der Ster, Dan

2012-12-01

With the exponential growth of LHC (Large Hadron Collider) data in 2011, and more coming in 2012, distributed computing has become the established way to analyse collider data. The ATLAS grid infrastructure includes almost 100 sites worldwide, ranging from large national computing centers to smaller university clusters. These facilities are used for data reconstruction and simulation, which are centrally managed by the ATLAS production system, and for distributed user analysis. To ensure the smooth operation of such a complex system, regular tests of all sites are necessary to validate the site capability of successfully executing user and production jobs. We report on the development, optimization and results of an automated functional testing suite using the HammerCloud framework. Functional tests are short lightweight applications covering typical user analysis and production schemes, which are periodically submitted to all ATLAS grid sites. Results from those tests are collected and used to evaluate site performances. Sites that fail or are unable to run the tests are automatically excluded from the PanDA brokerage system, therefore avoiding user or production jobs to be sent to problematic sites.

Initial validation of ATLAS software on the ARM architecture

Energy Technology Data Exchange (ETDEWEB)

Kawamura, Gen; Quadt, Arnulf; Smith, Joshua Wyatt [II. Physikalisches Institut, Georg-August Universitaet Goettingen (Germany); Seuster, Rolf [TRIUMF (Canada); Stewart, Graeme [University of Glasgow (United Kingdom)

2016-07-01

In the early 2000's the introduction of the multi-core era of computing helped industry and experiments such as ATLAS realize even more computing power. This was necessary as the limits of what a single-core processor could do where quickly being reached. Our current model of computing is to increase the number of multi-core nodes in a server farm in order to handle the increased influx of data. As power costs and our need for more computing power increase, this model will eventually become non-realistic. Once again a paradigm shift has to take place. One such option is to look at alternative architectures for large scale server farms. ARM processors are such an example. Making up approximately 95 % of the smartphone and tablet market these processors are widely available, very power conservative and constantly becoming faster. The ATLAS software code base (Athena) is extremely complex comprising of more than 6.5 million lines of code. It has very recently been ported to the ARM 64-bit architecture. The process of our port as well as the first validation plots are presented and compared to the traditional x86 architecture.

The ATLAS Eventlndex: data flow and inclusion of other metadata

Science.gov (United States)

Barberis, D.; Cárdenas Zárate, S. E.; Favareto, A.; Fernandez Casani, A.; Gallas, E. J.; Garcia Montoro, C.; Gonzalez de la Hoz, S.; Hrivnac, J.; Malon, D.; Prokoshin, F.; Salt, J.; Sanchez, J.; Toebbicke, R.; Yuan, R.; ATLAS Collaboration

2016-10-01

The ATLAS EventIndex is the catalogue of the event-related metadata for the information collected from the ATLAS detector. The basic unit of this information is the event record, containing the event identification parameters, pointers to the files containing this event as well as trigger decision information. The main use case for the EventIndex is event picking, as well as data consistency checks for large production campaigns. The EventIndex employs the Hadoop platform for data storage and handling, as well as a messaging system for the collection of information. The information for the EventIndex is collected both at Tier-0, when the data are first produced, and from the Grid, when various types of derived data are produced. The EventIndex uses various types of auxiliary information from other ATLAS sources for data collection and processing: trigger tables from the condition metadata database (COMA), dataset information from the data catalogue AMI and the Rucio data management system and information on production jobs from the ATLAS production system. The ATLAS production system is also used for the collection of event information from the Grid jobs. EventIndex developments started in 2012 and in the middle of 2015 the system was commissioned and started collecting event metadata, as a part of ATLAS Distributed Computing operations.

New developments in file-based infrastructure for ATLAS event selection

Energy Technology Data Exchange (ETDEWEB)

Gemmeren, P van; Malon, D M [Argonne National Laboratory, Argonne, Illinois 60439 (United States); Nowak, M, E-mail: gemmeren@anl.go [Brookhaven National Laboratory, Upton, NY 11973-5000 (United States)

2010-04-01

In ATLAS software, TAGs are event metadata records that can be stored in various technologies, including ROOT files and relational databases. TAGs are used to identify and extract events that satisfy certain selection predicates, which can be coded as SQL-style queries. TAG collection files support in-file metadata to store information describing all events in the collection. Event Selector functionality has been augmented to provide such collection-level metadata to subsequent algorithms. The ATLAS I/O framework has been extended to allow computational processing of TAG attributes to select or reject events without reading the event data. This capability enables physicists to use more detailed selection criteria than are feasible in an SQL query. For example, the TAGs contain enough information not only to check the number of electrons, but also to calculate their distance to the closest jet-a calculation that would be difficult to express in SQL. Another new development allows ATLAS to write TAGs directly into event data files. This feature can improve performance by supporting advanced event selection capabilities, including computational processing of TAG information, without the need for external TAG file or database access.

Discovery of SM Higgs Boson in ATLAS Experiment

Indian Academy of Sciences (India)

IAS Admin

ics, Higgs boson, particle detec- tors, trigger, grid computing. Discovery of SM Higgs Boson in ATLAS Experiment. Prafulla Kumar Behera. Prafulla Kumar Behera is an experimental high energy physicist at the. IITM, Chennai. He has participated in many large-scale collider experiments namely. BELLE at Japan, BABAR.

ATLAS Thesis Award 2017

CERN Multimedia

Anthony, Katarina

2018-01-01

Winners of the ATLAS Thesis Award were presented with certificates and glass cubes during a ceremony on 22 February, 2018. They are pictured here with Karl Jakobs (ATLAS Spokesperson), Max Klein (ATLAS Collaboration Board Chair) and Katsuo Tokushuku (ATLAS Collaboration Board Deputy Chair).

ATLAS TDAQ System Administration: an overview and evolution

CERN Document Server

LEE, CJ; The ATLAS collaboration; BOGDANCHIKOV, A; BRASOLIN, F; CONTESCU, AC; DARLEA, G-L; KOROL, A; SCANNICCHIO, DA; TWOMEY, M; VALSAN, ML

2013-01-01

The ATLAS Trigger and Data Acquisition (TDAQ) system is responsible for the online processing of live data streaming from the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. The system processes the direct data readout from ~100 million channels on the detector through multiple trigger levels, selecting interesting events for analysis with a factor of $10^{7}$ reduction on the data rate with a latency of less than a few seconds. Most of the functionality is implemented on ~3000 servers composing the online farm. Due to the critical functionality of the system a sophisticated computing environment is maintained, covering the online farm and ATLAS control rooms, as well as a number of development and testing labs. The specificity of the system required the development of dedicated applications (e.g. ConfDB, BWM) for system configuration and maintenance; in parallel other Open Source tools (Puppet and Quattor) are used to centrally configure the operating systems. The health monitoring of the TDAQ s...

ATLAS TDAQ System Administration: an overview and evolution

CERN Document Server

LEE, CJ; The ATLAS collaboration; BOGDANCHIKOV, A; BRASOLIN, F; CONTESCU, AC; DARLEA, GL; KOROL, A; SCANNICCHIO, DA; TWOMEY, M; VALSAN, ML

2013-01-01

The ATLAS Trigger and Data Acquisition (TDAQ) system is responsible for the online processing of live data streaming from the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. The system processes the direct data readout from ~100 million channels on the detector through three trigger levels, selecting interesting events for analysis with a factor of 10^7 reduction on the data rate with a latency of less than a few seconds. Most of the functionality is implemented on ~3000 servers composing the online farm. Due to the critical functionality of the system a sophisticated computing environment is maintained, covering the online farm and ATLAS control rooms, as well as a number of development and testing labs. The specificity of the system required the development of dedicated applications (e.g. ConfDB, BWM) for system configuration and maintenance; in parallel other Open Source tools (Puppet and Quattor) are used to centrally configure the operating systems. The health monitoring of the TDAQ system h...

Online remote monitoring facilities for the ATLAS experiment

CERN Document Server

Kolos, S; The ATLAS collaboration; Feng, E; Hauser, R; Yakovlev, A; Zaytsev, A

2011-01-01

ATLAS is one of the 4 LHC experiments which started to be operated in the collisions mode in 2010. The ATLAS apparatus itself as well as the Trigger and the DAQ system are extremely complex facilities which have been built up by the collaboration including 144 institutes from 33 countries. The effective running of the experiment is supported by a large number of experts distributed all over the world. This paper describes the online remote monitoring system which has been developed in the ATLAS Trigger and DAQ(TDAQ) community in order to support efficient participation of the experts from remote institutes in the exploitation of the experiment. The facilities provided by the remote monitoring system are ranging from the WEB based access to the general status and data quality for the ongoing data taking session to the scalable service providing real-time mirroring of the detailed monitoring data from the experimental area to the dedicated computers in the CERN public network, where this data is made available ...

Data Federation Strategies for ATLAS using XRootD

CERN Document Server

Gardner, R; The ATLAS collaboration; Duckeck, G; Elmsheuser, J; Hanushevski, A; Hönig, F; Iven, J; Legger, F; Vukotic, I; Yang, W

2014-01-01

In the past year the ATLAS Collaboration accelerated its program to federate data storage resources using an architecture based on XRootD with its attendant redirection and storage integration services. The main goal of the federation is an improvement in the data access experience for the end user while allowing more efficient and intelligent use of computing resources. Along with these advances come integration with existing ATLAS production services (PanDA and its pilot services) and data management services (DQ2, and in the next generation, Rucio). Functional testing of the federation has been integrated into the standard ATLAS and WLCG monitoring frameworks and a dedicated set of tools provides high granularity information on its current and historical usage. We use a federation topology designed to search from the site's local storage outward to its region and to globally distributed storage resources. We describe programmatic testing of various federation access modes including direct access over the w...

Data Federation Strategies for ATLAS using XRootD

CERN Document Server

Gardner, R; The ATLAS collaboration; Duckeck, G; Elmsheuser, J; Hanushevski, A; Hönig, F; Iven, J; Legger, F; Vukotic, I; Yang, W

2013-01-01

In the past year the ATLAS Collaboration accelerated its program to federate data storage resources using an architecture based on XRootD with its attendant redirection and storage integration services. The main goal of the federation is an improvement in the data access experience for the end user while allowing more efficient and intelligent use of computing resources. Along with these advances come integration with existing ATLAS production services (PanDA and its pilot services) and data management services (DQ2, and in the next generation, Rucio). Functional testing of the federation has been integrated into the standard ATLAS and WLCG monitoring frameworks and a dedicated set of tools provides high granularity information on its current and historical usage. We use a federation topology designed to search from the site's local storage outward to its region and to globally distributed storage resources. We describe programmatic testing of various federation access modes including direct access over the w...

Distributed analysis in ATLAS using GANGA

International Nuclear Information System (INIS)

Elmsheuser, Johannes; Brochu, Frederic; Egede, Ulrik; Reece, Will; Williams, Michael; Gaidioz, Benjamin; Maier, Andrew; Moscicki, Jakub; Vanderster, Daniel; Lee, Hurng-Chun; Pajchel, Katarina; Samset, Bjorn; Slater, Mark; Soroko, Alexander; Cowan, Greig

2010-01-01

Distributed data analysis using Grid resources is one of the fundamental applications in high energy physics to be addressed and realized before the start of LHC data taking. The needs to manage the resources are very high. In every experiment up to a thousand physicists will be submitting analysis jobs to the Grid. Appropriate user interfaces and helper applications have to be made available to assure that all users can use the Grid without expertise in Grid technology. These tools enlarge the number of Grid users from a few production administrators to potentially all participating physicists. The GANGA job management system (http://cern.ch/ganga), developed as a common project between the ATLAS and LHCb experiments, provides and integrates these kind of tools. GANGA provides a simple and consistent way of preparing, organizing and executing analysis tasks within the experiment analysis framework, implemented through a plug-in system. It allows trivial switching between running test jobs on a local batch system and running large-scale analyzes on the Grid, hiding Grid technicalities. We will be reporting on the plug-ins and our experiences of distributed data analysis using GANGA within the ATLAS experiment. Support for all Grids presently used by ATLAS, namely the LCG/EGEE, NDGF/NorduGrid, and OSG/PanDA is provided. The integration and interaction with the ATLAS data management system DQ2 into GANGA is a key functionality. An intelligent job brokering is set up by using the job splitting mechanism together with data-set and file location knowledge. The brokering is aided by an automated system that regularly processes test analysis jobs at all ATLAS DQ2 supported sites. Large numbers of analysis jobs can be sent to the locations of data following the ATLAS computing model. GANGA supports amongst other things tasks of user analysis with reconstructed data and small scale production of Monte Carlo data.

Lessons learned from the ATLAS performance studies of the Iberian Cloud for the first LHC running period

International Nuclear Information System (INIS)

Sánchez-Martínez, V; Hoz, S González de la; Salt, J; Villaplana, M; Borges, G; Gomes, J; Borrego, C; Pages, A Pacheco; Sedov, A; Peso, J del; Delfino, M; Wolters, H

2014-01-01

In this contribution we describe the performance of the Iberian (Spain and Portugal) ATLAS cloud during the first LHC running period (March 2010-January 2013) in the context of the GRID Computing and Data Distribution Model. The evolution of the resources for CPU, disk and tape in the Iberian Tier-1 and Tier-2s is summarized. The data distribution over all ATLAS destinations is shown, focusing on the number of files transferred and the size of the data. The status and distribution of simulation and analysis jobs within the cloud are discussed. The Distributed Analysis tools used to perform physics analysis are explained as well. Cloud performance in terms of the availability and reliability of its sites is discussed. The effect of the changes in the ATLAS Computing Model on the cloud is analyzed. Finally, the readiness of the Iberian Cloud towards the first Long Shutdown (LS1) is evaluated and an outline of the foreseen actions to take in the coming years is given. The shutdown will be a good opportunity to improve and evolve the ATLAS Distributed Computing system to prepare for the future challenges of the LHC operation.

ATLAS

CERN Multimedia

Akhnazarov, V; Canepa, A; Bremer, J; Burckhart, H; Cattai, A; Voss, R; Hervas, L; Kaplon, J; Nessi, M; Werner, P; Ten kate, H; Tyrvainen, H; Vandelli, W; Krasznahorkay, A; Gray, H; Alvarez gonzalez, B; Eifert, T F; Rolando, G; Oide, H; Barak, L; Glatzer, J; Backhaus, M; Schaefer, D M; Maciejewski, J P; Milic, A; Jin, S; Von torne, E; Limbach, C; Medinnis, M J; Gregor, I; Levonian, S; Schmitt, S; Waananen, A; Monnier, E; Muanza, S G; Pralavorio, P; Talby, M; Tiouchichine, E; Tocut, V M; Rybkin, G; Wang, S; Lacour, D; Laforge, B; Ocariz, J H; Bertoli, W; Malaescu, B; Sbarra, C; Yamamoto, A; Sasaki, O; Koriki, T; Hara, K; Da silva gomes, A; Carvalho maneira, J; Marcalo da palma, A; Chekulaev, S; Tikhomirov, V; Snesarev, A; Buzykaev, A; Maslennikov, A; Peleganchuk, S; Sukharev, A; Kaplan, B E; Swiatlowski, M J; Nef, P D; Schnoor, U; Oakham, G F; Ueno, R; Orr, R S; Abouzeid, O; Haug, S; Peng, H; Kus, V; Vitek, M; Temming, K K; Dang, N P; Meier, K; Schultz-coulon, H; Geisler, M P; Sander, H; Schaefer, U; Ellinghaus, F; Rieke, S; Nussbaumer, A; Liu, Y; Richter, R; Kortner, S; Fernandez-bosman, M; Ullan comes, M; Espinal curull, J; Chiriotti alvarez, S; Caubet serrabou, M; Valladolid gallego, E; Kaci, M; Carrasco vela, N; Lancon, E C; Besson, N E; Gautard, V; Bracinik, J; Bartsch, V C; Potter, C J; Lester, C G; Moeller, V A; Rosten, J; Crooks, D; Mathieson, K; Houston, S C; Wright, M; Jones, T W; Harris, O B; Byatt, T J; Dobson, E; Hodgson, P; Hodgkinson, M C; Dris, M; Karakostas, K; Ntekas, K; Oren, D; Duchovni, E; Etzion, E; Oren, Y; Ferrer, L M; Testa, M; Doria, A; Merola, L; Sekhniaidze, G; Giordano, R; Ricciardi, S; Milazzo, A; Falciano, S; De pedis, D; Dionisi, C; Veneziano, S; Cardarelli, R; Verzegnassi, C; Soualah, R; Ochi, A; Ohshima, T; Kishiki, S; Linde, F L; Vreeswijk, M; Werneke, P; Muijs, A; Vankov, P H; Jansweijer, P P M; Dale, O; Lund, E; Bruckman de renstrom, P; Dabrowski, W; Adamek, J D; Wolters, H; Micu, L; Pantea, D; Tudorache, V; Mjoernmark, J; Klimek, P J; Ferrari, A; Abdinov, O; Akhoundov, A; Hashimov, R; Shelkov, G; Khubua, J; Ladygin, E; Lazarev, A; Glagolev, V; Dedovich, D; Lykasov, G; Zhemchugov, A; Zolnikov, Y; Ryabenko, M; Sivoklokov, S; Vasilyev, I; Shalimov, A; Lobanov, M; Paramoshkina, E; Mosidze, M; Bingul, A; Nodulman, L J; Guarino, V J; Yoshida, R; Drake, G R; Calafiura, P; Haber, C; Quarrie, D R; Alonso, J R; Anderson, C; Evans, H; Lammers, S W; Baubock, M; Anderson, K; Petti, R; Suhr, C A; Linnemann, J T; Richards, R A; Tollefson, K A; Holzbauer, J L; Stoker, D P; Pier, S; Nelson, A J; Isakov, V; Martin, A J; Adelman, J A; Paganini, M; Gutierrez, P; Snow, J M; Pearson, B L; Cleland, W E; Savinov, V; Wong, W; Goodson, J J; Li, H; Lacey, R A; Gordeev, A; Gordon, H; Lanni, F; Nevski, P; Rescia, S; Kierstead, J A; Liu, Z; Yu, W W H; Bensinger, J; Hashemi, K S; Bogavac, D; Cindro, V; Hoeferkamp, M R; Coelli, S; Iodice, M; Piegaia, R N; Alonso, F; Wahlberg, H P; Barberio, E L; Limosani, A; Rodd, N L; Jennens, D T; Hill, E C; Pospisil, S; Smolek, K; Schaile, D A; Rauscher, F G; Adomeit, S; Mattig, P M; Wahlen, H; Volkmer, F; Calvente lopez, S; Sanchis peris, E J; Pallin, D; Podlyski, F; Says, L; Boumediene, D E; Scott, W; Phillips, P W; Greenall, A; Turner, P; Gwilliam, C B; Kluge, T; Wrona, B; Sellers, G J; Millward, G; Adragna, P; Hartin, A; Alpigiani, C; Piccaro, E; Bret cano, M; Hughes jones, R E; Mercer, D; Oh, A; Chavda, V S; Carminati, L; Cavasinni, V; Fedin, O; Patrichev, S; Ryabov, Y; Nesterov, S; Grebenyuk, O; Sasso, J; Mahmood, H; Polsdofer, E; Dai, T; Ferretti, C; Liu, H; Hegazy, K H; Benjamin, D P; Zobernig, G; Ban, J; Brooijmans, G H; Keener, P; Williams, H H; Le geyt, B C; Hines, E J; Fadeyev, V; Schumm, B A; Law, A T; Kuhl, A D; Neubauer, M S; Shang, R; Gagliardi, G; Calabro, D; Conta, C; Zinna, M; Jones, G; Li, J; Stradling, A R; Hadavand, H K; Mcguigan, P; Chiu, P; Baldelomar, E; Stroynowski, R A; Kehoe, R L; De groot, N; Timmermans, C; Lach-heb, F; Addy, T N; Nakano, I; Moreno lopez, D; Grosse-knetter, J; Tyson, B; Rude, G D; Tafirout, R; Benoit, P; Danielsson, H O; Elsing, M; Fassnacht, P; Froidevaux, D; Ganis, G; Gorini, B; Lasseur, C; Lehmann miotto, G; Kollar, D; Aleksa, M; Sfyrla, A; Duehrssen-debling, K; Fressard-batraneanu, S; Van der ster, D C; Bortolin, C; Schumacher, J; Mentink, M; Geich-gimbel, C; Yau wong, K H; Lafaye, R; Crepe-renaudin, S; Albrand, S; Hoffmann, D; Pangaud, P; Meessen, C; Hrivnac, J; Vernay, E; Perus, A; Henrot versille, S L; Le dortz, O; Derue, F; Piccinini, M; Polini, A; Terada, S; Arai, Y; Ikeno, M; Fujii, H; Nagano, K; Ukegawa, F; Aguilar saavedra, J A; Conde muino, P; Castro, N F; Eremin, V; Kopytine, M; Sulin, V; Tsukerman, I; Korol, A; Nemethy, P; Bartoldus, R; Glatte, A; Chelsky, S; Van nieuwkoop, J; Bellerive, A; Sinervo, J K; Battaglia, A; Barbier, G J; Pohl, M; Rosselet, L; Alexandre, G B; Prokoshin, F; Pezoa rivera, R A; Batkova, L; Kladiva, E; Stastny, J; Kubes, T; Vidlakova, Z; Esch, H; Homann, M; Herten, L G; Zimmermann, S U; Pfeifer, B; Stenzel, H; 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Kroseberg, J; Gonella, L; Velz, T; Schmitt, S; Lobodzinska, E M; Lovschall-jensen, A E; Galster, G; Perrot, G; Cailles, M; Berger, N; Barnovska, Z; Delsart, P; Lleres, A; Tisserant, S; Grivaz, J; Matricon, P; Bellagamba, L; Bertin, A; Bruschi, M; De castro, S; Semprini cesari, N; Fabbri, L; Rinaldi, L; Quayle, W B; Truong, T N L; Kondo, T; Haruyama, T; Ng, C; Do valle wemans, A; Almeida veloso, F M; Konovalov, S; Ziegler, J M; Su, D; Lukas, W; Prince, S; Ortega urrego, E J; Teuscher, R J; Knecht, N; Pretzl, K; Borer, C; Gadomski, S; Koch, B; Kuleshov, S; Brooks, W K; Antos, J; Kulkova, I; Chudoba, J; Chyla, J; Tomasek, L; Bazalova, M; Messmer, I; Tobias, J; Sundermann, J E; Kuehn, S S; Kluge, E; Scharf, V L; Barillari, T; Kluth, S; Menke, S; Weigell, P; Schwegler, P; Ziolkowski, M; Casado lechuga, P M; Garcia, C; Sanchez, J; Costa mezquita, M J; Valero biot, J A; Laporte, J; Nikolaidou, R; Virchaux, M; Nguyen, V T H; Charlton, D; Harrison, K; Slater, M W; Newman, P R; Parker, A M; Ward, P; 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Carlsen, A; Kanwal, B; Cochran jr, J H; Aronnax, M V; Lockner, M J; Zhou, B; Levin, D S; Weaverdyck, C J; Grom, G F; Rudge, A; Ebenstein, W L; Jia, B; Yamaoka, J; Jared, R C; Wu, S L; Banerjee, S; Lu, Q; Hughes, E W; Alkire, S P; Degenhardt, J D; Lipeles, E D; Spencer, E N; Savine, A; Cheu, E C; Lampl, W; Veatch, J R; Roberts, K; Atkinson, M J; Odino, G A; Polesello, G; Martin, T; White, A P; Stephens, R; Grinbaum sarkisyan, E; Vartapetian, A; Yu, J; Sosebee, M; Thilagar, P A; Spurlock, B; Bonde, R; Filthaut, F; Klok, P; Hoummada, A; Ouchrif, M; Pellegrini, G; Rafi tatjer, J M; Navarro, G A; Blumenschein, U; Weingarten, J C; Mueller, D; Graber, L; Gao, Y; Bode, A; Capeans garrido, M D M; Carli, T; Wells, P; Beltramello, O; Vuillermet, R; Dudarev, A; Salzburger, A; Torchiani, C I; Serfon, C L G; Sloper, J E; Duperrier, G; Lilova, P T; Knecht, M O; Lassnig, M; Anders, G; Deviveiros, P; Young, C; Sforza, F; Shaochen, C; Lu, F; Wermes, N; Wienemann, P; Schwindt, T; Hansen, P H; Hansen, J B; 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Murillo quijada, J A; Grout, Z J; Chapman, J D; Cowan, G D; George, S; Boisvert, V; Mcmahon, T R; Doyle, A T; Thompson, S A; Britton, D; Smizanska, M; Campanelli, M; Butterworth, J M; Loken, J; Renton, P; Barr, A J; Issever, C; Short, D; Crispin ortuzar, M; Tovey, D R; French, R; Rozen, Y; Alexander, G; Kreisel, A; Conventi, F; Raulo, A; Schioppa, M; Susinno, G; Tassi, E; Giagu, S; Luci, C; Nisati, A; Cobal, M; Ishikawa, A; Jinnouchi, O; Bos, K; Verkerke, W; Vermeulen, J; Van vulpen, I B; Kieft, G; Mora, K D; Olsen, F; Rohne, O M; Pajchel, K; Nilsen, J K; Wosiek, B K; Wozniak, K W; Badescu, E; Jinaru, A; Bohm, C; Johansson, E K; Sjoelin, J B R; Clement, C; Buszello, C P; Huseynova, D; Boyko, I; Popov, B; Poukhov, O; Vinogradov, V; Tsiareshka, P; Skvorodnev, N; Soldatov, A; Chuguev, A; Gushchin, V; Yazici, E; Lutz, M S; Malon, D; Vanyashin, A; Lavrijsen, W; Spieler, H; Biesiada, J L; Bahr, M; Kong, J; Tatarkhanov, M; Ogren, H; Van kooten, R J; Cwetanski, P; Butler, J M; Shank, J T; Chakraborty, D; Ermoline, I; Sinev, N; Whiteson, D O; Corso radu, A; Huang, J; Werth, M P; Kastoryano, M; Meirose da silva costa, B; Namasivayam, H; Hobbs, J D; Schamberger jr, R D; Guo, F; Potekhin, M; Popovic, D; Gorisek, A; Sokhrannyi, G; Hofsajer, I W; Mandelli, L; Ceradini, F; Graziani, E; Giorgi, F; Zur nedden, M E G; Grancagnolo, S; Volpi, M; Nunes hanninger, G; Rados, P K; Milesi, M; Cuthbert, C J; Black, C W; Fink grael, F; Fincke-keeler, M; Keeler, R; Kowalewski, R V; Berghaus, F O; Qi, M; Davidek, T; Tas, P; Jakubek, J; Duckeck, G; Walker, R; Mitterer, C A; Harenberg, T; Sandvoss, S A; Del peso, J; Llorente merino, J; Gonzalez millan, V; Irles quiles, A; Crouau, M; Gris, P L Y; Liauzu, S; Romano saez, S M; Gallop, B J; Jones, T J; Austin, N C; Morris, J; Duerdoth, I; Thompson, R J; Kelly, M P; Leisos, A; Garas, A; Pizio, C; Venda pinto, B A; Kudin, L; Qian, J; Wilson, A W; Mietlicki, D; Long, J D; Sang, Z; Arms, K E; Rahimi, A M; Moss, J J; Oh, S H; Parker, S I; Parsons, J; Cunitz, H; Vanguri, R S; Sadrozinski, H; Lockman, W S; Martinez-mc kinney, G; Goussiou, A; Jones, A; Lie, K; Hasegawa, Y; Olcese, M; Gilewsky, V; Harrison, P F; Janus, M; Spangenberg, M; De, K; Ozturk, N; Pal, A K; Darmora, S; Bullock, D J; Oviawe, O; Derkaoui, J E; Rahal, G; Sircar, A; Frey, A S; Stolte, P; Rosien, N; Zoch, K; Li, L; Schouten, D W; Catinaccio, A; Ciapetti, M; Delruelle, N; Ellis, N; Farthouat, P; Hoecker, A; Klioutchnikova, T; Macina, D; Malyukov, S; Spiwoks, R D; Unal, G P; Vandoni, G; Petersen, B A; Pommes, K; Nairz, A M; Wengler, T; Mladenov, D; Solans sanchez, C A; Lantzsch, K; Schmieden, K; Jakobsen, S; Ritsch, E; Sciuccati, A; Alves dos santos, A M; Ouyang, Q; Zhou, M; Brock, I C; Janssen, J; Katzy, J; Anders, C F; Nilsson, B S; Bazan, A; Di ciaccio, L; Yildizkaya, T; Collot, J; Malek, F; Trocme, B S; Breugnon, P; Godiot, S; Adam bourdarios, C; Coulon, J; Duflot, L; Petroff, P G; Zerwas, D; Lieuvin, M; Calderini, G; Laporte, D; Ocariz, J; Gabrielli, A; Ohska, T K; Kurochkin, Y; Kantserov, V; Vasilyeva, L; Speransky, M; Smirnov, S; Antonov, A; Bulekov, O; Tikhonov, Y; Sargsyan, L; Vardanyan, G; Budick, B; Kocian, M L; Luitz, S; Young, C C; Grenier, P J; Kelsey, M; Black, J E; Kneringer, E; Jussel, P; Horton, A J; Beaudry, J; Chandra, A; Ereditato, A; Topfel, C M; Mathieu, R; Bucci, F; Muenstermann, D; White, R M; He, M; Urban, J; Straka, M; Vrba, V; Schumacher, M; Parzefall, U; Mahboubi, K; Sommer, P O; Koepke, L H; Bethke, S; Moser, H; Wiesmann, M; Walkowiak, W A; Fleck, I J; Martinez-perez, M; Sanchez sanchez, C A; Jorgensen roca, S; Accion garcia, E; Sainz ruiz, C A; Valls ferrer, J A; Amoros vicente, G; Vives torrescasana, R; Ouraou, A; Formica, A; Hassani, S; Watson, M F; Cottin buracchio, G F; Bussey, P J; Saxon, D; Ferrando, J E; Collins-tooth, C L; Hall, D C; Cuhadar donszelmann, T; Dawson, I; Duxfield, R; Argyropoulos, T; Brodet, E; Livneh, R; Shougaev, K; Reinherz, E I; Guttman, N; Beretta, M M; Vilucchi, E; Aloisio, A; Patricelli, S; Caprio, M; Cevenini, F; De vecchi, C; Livan, M; Rimoldi, A; Vercesi, V; Ayad, R; Mastroberardino, A; Ciapetti, G; Luminari, L; Rescigno, M; Santonico, R; Salamon, A; Del papa, C; Kurashige, H; Homma, Y; Tomoto, M; Horii, Y; Sugaya, Y; Hanagaki, K; Bobbink, G; Kluit, P M; Koffeman, E N; Van eijk, B; Lee, H; Eigen, G; Dorholt, O; Strandlie, A; Strzempek, P B; Dita, S; Stoicea, G; Chitan, A; Leven, S S; Moa, T; Brenner, R; Ekelof, T J C; Olshevskiy, A; Roumiantsev, V; Chlachidze, G; Zimine, N; Gusakov, Y; Grigalashvili, N; Mineev, M; Potrap, I; Barashkou, A; Shoukavy, D; Shaykhatdenov, B; Pikelner, A; Gladilin, L; Ammosov, V; Abramov, A; Arik, M; Sahinsoy, M; Uysal, Z; Azizi, K; Hotinli, S C; Zhou, S; Berger, E; Blair, R; Underwood, D G; Einsweiler, K; Garcia-sciveres, M A; Siegrist, J L; Kipnis, I; Dahl, O; Holland, S; Barbaro galtieri, A; Smith, P T; Parua, N; Franklin, M; Mercurio, K M; Tong, B; Pod, E; Cole, S G; Hopkins, W H; Guest, D H; Severini, H; Marsicano, J J; Abbott, B K; Wang, Q; Lissauer, D; Ma, H; Takai, H; Rajagopalan, S; Protopopescu, S D; Snyder, S S; Undrus, A; Popescu, R N; Begel, M A; Blocker, C A; Amelung, C; Mandic, I; Macek, B; Tucker, B H; Citterio, M; Troncon, C; Orestano, D; Taccini, C; Romeo, G L; Dova, M T; Taylor, G N; Gesualdi manhaes, A; Mcpherson, R A; Sobie, R; Taylor, R P; Dolezal, Z; Kodys, P; Slovak, R; Sopko, B; Vacek, V; Sanders, M P; Hertenberger, R; Meineck, C; Becks, K; Kind, P; Sandhoff, M; Cantero garcia, J; De la torre perez, H; Castillo gimenez, V; Ros, E; Hernandez jimenez, Y; Chadelas, R; Santoni, C; Washbrook, A J; O'brien, B J; Wynne, B M; Mehta, A; Vossebeld, J H; Landon, M; Teixeira dias castanheira, M; Cerrito, L; Keates, J R; Fassouliotis, D; Chardalas, M; Manousos, A; Grachev, V; Seliverstov, D; Sedykh, E; Cakir, O; Ciftci, R; Edson, W; Prell, S A; Rosati, M; Stroman, T; Jiang, H; Neal, H A; Li, X; Gan, K K; Smith, D S; Kruse, M C; Ko, B R; Leung fook cheong, A M; Cole, B; Angerami, A R; Greene, Z S; Kroll, J I; Van berg, R P; Forbush, D A; Lubatti, H; Raisher, J; Shupe, M A; Wolin, S; Oshita, H; Gaudio, G; Das, R; Konig, A C; Croft, V A; Harvey, A; Maaroufi, F; Melo, I; Greenwood jr, Z D; Shabalina, E; Mchedlidze, G; Drechsler, E; Rieger, J K; Blackston, M; Colombo, T

2002-01-01

% ATLAS \\\\ \\\\ ATLAS is a general-purpose experiment for recording proton-proton collisions at LHC. The ATLAS collaboration consists of 144 participating institutions (June 1998) with more than 1750~physicists and engineers (700 from non-Member States). The detector design has been optimized to cover the largest possible range of LHC physics: searches for Higgs bosons and alternative schemes for the spontaneous symmetry-breaking mechanism; searches for supersymmetric particles, new gauge bosons, leptoquarks, and quark and lepton compositeness indicating extensions to the Standard Model and new physics beyond it; studies of the origin of CP violation via high-precision measurements of CP-violating B-decays; high-precision measurements of the third quark family such as the top-quark mass and decay properties, rare decays of B-hadrons, spectroscopy of rare B-hadrons, and $ B ^0 _{s} $-mixing. \\\\ \\\\The ATLAS dectector, shown in the Figure includes an inner tracking detector inside a 2~T~solenoid providing an axial...

Supporting ATLAS

CERN Multimedia

2003-01-01

Eighteen feet made of stainless steel will support the barrel ATLAS detector in the cavern at Point 1. In total, the ATLAS feet system will carry approximately 6000 tons, and will give the same inclination to the detector as the LHC accelerator. The installation of the feet is scheduled to finish during January 2004 with an installation precision at the 1 mm level despite their height of 5.3 metres. The manufacture was carried out in Russia (Company Izhorskiye Zavody in St. Petersburg), as part of a Russian and JINR Dubna in-kind contribution to ATLAS. Involved in the installation is a team from IHEP-Protvino (Russia), the ATLAS technical co-ordination team at CERN, and the CERN survey team. In all, about 15 people are involved. After the feet are in place, the barrel toroid magnet and the barrel calorimeters will be installed. This will keep the ATLAS team busy for the entire year 2004.

ATLAS Point-1 System Administration Group

CERN Multimedia

Marc Dobson

2007-01-01

Hello, my name is Joe Blog and I am about to go on shift at ATLAS. When I enter the control room shown below with my CERN ID card, I go to the subsystem desk for which I am responsible. This is the first shift of the run period and there is a login window displayed on the screens. I just need to hit return and the control room desktop is started. Before I can do anything I must give my credentials in the shifter window which is then synchronised with the shift plan. After that I have access to all the allowed commands and can start preparing for the run. In order not to forget any steps I consult the documentation on how to prepare for a run on the Point-1 web. I can also check what the general status is for the ATLAS online computing farm, the sub-detectors and the LHC by using the utilities provided. ATLAS Control Room. The situation described is made up but the conditions are real. But the control room that the shifters and general public see is only the tip of the iceberg. Behind these tools lie the...

17 April 2008 - Head of Internal Audit Network meeting visiting the ATLAS experimental area with CERN ATLAS Team Leader P. Fassnacht, ATLAS Technical Coordinator M. Nessi and ATLAS Resources Manager M. Nordberg.

CERN Multimedia

Mona Schweizer

2008-01-01

17 April 2008 - Head of Internal Audit Network meeting visiting the ATLAS experimental area with CERN ATLAS Team Leader P. Fassnacht, ATLAS Technical Coordinator M. Nessi and ATLAS Resources Manager M. Nordberg.

Prompt data reconstruction at the ATLAS experiment

International Nuclear Information System (INIS)

Andrew Stewart, Graeme; Boyd, Jamie; Unal, Guillaume; Firmino da Costa, João; Tuggle, Joseph

2012-01-01

The ATLAS experiment at the LHC collider recorded more than 5 fb −1 data of pp collisions at a centre-of-mass energy of 7 TeV during 2011. The recorded data are promptly reconstructed in two steps at a large computing farm at CERN to provide fast access to high quality data for physics analysis. In the first step, a subset of the data, corresponding to the express stream and having 10Hz of events, is processed in parallel with data taking. Data quality, detector calibration constants, and the beam spot position are determined using the reconstructed data within 48 hours. In the second step all recorded data are processed with the updated parameters. The LHC significantly increased the instantaneous luminosity and the number of interactions per bunch crossing in 2011; the data recording rate by ATLAS exceeds 400 Hz. To cope with these challenges the performance and reliability of the ATLAS reconstruction software have been improved. In this paper we describe how the prompt data reconstruction system quickly and stably provides high quality data to analysers.

The ATLAS Detector Control System

International Nuclear Information System (INIS)

Lantzsch, K; Braun, H; Hirschbuehl, D; Kersten, S; Arfaoui, S; Franz, S; Gutzwiller, O; Schlenker, S; Tsarouchas, C A; Mindur, B; Hartert, J; Zimmermann, S; Talyshev, A; Oliveira Damazio, D; Poblaguev, A; Martin, T; Thompson, P D; Caforio, D; Sbarra, C; Hoffmann, D

2012-01-01

The ATLAS experiment is one of the multi-purpose experiments at the Large Hadron Collider (LHC) at CERN, constructed to study elementary particle interactions in collisions of high-energy proton beams. Twelve different sub detectors as well as the common experimental infrastructure are controlled and monitored by the Detector Control System (DCS) using a highly distributed system of 140 server machines running the industrial SCADA product PVSS. Higher level control system layers allow for automatic control procedures, efficient error recognition and handling, manage the communication with external systems such as the LHC controls, and provide a synchronization mechanism with the ATLAS data acquisition system. Different databases are used to store the online parameters of the experiment, replicate a subset used for physics reconstruction, and store the configuration parameters of the systems. This contribution describes the computing architecture and software tools to handle this complex and highly interconnected control system.

The ATLAS Detector Control System

Science.gov (United States)

Lantzsch, K.; Arfaoui, S.; Franz, S.; Gutzwiller, O.; Schlenker, S.; Tsarouchas, C. A.; Mindur, B.; Hartert, J.; Zimmermann, S.; Talyshev, A.; Oliveira Damazio, D.; Poblaguev, A.; Braun, H.; Hirschbuehl, D.; Kersten, S.; Martin, T.; Thompson, P. D.; Caforio, D.; Sbarra, C.; Hoffmann, D.; Nemecek, S.; Robichaud-Veronneau, A.; Wynne, B.; Banas, E.; Hajduk, Z.; Olszowska, J.; Stanecka, E.; Bindi, M.; Polini, A.; Deliyergiyev, M.; Mandic, I.; Ertel, E.; Marques Vinagre, F.; Ribeiro, G.; Santos, H. F.; Barillari, T.; Habring, J.; Huber, J.; Arabidze, G.; Boterenbrood, H.; Hart, R.; Iakovidis, G.; Karakostas, K.; Leontsinis, S.; Mountricha, E.; Ntekas, K.; Filimonov, V.; Khomutnikov, V.; Kovalenko, S.; Grassi, V.; Mitrevski, J.; Phillips, P.; Chekulaev, S.; D'Auria, S.; Nagai, K.; Tartarelli, G. F.; Aielli, G.; Marchese, F.; Lafarguette, P.; Brenner, R.

2012-12-01

The ATLAS experiment is one of the multi-purpose experiments at the Large Hadron Collider (LHC) at CERN, constructed to study elementary particle interactions in collisions of high-energy proton beams. Twelve different sub detectors as well as the common experimental infrastructure are controlled and monitored by the Detector Control System (DCS) using a highly distributed system of 140 server machines running the industrial SCADA product PVSS. Higher level control system layers allow for automatic control procedures, efficient error recognition and handling, manage the communication with external systems such as the LHC controls, and provide a synchronization mechanism with the ATLAS data acquisition system. Different databases are used to store the online parameters of the experiment, replicate a subset used for physics reconstruction, and store the configuration parameters of the systems. This contribution describes the computing architecture and software tools to handle this complex and highly interconnected control system.

Overview of the ATLAS Fast Tracker Project

CERN Document Server

Ancu, Lucian Stefan; The ATLAS collaboration

2016-01-01

The next LHC runs, with a significant increase in instantaneous luminosity, will provide a big challenge for the trigger and data acquisition systems of all the experiments. An intensive use of the tracking information at the trigger level will be important to keep high efficiency for interesting events despite the increase in multiple collisions per bunch crossing. In order to increase the use of tracks within the High Level Trigger, the ATLAS experiment planned the installation of a hardware processor dedicated to tracking: the Fast TracKer processor. The Fast Tracker is designed to perform full scan track reconstruction of every event accepted by the ATLAS first level hardware trigger. To achieve this goal the system uses a parallel architecture, with algorithms designed to exploit the computing power of custom Associative Memory chips, and modern field programmable gate arrays. The processor will provide computing power to reconstruct tracks with transverse momentum greater than 1 GeV in the whol...

Simulation of the heat transfer around the ATLAS muon chambers

CERN Multimedia

2005-01-01

This 2D simulation recently carried out on the ATLAS muon chambers by a small team of CERN engineers specialises in the numerical computation of fluid dynamics, in other words the flow of fluids and heat.

Experience commissioning the ATLAS distributed data management system on top of the WLCG service

International Nuclear Information System (INIS)

Campana, S

2010-01-01

The ATLAS experiment at CERN developed an automated system for distribution of simulated and detector data. Such system, which partially consists of various ATLAS specific services, strongly relies on the WLCG infrastructure, both at the level of middleware components, service deployment and operations. Because of the complexity of the system and its highly distributed nature, a dedicated effort was put in place to deliver a reliable service for ATLAS data distribution, offering the necessary performance, high availability and accommodating the main use cases. This contribution will describe the various challenges and activities carried on in 2008 for the commissioning of the system, together with the experience distributing simulated data and detector data. The main commissioning activity was concentrated in two Combined Computing Resource Challenges, in February and May 2008, where it was demonstrated that the WLCG service and the ATLAS system could sustain the peak load of data transfer according to the computing model, for several days in a row, concurrently with other LHC experiment activities. This dedicated effort led to the consequential improvements of ATLAS and WLCG services and to daily operation activities throughout the last year. The system has been delivering to WLCG tiers many hundreds of terabytes of simulated data and, since the summer of 2008, more than two petabytes of cosmic and beam data.

Discrete event simulation of the ATLAS second level trigger

International Nuclear Information System (INIS)

Vermeulen, J.C.; Dankers, R.J.; Hunt, S.; Harris, F.; Hortnagl, C.; Erasov, A.; Bogaerts, A.

1998-01-01

Discrete event simulation is applied for determining the computing and networking resources needed for the ATLAS second level trigger. This paper discusses the techniques used and some of the results obtained so far for well defined laboratory configurations and for the full system

Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics

Energy Technology Data Exchange (ETDEWEB)

Aad, G.; Abat, E.; Abbott, B.; Abdallah, J.; Abdelalim, A.A.; Abdesselam, A.; Abdinov, O.; Abi, B.; Abolins, M.; Abramowicz, H.; Acharya, Bobby Samir; Adams, D.L.; Addy, T.N.; Adorisio, C.; Adragna, P.; Adye, T.; Aguilar-Saavedra, J.A.; Aharrouche, M.; Ahlen, S.P.; Ahles, F.; Ahmad, A.; /SUNY, Albany /Alberta U. /Ankara U. /Annecy, LAPP /Argonne /Arizona U. /Texas U., Arlington /Athens U. /Natl. Tech. U., Athens /Baku, Inst. Phys. /Barcelona, IFAE /Belgrade U. /VINCA Inst. Nucl. Sci., Belgrade /Bergen U. /LBL, Berkeley /Humboldt U., Berlin /Bern U., LHEP /Birmingham U. /Bogazici U. /INFN, Bologna /Bologna U.

2011-11-28

The Large Hadron Collider (LHC) at CERN promises a major step forward in the understanding of the fundamental nature of matter. The ATLAS experiment is a general-purpose detector for the LHC, whose design was guided by the need to accommodate the wide spectrum of possible physics signatures. The major remit of the ATLAS experiment is the exploration of the TeV mass scale where groundbreaking discoveries are expected. In the focus are the investigation of the electroweak symmetry breaking and linked to this the search for the Higgs boson as well as the search for Physics beyond the Standard Model. In this report a detailed examination of the expected performance of the ATLAS detector is provided, with a major aim being to investigate the experimental sensitivity to a wide range of measurements and potential observations of new physical processes. An earlier summary of the expected capabilities of ATLAS was compiled in 1999 [1]. A survey of physics capabilities of the CMS detector was published in [2]. The design of the ATLAS detector has now been finalised, and its construction and installation have been completed [3]. An extensive test-beam programme was undertaken. Furthermore, the simulation and reconstruction software code and frameworks have been completely rewritten. Revisions incorporated reflect improved detector modelling as well as major technical changes to the software technology. Greatly improved understanding of calibration and alignment techniques, and their practical impact on performance, is now in place. The studies reported here are based on full simulations of the ATLAS detector response. A variety of event generators were employed. The simulation and reconstruction of these large event samples thus provided an important operational test of the new ATLAS software system. In addition, the processing was distributed world-wide over the ATLAS Grid facilities and hence provided an important test of the ATLAS computing system - this is the origin of

Probabilistic liver atlas construction.

Science.gov (United States)

Dura, Esther; Domingo, Juan; Ayala, Guillermo; Marti-Bonmati, Luis; Goceri, E

2017-01-13

Anatomical atlases are 3D volumes or shapes representing an organ or structure of the human body. They contain either the prototypical shape of the object of interest together with other shapes representing its statistical variations (statistical atlas) or a probability map of belonging to the object (probabilistic atlas). Probabilistic atlases are mostly built with simple estimations only involving the data at each spatial location. A new method for probabilistic atlas construction that uses a generalized linear model is proposed. This method aims to improve the estimation of the probability to be covered by the liver. Furthermore, all methods to build an atlas involve previous coregistration of the sample of shapes available. The influence of the geometrical transformation adopted for registration in the quality of the final atlas has not been sufficiently investigated. The ability of an atlas to adapt to a new case is one of the most important quality criteria that should be taken into account. The presented experiments show that some methods for atlas construction are severely affected by the previous coregistration step. We show the good performance of the new approach. Furthermore, results suggest that extremely flexible registration methods are not always beneficial, since they can reduce the variability of the atlas and hence its ability to give sensible values of probability when used as an aid in segmentation of new cases.

A four-dimensional motion field atlas of the tongue from tagged and cine magnetic resonance imaging

Science.gov (United States)

Xing, Fangxu; Prince, Jerry L.; Stone, Maureen; Wedeen, Van J.; El Fakhri, Georges; Woo, Jonghye

2017-02-01

Representation of human tongue motion using three-dimensional vector fields over time can be used to better understand tongue function during speech, swallowing, and other lingual behaviors. To characterize the inter-subject variability of the tongue's shape and motion of a population carrying out one of these functions it is desirable to build a statistical model of the four-dimensional (4D) tongue. In this paper, we propose a method to construct a spatio-temporal atlas of tongue motion using magnetic resonance (MR) images acquired from fourteen healthy human subjects. First, cine MR images revealing the anatomical features of the tongue are used to construct a 4D intensity image atlas. Second, tagged MR images acquired to capture internal motion are used to compute a dense motion field at each time frame using a phase-based motion tracking method. Third, motion fields from each subject are pulled back to the cine atlas space using the deformation fields computed during the cine atlas construction. Finally, a spatio-temporal motion field atlas is created to show a sequence of mean motion fields and their inter-subject variation. The quality of the atlas was evaluated by deforming cine images in the atlas space. Comparison between deformed and original cine images showed high correspondence. The proposed method provides a quantitative representation to observe the commonality and variability of the tongue motion field for the first time, and shows potential in evaluation of common properties such as strains and other tensors based on motion fields.

Report to users of ATLAS

International Nuclear Information System (INIS)

Ahmad, I.; Glagola, B.

1995-05-01

This report contains discussing in the following areas: Status of the Atlas accelerator; highlights of recent research at Atlas; concept for an advanced exotic beam facility based on Atlas; program advisory committee; Atlas executive committee; and Atlas and ANL physics division on the world wide web

One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI

International Nuclear Information System (INIS)

Arabi, Hossein; Zaidi, Habib

2016-01-01

The outcome of a detailed assessment of various strategies for atlas-based whole-body bone segmentation from magnetic resonance imaging (MRI) was exploited to select the optimal parameters and setting, with the aim of proposing a novel one-registration multi-atlas (ORMA) pseudo-CT generation approach. The proposed approach consists of only one online registration between the target and reference images, regardless of the number of atlas images (N), while for the remaining atlas images, the pre-computed transformation matrices to the reference image are used to align them to the target image. The performance characteristics of the proposed method were evaluated and compared with conventional atlas-based attenuation map generation strategies (direct registration of the entire atlas images followed by voxel-wise weighting (VWW) and arithmetic averaging atlas fusion). To this end, four different positron emission tomography (PET) attenuation maps were generated via arithmetic averaging and VWW scheme using both direct registration and ORMA approaches as well as the 3-class attenuation map obtained from the Philips Ingenuity TF PET/MRI scanner commonly used in the clinical setting. The evaluation was performed based on the accuracy of extracted whole-body bones by the different attenuation maps and by quantitative analysis of resulting PET images compared to CT-based attenuation-corrected PET images serving as reference. The comparison of validation metrics regarding the accuracy of extracted bone using the different techniques demonstrated the superiority of the VWW atlas fusion algorithm achieving a Dice similarity measure of 0.82 ± 0.04 compared to arithmetic averaging atlas fusion (0.60 ± 0.02), which uses conventional direct registration. Application of the ORMA approach modestly compromised the accuracy, yielding a Dice similarity measure of 0.76 ± 0.05 for ORMA-VWW and 0.55 ± 0.03 for ORMA-averaging. The results of quantitative PET analysis followed the same

One registration multi-atlas-based pseudo-CT generation for attenuation correction in PET/MRI

Energy Technology Data Exchange (ETDEWEB)

Arabi, Hossein [Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva 4 (Switzerland); Zaidi, Habib [Geneva University Hospital, Division of Nuclear Medicine and Molecular Imaging, Geneva 4 (Switzerland); Geneva University, Geneva Neuroscience Center, Geneva (Switzerland); University of Groningen, University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, Groningen (Netherlands); University of Southern Denmark, Department of Nuclear Medicine, Odense (Denmark)

2016-10-15

The outcome of a detailed assessment of various strategies for atlas-based whole-body bone segmentation from magnetic resonance imaging (MRI) was exploited to select the optimal parameters and setting, with the aim of proposing a novel one-registration multi-atlas (ORMA) pseudo-CT generation approach. The proposed approach consists of only one online registration between the target and reference images, regardless of the number of atlas images (N), while for the remaining atlas images, the pre-computed transformation matrices to the reference image are used to align them to the target image. The performance characteristics of the proposed method were evaluated and compared with conventional atlas-based attenuation map generation strategies (direct registration of the entire atlas images followed by voxel-wise weighting (VWW) and arithmetic averaging atlas fusion). To this end, four different positron emission tomography (PET) attenuation maps were generated via arithmetic averaging and VWW scheme using both direct registration and ORMA approaches as well as the 3-class attenuation map obtained from the Philips Ingenuity TF PET/MRI scanner commonly used in the clinical setting. The evaluation was performed based on the accuracy of extracted whole-body bones by the different attenuation maps and by quantitative analysis of resulting PET images compared to CT-based attenuation-corrected PET images serving as reference. The comparison of validation metrics regarding the accuracy of extracted bone using the different techniques demonstrated the superiority of the VWW atlas fusion algorithm achieving a Dice similarity measure of 0.82 ± 0.04 compared to arithmetic averaging atlas fusion (0.60 ± 0.02), which uses conventional direct registration. Application of the ORMA approach modestly compromised the accuracy, yielding a Dice similarity measure of 0.76 ± 0.05 for ORMA-VWW and 0.55 ± 0.03 for ORMA-averaging. The results of quantitative PET analysis followed the same

Data federation strategies for ATLAS using XRootD

Science.gov (United States)

Gardner, Robert; Campana, Simone; Duckeck, Guenter; Elmsheuser, Johannes; Hanushevsky, Andrew; Hönig, Friedrich G.; Iven, Jan; Legger, Federica; Vukotic, Ilija; Yang, Wei; Atlas Collaboration

2014-06-01

In the past year the ATLAS Collaboration accelerated its program to federate data storage resources using an architecture based on XRootD with its attendant redirection and storage integration services. The main goal of the federation is an improvement in the data access experience for the end user while allowing more efficient and intelligent use of computing resources. Along with these advances come integration with existing ATLAS production services (PanDA and its pilot services) and data management services (DQ2, and in the next generation, Rucio). Functional testing of the federation has been integrated into the standard ATLAS and WLCG monitoring frameworks and a dedicated set of tools provides high granularity information on its current and historical usage. We use a federation topology designed to search from the site's local storage outward to its region and to globally distributed storage resources. We describe programmatic testing of various federation access modes including direct access over the wide area network and staging of remote data files to local disk. To support job-brokering decisions, a time-dependent cost-of-data-access matrix is made taking into account network performance and key site performance factors. The system's response to production-scale physics analysis workloads, either from individual end-users or ATLAS analysis services, is discussed.

ATLAS Transition Radiation Tracker - large piece

CERN Multimedia

2006-01-01

The ATLAS transition radiation tracker is made of 300'000 straw tubes, up to 144cm long. Filled with a gas mixture and threaded with a wire, each straw is a complete mini-detector in its own right. An electric field is applied between the wire and the outside wall of the straw. As particles pass through, they collide with atoms in the gas, knocking out electrons. The avalanche of electrons is detected as an electrical signal on the wire in the centre. The tracker plays two important roles. Firstly, it makes more position measurements, giving more dots for the computers to join up to recreate the particle tracks. Also, together with the ATLAS calorimeters, it distinguishes between different types of particles depending on whether they emit radiation as they make the transition from the surrounding foil into the straws.

ATLAS Transition Radiation Tracker - small piece

CERN Multimedia

2006-01-01

The ATLAS transition radiation tracker is made of 300'000 straw tubes, up to 144cm long. Filled with a gas mixture and threaded with a wire, each straw is a complete mini-detector in its own right. An electric field is applied between the wire and the outside wall of the straw. As particles pass through, they collide with atoms in the gas, knocking out electrons. The avalanche of electrons is detected as an electrical signal on the wire in the centre. The tracker plays two important roles. Firstly, it makes more position measurements, giving more dots for the computers to join up to recreate the particle tracks. Also, together with the ATLAS calorimeters, it distinguishes between different types of particles depending on whether they emit radiation as they make the transition from the surrounding foil into the straws.

EnviroAtlas

Data.gov (United States)

City and County of Durham, North Carolina — This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The layers in this web...

Large scale access tests and online interfaces to ATLAS conditions databases

International Nuclear Information System (INIS)

Amorim, A; Lopes, L; Pereira, P; Simoes, J; Soloviev, I; Burckhart, D; Schmitt, J V D; Caprini, M; Kolos, S

2008-01-01

The access of the ATLAS Trigger and Data Acquisition (TDAQ) system to the ATLAS Conditions Databases sets strong reliability and performance requirements on the database storage and access infrastructures. Several applications were developed to support the integration of Conditions database access with the online services in TDAQ, including the interface to the Information Services (IS) and to the TDAQ Configuration Databases. The information storage requirements were the motivation for the ONline A Synchronous Interface to COOL (ONASIC) from the Information Service (IS) to LCG/COOL databases. ONASIC avoids the possible backpressure from Online Database servers by managing a local cache. In parallel, OKS2COOL was developed to store Configuration Databases into an Offline Database with history record. The DBStressor application was developed to test and stress the access to the Conditions database using the LCG/COOL interface while operating in an integrated way as a TDAQ application. The performance scaling of simultaneous Conditions database read accesses was studied in the context of the ATLAS High Level Trigger large computing farms. A large set of tests were performed involving up to 1000 computing nodes that simultaneously accessed the LCG central database server infrastructure at CERN

ATLAS Overview Week at Brookhaven

CERN Multimedia

Pilcher, J

Over 200 ATLAS participants gathered at Brookhaven National Laboratory during the first week of June for our annual overview week. Some system communities arrived early and held meetings on Saturday and Sunday, and the detector interface group (DIG) and Technical Coordination also took advantage of the time to discuss issues of interest for all detector systems. Sunday was also marked by a workshop on the possibilities for heavy ion physics with ATLAS. Beginning on Monday, and for the rest of the week, sessions were held in common in the well equipped Berkner Hall auditorium complex. Laptop computers became the norm for presentations and a wireless network kept laptop owners well connected. Most lunches and dinners were held on the lawn outside Berkner Hall. The weather was very cooperative and it was an extremely pleasant setting. This picture shows most of the participants from a view on the roof of Berkner Hall. Technical Coordination and Integration issues started the reports on Monday and became a...

ATLAS FTK: Fast Track Trigger

CERN Document Server

Volpi, Guido; The ATLAS collaboration

2015-01-01

An overview of the ATLAS Fast Tracker processor is presented, reporting the design of the system, its expected performance, and the integration status. The next LHC runs, with a significant increase in instantaneous luminosity, will provide a big challenge to the trigger and data acquisition systems of all the experiments. An intensive use of the tracking information at the trigger level will be important to keep high efficiency in interesting events, despite the increase in multiple p-p collisions per bunch crossing (pile-up). In order to increase the use of tracks within the High Level Trigger (HLT), the ATLAS experiment planned the installation of an hardware processor dedicated to tracking: the Fast TracKer (FTK) processor. The FTK is designed to perform full scan track reconstruction at every Level-1 accept. To achieve this goal, the FTK uses a fully parallel architecture, with algorithms designed to exploit the computing power of custom VLSI chips, the Associative Memory, as well as modern FPGAs. The FT...

Dear ATLAS colleagues,

CERN Multimedia

PH Department

2008-01-01

We are collecting old pairs of glasses to take out to Mali, where they can be re-used by people there. The price for a pair of glasses can often exceed 3 months salary, so they are prohibitively expensive for many people. If you have any old spectacles you can donate, please put them in the special box in the ATLAS secretariat, bldg.40-4-D01 before the Christmas closure on 19 December so we can take them with us when we leave for Africa at the end of the month. (more details in ATLAS e-news edition of 29 September 2008: http://atlas-service-enews.web.cern.ch/atlas-service-enews/news/news_mali.php) many thanks! Katharine Leney co-driver of the ATLAS car on the Charity Run to Mali

Evolution of user analysis on the grid in ATLAS

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00218990; The ATLAS collaboration; Dewhurst, Alastair

2017-01-01

More than one thousand physicists analyse data collected by the ATLAS experiment at the Large Hadron Collider (LHC) at CERN through 150 computing facilities around the world. Efficient distributed analysis requires optimal resource usage and the interplay of several factors: robust grid and software infrastructures, and system capability to adapt to different workloads. The continuous automatic validation of grid sites and the user support provided by a dedicated team of expert shifters have been proven to provide a solid distributed analysis system for ATLAS users. Typical user workflows on the grid, and their associated metrics, are discussed. Measurements of user job performance and typical requirements are also shown.

Recent ATLAS Articles on WLAP

CERN Multimedia

Goldfarb, S

2005-01-01

As reported in the September 2004 ATLAS eNews, the Web Lecture Archive Project is a system for the archiving and publishing of multimedia presentations, using the Web as medium. We list here newly available WLAP items relating to ATLAS: Atlas Software Week Plenary 6-10 December 2004 North American ATLAS Physics Workshop (Tucson) 20-21 December 2004 (17 talks) Physics Analysis Tools Tutorial (Tucson) 19 December 2004 Full Chain Tutorial 21 September 2004 ATLAS Plenary Sessions, 17-18 February 2005 (17 talks) Coming soon: ATLAS Tutorial on Electroweak Physics, 14 Feb. 2005 Software Workshop, 21-22 February 2005 Click here to browse WLAP for all ATLAS lectures.

ATLAS people can run!

CERN Multimedia

Claudia Marcelloni de Oliveira; Pauline Gagnon

It must be all the training we are getting every day, running around trying to get everything ready for the start of the LHC next year. This year, the ATLAS runners were in fine form and came in force. Nine ATLAS teams signed up for the 37th Annual CERN Relay Race with six runners per team. Under a blasting sun on Wednesday 23rd May 2007, each team covered the distances of 1000m, 800m, 800m, 500m, 500m and 300m taking the runners around the whole Meyrin site, hills included. A small reception took place in the ATLAS secretariat a week later to award the ATLAS Cup to the best ATLAS team. For the details on this complex calculation which takes into account the age of each runner, their gender and the color of their shoes, see the July 2006 issue of ATLAS e-news. The ATLAS Running Athena Team, the only all-women team enrolled this year, won the much coveted ATLAS Cup for the second year in a row. In fact, they are so good that Peter Schmid and Patrick Fassnacht are wondering about reducing the women's bonus in...

ATLAS tile calorimeter cesium calibration control and analysis software

International Nuclear Information System (INIS)

Solovyanov, O; Solodkov, A; Starchenko, E; Karyukhin, A; Isaev, A; Shalanda, N

2008-01-01

An online control system to calibrate and monitor ATLAS Barrel hadronic calorimeter (TileCal) with a movable radioactive source, driven by liquid flow, is described. To read out and control the system an online software has been developed, using ATLAS TDAQ components like DVS (Diagnostic and Verification System) to verify the hardware before running, IS (Information Server) for data and status exchange between networked computers, and other components like DDC (DCS to DAQ Connection), to connect to PVSS-based slow control systems of Tile Calorimeter, high voltage and low voltage. A system of scripting facilities, based on Python language, is used to handle all the calibration and monitoring processes from hardware perspective to final data storage, including various abnormal situations. A QT based graphical user interface to display the status of the calibration system during the cesium source scan is described. The software for analysis of the detector response, using online data, is discussed. Performance of the system and first experience from the ATLAS pit are presented

SIGNAL RECONSTRUCTION PERFORMANCE OF THE ATLAS HADRONIC TILE CALORIMETER

CERN Document Server

Do Amaral Coutinho, Y; The ATLAS collaboration

2013-01-01

"The Tile Calorimeter for the ATLAS experiment at the CERN Large Hadron Collider (LHC) is a sampling calorimeter with steel as absorber and scintillators as active medium. The scintillators are readout by wavelength shifting fibers coupled to photomultiplier tubes (PMT). The analogue signals from the PMTs are amplified, shaped and digitized by sampling the signal every 25 ns. The TileCal front-end electronics allows to read out the signals produced by about 10000 channels measuring energies ranging from ~30 MeV to ~2 TeV. The read-out system is responsible for reconstructing the data in real-time fulfilling the tight time constraint imposed by the ATLAS first level trigger rate (100 kHz). The main component of the read-out system is the Digital Signal Processor (DSP) which, using an Optimal Filtering reconstruction algorithm, allows to compute for each channel the signal amplitude, time and quality factor at the required high rate. Currently the ATLAS detector and the LHC are undergoing an upgrade program tha...

ATLAS tile calorimeter cesium calibration control and analysis software

Energy Technology Data Exchange (ETDEWEB)

Solovyanov, O; Solodkov, A; Starchenko, E; Karyukhin, A; Isaev, A; Shalanda, N [Institute for High Energy Physics, Protvino 142281 (Russian Federation)], E-mail: Oleg.Solovyanov@ihep.ru

2008-07-01

An online control system to calibrate and monitor ATLAS Barrel hadronic calorimeter (TileCal) with a movable radioactive source, driven by liquid flow, is described. To read out and control the system an online software has been developed, using ATLAS TDAQ components like DVS (Diagnostic and Verification System) to verify the hardware before running, IS (Information Server) for data and status exchange between networked computers, and other components like DDC (DCS to DAQ Connection), to connect to PVSS-based slow control systems of Tile Calorimeter, high voltage and low voltage. A system of scripting facilities, based on Python language, is used to handle all the calibration and monitoring processes from hardware perspective to final data storage, including various abnormal situations. A QT based graphical user interface to display the status of the calibration system during the cesium source scan is described. The software for analysis of the detector response, using online data, is discussed. Performance of the system and first experience from the ATLAS pit are presented.

Rucio, the next-generation Data Management system in ATLAS

CERN Document Server

Serfon, C; The ATLAS collaboration; Beermann, T; Garonne, V; Goossens, L; Lassnig, M; Nairz, A; Vigne, R

2014-01-01

Rucio is the next-generation of Distributed Data Management (DDM) system benefiting from recent advances in cloud and "Big Data" computing to address HEP experiments scaling requirements. Rucio is an evolution of the ATLAS DDM system Don Quijote 2 (DQ2), which has demonstrated very large scale data management capabilities with more than 160 petabytes spread worldwide across 130 sites, and accesses from 1,000 active users. However, DQ2 is reaching its limits in terms of scalability, requiring a large number of support staff to operate and being hard to extend with new technologies. Rucio addresses these issues by relying on new technologies to ensure system scalability, cover new user requirements and employ new automation framework to reduce operational overheads. In this talk, we will present the history of the DDM project and the experience of data management operation in ATLAS computing. Thus, We will show the key concepts of Rucio, including its data organization. The Rucio design, and the technology it e...

Recent ATLAS Articles on WLAP

CERN Multimedia

J. Herr

As reported in the September 2004 ATLAS eNews, the Web Lecture Archive Project is a system for the archiving and publishing of multimedia presentations, using the Web as medium. We list here newly available WLAP items relating to ATLAS: Atlas Physics Workshop 6-11 June 2005 June 2005 ATLAS Week Plenary Session Click here to browse WLAP for all ATLAS lectures.

Atlas of liver imaging

International Nuclear Information System (INIS)

1989-05-01

This atlas is an outcome of an IAEA co-ordinated research programme. In addition to Japan, nine other Asian countries participated in the project and 293 liver scintigrams (116 from Japanese institutions and 177 from seven Asian countries) were evaluated by physicians from the participating Asian countries. The computer analysis of the scan findings of the individual physicians was carried out and individual scores have been separately tabulated for: (a) scan abnormality; (b) space occupying lesions; (c) cirrhosis and (d) diffuse liver diseases like hepatitis. Refs, figs and tabs

New format for ATLAS e-news

CERN Multimedia

Pauline Gagnon

ATLAS e-news got a new look! As of November 30, 2007, we have a new format for ATLAS e-news. Please go to: http://atlas-service-enews.web.cern.ch/atlas-service-enews/index.html . ATLAS e-news will now be published on a weekly basis. If you are not an ATLAS colaboration member but still want to know how the ATLAS experiment is doing, we will soon have a version of ATLAS e-news intended for the general public. Information will be sent out in due time.

Studies of ATM for ATLAS high-level triggers

CERN Document Server

Bystrický, J; Huet, M; Le Dû, P; Mandjavidze, I D

2001-01-01

This paper presents some of the conclusions of our studies on asynchronous transfer mode (ATM) and fast Ethernet in the ATLAS level-2 trigger pilot project. We describe the general concept and principles of our data-collection and event-building scheme that could be transposed to various experiments in high-energy and nuclear physics. To validate the approach in view of ATLAS high-level triggers, we assembled a testbed composed of up to 48 computers linked by a 7.5-Gbit/s ATM switch. This modular switch is used as a single entity or is split into several smaller interconnected switches. This allows study of how to construct a large network from smaller units. Alternatively, the ATM network can be replaced by fast Ethernet. We detail the operation of the system and present series of performance measurements made with event-building traffic pattern. We extrapolate these results to show how today's commercial networking components could be used to build a 1000-port network adequate for ATLAS needs. Lastly, we li...

Online remote monitoring facilities for the ATLAS experiment

CERN Document Server

Kolos, S; The ATLAS collaboration; Feng, E; Hauser, R; Yakovlev, A; Zaytsev, A

2010-01-01

ATLAS is one of the 4 LHC experiments which started to be operated in the collisions mode in 2010. The ATLAS apparatus itself as well as the Trigger and the DAQ system are extremely complex facilities which have been built up by the collaboration including 144 institutes from 33 countries. The effective running of the experiment is supported by a large number of experts distributed all over the world. This paper describes the online remote monitoring system which has been developed in the ATLAS TDAQ community in order to support efficient participation of the experts from remote institutes in the exploitation of the experiment. The facilities provided by the remote monitoring system are ranging from the WEB based access to the general status and data quality for the ongoing data taking session to the scalable service providing real-time mirroring of the detailed monitoring data from the experimental area to the dedicated computers in the CERN public network, where this data is made available to remote users t...

Multi-threading in the ATLAS High-Level Trigger

CERN Document Server

Barton, Adam Edward; The ATLAS collaboration

2018-01-01

Over the next decade of LHC data-taking the instantaneous luminosity will reach up 7.5 times the design value with over 200 interactions per bunch-crossing and will pose unprecedented challenges for the ATLAS trigger system. With the evolution of the CPU market to many-core systems, both the ATLAS offline reconstruction and High-Level Trigger (HLT) software will have to transition from a multi-process to a multithreaded processing paradigm in order not to exhaust the available physical memory of a typical compute node. The new multithreaded ATLAS software framework, AthenaMT, has been designed from the ground up to support both the offline and online use-cases with the aim to further harmonize the offline and trigger algorithms. The latter is crucial both in terms of maintenance effort and to guarantee the high trigger efficiency and rejection factors needed for the next two decades of data-taking. We report on an HLT prototype in which the need for HLT­specific components has been reduced to a minimum while...

Multi-scale hippocampal parcellation improves atlas-based segmentation accuracy

Science.gov (United States)

Plassard, Andrew J.; McHugo, Maureen; Heckers, Stephan; Landman, Bennett A.

2017-02-01

Known for its distinct role in memory, the hippocampus is one of the most studied regions of the brain. Recent advances in magnetic resonance imaging have allowed for high-contrast, reproducible imaging of the hippocampus. Typically, a trained rater takes 45 minutes to manually trace the hippocampus and delineate the anterior from the posterior segment at millimeter resolution. As a result, there has been a significant desire for automated and robust segmentation of the hippocampus. In this work we use a population of 195 atlases based on T1-weighted MR images with the left and right hippocampus delineated into the head and body. We initialize the multi-atlas segmentation to a region directly around each lateralized hippocampus to both speed up and improve the accuracy of registration. This initialization allows for incorporation of nearly 200 atlases, an accomplishment which would typically involve hundreds of hours of computation per target image. The proposed segmentation results in a Dice similiarity coefficient over 0.9 for the full hippocampus. This result outperforms a multi-atlas segmentation using the BrainCOLOR atlases (Dice 0.85) and FreeSurfer (Dice 0.75). Furthermore, the head and body delineation resulted in a Dice coefficient over 0.87 for both structures. The head and body volume measurements also show high reproducibility on the Kirby 21 reproducibility population (R2 greater than 0.95, p develop a robust tool for measurement of the hippocampus and other temporal lobe structures.

ATLAS Virtual Visits bringing the world into the ATLAS control room

CERN Document Server

AUTHOR|(CDS)2051192; The ATLAS collaboration; Yacoob, Sahal

2016-01-01

ATLAS Virtual Visits is a project initiated in 2011 for the Education & Outreach program of the ATLAS Experiment at CERN. Its goal is to promote public appreciation of the LHC physics program and particle physics, in general, through direct dialogue between ATLAS physicists and remote audiences. A Virtual Visit is an IP-based videoconference, coupled with a public webcast and video recording, between ATLAS physicists and remote locations around the world, that typically include high school or university classrooms, Masterclasses, science fairs, or other special events, usually hosted by collaboration members. Over the past two years, more than 10,000 people, from all of the world’s continents, have actively participated in ATLAS Virtual Visits, with many more enjoying the experience from the publicly available webcasts and recordings. We present an overview of our experience and discuss potential development for the future.

Exploring the human body space: A geographical information system based anatomical atlas

Directory of Open Access Journals (Sweden)

Antonio Barbeito

2016-06-01

Full Text Available Anatomical atlases allow mapping the anatomical structures of the human body. Early versions of these systems consisted of analogical representations with informative text and labeled images of the human body. With computer systems, digital versions emerged and the third and fourth dimensions were introduced. Consequently, these systems increased their efficiency, allowing more realistic visualizations with improved interactivity and functionality. The 4D atlases allow modeling changes over time on the structures represented. The anatomical atlases based on geographic information system (GIS environments allow the creation of platforms with a high degree of interactivity and new tools to explore and analyze the human body. In this study we expand the functions of a human body representation system by creating new vector data, topology, functions, and an improved user interface. The new prototype emulates a 3D GIS with a topological model of the human body, replicates the information provided by anatomical atlases, and provides a higher level of functionality and interactivity. At this stage, the developed system is intended to be used as an educational tool and integrates into the same interface the typical representations of surface and sectional atlases.

Design, Results, Evolution and Status of the ATLAS Simulation at Point1 Project

CERN Document Server

AUTHOR|(SzGeCERN)377840; Fressard-Batraneanu, Silvia Maria; Ballestrero, Sergio; Contescu, Alexandru Cristian; Fazio, Daniel; Di Girolamo, Alessandro; Lee, Christopher Jon; Pozo Astigarraga, Mikel Eukeni; Scannicchio, Diana; Sedov, Alexey; Twomey, Matthew Shaun; Wang, Fuquan; Zaytsev, Alexander

2015-01-01

Abstract. During the LHC Long Shutdown 1 period (LS1), that started in 2013, the Simulation at Point1 (Sim@P1) Project takes advantage, in an opportunistic way, of the TDAQ (Trigger and Data Acquisition) HLT (High Level Trigger) farm of the ATLAS experiment. This farm provides more than 1300 compute nodes, which are particularly suited for running event generation and Monte Carlo production jobs that are mostly CPU and not I/O bound. It is capable of running up to 2700 virtual machines (VMs) provided with 8 CPU cores each, for a total of up to 22000 parallel running jobs. This contribution gives a review of the design, the results, and the evolution of the Sim@P1 Project; operating a large scale OpenStack based virtualized platform deployed on top of the ATLAS TDAQ HLT farm computing resources. During LS1, Sim@P1 was one of the most productive ATLAS sites: it delivered more than 50 million CPU-hours and it generated more than 1.7 billion Monte Carlo events to various analysis communities. The design aspects a...

Design, Results, Evolution and Status of the ATLAS simulation in Point1 project.

CERN Document Server

Ballestrero, Sergio; The ATLAS collaboration; Brasolin, Franco; Contescu, Alexandru Cristian; Fazio, Daniel; Di Girolamo, Alessandro; Lee, Christopher Jon; Pozo Astigarraga, Mikel Eukeni; Scannicchio, Diana; Sedov, Alexey; Twomey, Matthew Shaun; Wang, Fuquan; Zaytsev, Alexander

2015-01-01

During the LHC long shutdown period (LS1), that started in 2013, the simulation in Point1 (Sim@P1) project takes advantage in an opportunistic way of the trigger and data acquisition (TDAQ) farm of the ATLAS experiment. The farm provides more than 1500 computer nodes, and they are particularly suitable for running event generation and Monte Carlo production jobs that are mostly CPU and not I/O bound. It is capable of running up to 2500 virtual machines (VM) provided with 8 CPU cores each, for a total of up to 20000 parallel running jobs. This contribution gives a thorough review of the design, the results and the evolution of the Sim@P1 project operating a large scale Openstack based virtualized platform deployed on top of the ATLAS TDAQ farm computing resources. During LS1, Sim@P1 was one of the most productive GRID sites: it delivered more than 50 million CPU-hours and it generated more than 1.7 billion Monte Carlo events to various analysis communities within the ATLAS collaboration. The particular design ...

The SysteMHC Atlas project.

Science.gov (United States)

Shao, Wenguang; Pedrioli, Patrick G A; Wolski, Witold; Scurtescu, Cristian; Schmid, Emanuel; Vizcaíno, Juan A; Courcelles, Mathieu; Schuster, Heiko; Kowalewski, Daniel; Marino, Fabio; Arlehamn, Cecilia S L; Vaughan, Kerrie; Peters, Bjoern; Sette, Alessandro; Ottenhoff, Tom H M; Meijgaarden, Krista E; Nieuwenhuizen, Natalie; Kaufmann, Stefan H E; Schlapbach, Ralph; Castle, John C; Nesvizhskii, Alexey I; Nielsen, Morten; Deutsch, Eric W; Campbell, David S; Moritz, Robert L; Zubarev, Roman A; Ytterberg, Anders Jimmy; Purcell, Anthony W; Marcilla, Miguel; Paradela, Alberto; Wang, Qi; Costello, Catherine E; Ternette, Nicola; van Veelen, Peter A; van Els, Cécile A C M; Heck, Albert J R; de Souza, Gustavo A; Sollid, Ludvig M; Admon, Arie; Stevanovic, Stefan; Rammensee, Hans-Georg; Thibault, Pierre; Perreault, Claude; Bassani-Sternberg, Michal; Aebersold, Ruedi; Caron, Etienne

2018-01-04

Mass spectrometry (MS)-based immunopeptidomics investigates the repertoire of peptides presented at the cell surface by major histocompatibility complex (MHC) molecules. The broad clinical relevance of MHC-associated peptides, e.g. in precision medicine, provides a strong rationale for the large-scale generation of immunopeptidomic datasets and recent developments in MS-based peptide analysis technologies now support the generation of the required data. Importantly, the availability of diverse immunopeptidomic datasets has resulted in an increasing need to standardize, store and exchange this type of data to enable better collaborations among researchers, to advance the field more efficiently and to establish quality measures required for the meaningful comparison of datasets. Here we present the SysteMHC Atlas (https://systemhcatlas.org), a public database that aims at collecting, organizing, sharing, visualizing and exploring immunopeptidomic data generated by MS. The Atlas includes raw mass spectrometer output files collected from several laboratories around the globe, a catalog of context-specific datasets of MHC class I and class II peptides, standardized MHC allele-specific peptide spectral libraries consisting of consensus spectra calculated from repeat measurements of the same peptide sequence, and links to other proteomics and immunology databases. The SysteMHC Atlas project was created and will be further expanded using a uniform and open computational pipeline that controls the quality of peptide identifications and peptide annotations. Thus, the SysteMHC Atlas disseminates quality controlled immunopeptidomic information to the public domain and serves as a community resource toward the generation of a high-quality comprehensive map of the human immunopeptidome and the support of consistent measurement of immunopeptidomic sample cohorts. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Using containers with ATLAS offline software

CERN Document Server

Vogel, Marcelo; The ATLAS collaboration; Heinrich, Lukas; Stewart, Graeme

2017-01-01

Title: Using containers with ATLAS offline software Marcelo Vogel, Bergische Universitaet Wuppertal Graeme Stewart, University of Glasgow Johannes Elmsheuser, Brookhaven National Laboratory Lukas Heinrich, New York University Abstract: This paper describes the deployment of ATLAS offline software in containers for software development and the use in production jobs on the grid - such as event generation, simulation, reconstruction and physics derivations - and in physics analysis. For this we are using Docker and Singularity which are both lightweight virtualization technologies to encapsulates a piece of software inside a complete file system. The deployment of offline releases via containers removes the interdependence between the runtime environment needed for job execution and the configuration of a computing site’s worker nodes. Once the two are decoupled from each other, sites can upgrade their nodes whenever and however they see fit. Docker or Singularity will provide a uniform runtime environment fo...

Distributed analysis with PROOF in ATLAS collaboration

International Nuclear Information System (INIS)

Panitkin, S Y; Ernst, M; Ito, H; Maeno, T; Majewski, S; Rind, O; Tarrade, F; Wenaus, T; Ye, S; Benjamin, D; Montoya, G Carillo; Guan, W; Mellado, B; Xu, N; Cranmer, K; Shibata, A

2010-01-01

The Parallel ROOT Facility - PROOF is a distributed analysis system which allows to exploit inherent event level parallelism of high energy physics data. PROOF can be configured to work with centralized storage systems, but it is especially effective together with distributed local storage systems - like Xrootd, when data are distributed over computing nodes. It works efficiently on different types of hardware and scales well from a multi-core laptop to large computing farms. From that point of view it is well suited for both large central analysis facilities and Tier 3 type analysis farms. PROOF can be used in interactive or batch like regimes. The interactive regime allows the user to work with typically distributed data from the ROOT command prompt and get a real time feedback on analysis progress and intermediate results. We will discuss our experience with PROOF in the context of ATLAS Collaboration distributed analysis. In particular we will discuss PROOF performance in various analysis scenarios and in multi-user, multi-session environments. We will also describe PROOF integration with the ATLAS distributed data management system and prospects of running PROOF on geographically distributed analysis farms.

Distributed analysis with PROOF in ATLAS collaboration

Energy Technology Data Exchange (ETDEWEB)

Panitkin, S Y; Ernst, M; Ito, H; Maeno, T; Majewski, S; Rind, O; Tarrade, F; Wenaus, T; Ye, S [Brookhaven National Laboratory, Upton, NY 11973 (United States); Benjamin, D [Duke University, Durham, NC 27708 (United States); Montoya, G Carillo; Guan, W; Mellado, B; Xu, N [University of Wisconsin-Madison, Madison, WI 53706 (United States); Cranmer, K; Shibata, A [New York University, New York, NY 10003 (United States)

2010-04-01

The Parallel ROOT Facility - PROOF is a distributed analysis system which allows to exploit inherent event level parallelism of high energy physics data. PROOF can be configured to work with centralized storage systems, but it is especially effective together with distributed local storage systems - like Xrootd, when data are distributed over computing nodes. It works efficiently on different types of hardware and scales well from a multi-core laptop to large computing farms. From that point of view it is well suited for both large central analysis facilities and Tier 3 type analysis farms. PROOF can be used in interactive or batch like regimes. The interactive regime allows the user to work with typically distributed data from the ROOT command prompt and get a real time feedback on analysis progress and intermediate results. We will discuss our experience with PROOF in the context of ATLAS Collaboration distributed analysis. In particular we will discuss PROOF performance in various analysis scenarios and in multi-user, multi-session environments. We will also describe PROOF integration with the ATLAS distributed data management system and prospects of running PROOF on geographically distributed analysis farms.

Supporting ATLAS

CERN Multimedia

maximilien brice

2003-01-01

Eighteen feet made of stainless steel will support the barrel ATLAS detector in the cavern at Point 1. In total, the ATLAS feet system will carry approximately 6000 tons, and will give the same inclination to the detector as the LHC accelerator.

Data federation strategies for ATLAS using XRootD

International Nuclear Information System (INIS)

Gardner, Robert; Vukotic, Ilija; Campana, Simone; Iven, Jan; Duckeck, Guenter; Elmsheuser, Johannes; Hönig, Friedrich G; Legger, Federica; Hanushevsky, Andrew; Yang, Wei

2014-01-01

In the past year the ATLAS Collaboration accelerated its program to federate data storage resources using an architecture based on XRootD with its attendant redirection and storage integration services. The main goal of the federation is an improvement in the data access experience for the end user while allowing more efficient and intelligent use of computing resources. Along with these advances come integration with existing ATLAS production services (PanDA and its pilot services) and data management services (DQ2, and in the next generation, Rucio). Functional testing of the federation has been integrated into the standard ATLAS and WLCG monitoring frameworks and a dedicated set of tools provides high granularity information on its current and historical usage. We use a federation topology designed to search from the site's local storage outward to its region and to globally distributed storage resources. We describe programmatic testing of various federation access modes including direct access over the wide area network and staging of remote data files to local disk. To support job-brokering decisions, a time-dependent cost-of-data-access matrix is made taking into account network performance and key site performance factors. The system's response to production-scale physics analysis workloads, either from individual end-users or ATLAS analysis services, is discussed.

An atlas of the prenatal mouse brain: gestational day 14.

Science.gov (United States)

Schambra, U B; Silver, J; Lauder, J M

1991-11-01

A prenatal atlas of the mouse brain is presently unavailable and is needed for studies of normal and abnormal development, using techniques including immunocytochemistry and in situ hybridization. This atlas will be especially useful for researchers studying transgenic and mutant mice. This collection of photomicrographs and corresponding drawings of Gestational Day (GD) 14 mouse brain sections is an excerpt from a larger atlas encompassing GD 12-18. In composing this atlas, available published studies on the developing rodent brain were consulted to aid in the detailed labeling of embryonic brain structures. C57Bl/6J mice were mated for 1 h, and the presence of a copulation plug was designated as GD 0. GD 14 embryos were perfused transcardially with 4% paraformaldehyde in 0.1 M phosphate buffer and embedded in paraffin. Serial sections (10 microns thickness) were cut through whole heads in sagittal and horizontal planes. They were stained with hematoxylin and eosin and photographed. Magnifications were 43X and 31X for the horizontal and sagittal sections, respectively. Photographs were traced and line drawings prepared using an Adobe Illustrator on a Macintosh computer.

The ATLAS Fast Tracker Processing Units - input and output data preparation

CERN Document Server

Bolz, Arthur; Adelman, Jahred; Anderson, John Thomas; Armbruster, Aaron James; Asbah, Nedaa; Blair, Robert; Brost, Elizabeth; Drake, Gary; Gkaitatzis, Stamatios; Iizawa, Tomoya; Ilic, Nikolina; Jiang, Zihao; Kawaguchi, Yoshimasa; Kimura, Naoki; Kordas, Kostantinos; Sotiropoulou, Calliope Louisa; Love, Jeremy; Mitani, Takashi; Nikolaidis, Spyridon; Okumura, Yasuyuki; Proudfoot, James; Thayil, Steffie Ann; Tompkins, Lauren; Wang, Rui; Watari, Ryutaro; Webster, Jordan S; Yorita, Kohei; Zhang, Jinlong

2017-01-01

The ATLAS Fast TracKer(FTK) is a custom hardware system for fast, associative memory based track reconstruction. It will provide tracking information within the full acceptance of the inner tracking detectors to the high level trigger at a rate of up to 100 kHz. %, thus allowing for a refined and more efficient event selection at the trigger level. At the first stage of the FTK the Data Formatter subsystem clusters inner detector hits and organizes them into 64 $\\eta$-$\\phi$ trigger regions. At the last stage, the FTK to Level-2 Interface Cards repackage track records and send them to the high level trigger computing farm. This report aims to give an overview over the functionality of the two systems, their hardware implementation in the Advanced Telecommunications Computing Architecture standard, and the status of their integration into ATLAS.

New data access with HTTP/WebDAV in the ATLAS experiment

CERN Document Server

Elmsheuser, Johannes; The ATLAS collaboration; Serfon, Cedric; Garonne, Vincent; Blunier, Sylvain; Lavorini, Vincenzo; Nilsson, Paul

2015-01-01

With the exponential growth of LHC (Large Hadron Collider) data in the years 2010-2012, distributed computing has become the established way to analyze collider data. The ATLAS experiment Grid infrastructure includes more than 130 sites worldwide, ranging from large national computing centres to smaller university clusters. So far the storage technologies and access protocols to the clusters that host this tremendous amount of data vary from site to site. HTTP/WebDAV offers the possibility to use a unified industry standard to access the storage. We present the deployment and testing of HTTP/WebDAV for local and remote data access in the ATLAS experiment for the new data management system Rucio and the PanDA workload management system. Deployment and large scale tests have been performed using the Grid testing system HammerCloud and the ROOT HTTP plugin Davix.

New data access with HTTP/WebDAV in the ATLAS experiment

CERN Document Server

Elmsheuser, Johannes; The ATLAS collaboration; Serfon, Cedric; Garonne, Vincent; Blunier, Sylvain; Lavorini, Vincenzo; Nilsson, Paul

2015-01-01

With the exponential growth of LHC (Large Hadron Collider) data in the years 2010-2012, distributed computing has become the established way to analyse collider data. The ATLAS experiment Grid infrastructure includes more than 130 sites worldwide, ranging from large national computing centres to smaller university clusters. So far the storage technologies and access protocols to the clusters that host this tremendous amount of data vary from site to site. HTTP/WebDAV offers the possibility to use a unified industry standard to access the storage. We present the deployment and testing of HTTP/WebDAV for local and remote data access in the ATLAS experiment for the new data management system Rucio and the PanDA workload management system. Deployment and large scale tests have been performed using the Grid testing system HammerCloud and the ROOT HTTP plugin Davix.

Fine grained event processing on HPCs with the ATLAS Yoda system

CERN Document Server

Calafiura, Paolo; The ATLAS collaboration; Guan, Wen; Maeno, Tadashi; Nilsson, Paul; Oleynik, Danila; Panitkin, Sergey; Tsulaia, Vakhtang; van Gemmeren, Peter; Wenaus, Torre

2015-01-01

High performance computing facilities present unique challenges and opportunities for HENP event processing. The massive scale of many HPC systems means that fractionally small utilizations can yield large returns in processing throughput. Parallel applications which can dynamically and efficiently fill any scheduling opportunities the resource presents benefit both the facility (maximal utilization) and the (compute-limited) science. The ATLAS Yoda system provides this capability to HENP-like event processing applications by implementing event-level processing in an MPI-based master-client model that integrates seamlessly with the more broadly scoped ATLAS Event Service. Fine grained, event level work assignments are intelligently dispatched to parallel workers to sustain full utilization on all cores, with outputs streamed off to destination object stores in near real time with similarly fine granularity, such that processing can proceed until termination with full utilization. The system offers the efficie...

The ATLAS Fast Tracker Processing Units - input and output data preparation

CERN Document Server

Bolz, Arthur; Adelman, Jahred; Anderson, John Thomas; Armbruster, Aaron James; Asbah, Nedaa; Blair, Robert; Brost, Elizabeth; Drake, Gary; Gkaitatzis, Stamatios; Iizawa, Tomoya; Ilic, Nikolina; Jiang, Zihao; Kawaguchi, Yoshimasa; Kimura, Naoki; Kordas, Kostantinos; Sotiropoulou, Calliope Louisa; Love, Jeremy; Mitani, Takashi; Nikolaidis, Spyridon; Okumura, Yasuyuki; Proudfoot, James; Thayil, Steffie Ann; Tompkins, Lauren; Wang, Rui; Watari, Ryutaro; Webster, Jordan S; Yorita, Kohei; Zhang, Jinlong

2016-01-01

The ATLAS Fast TracKer(FTK) is a custom hardware system for fast, associative memory based track reconstruction. It will provide tracking information within the full acceptance of the inner tracking detectors to the high level trigger at a rate of up to 100 kHz. %, thus allowing for a refined and more efficient event selection at the trigger level. At the first stage of the FTK the Data Formatter subsystem clusters inner detector hits and organizes them into 64 $\\eta$-$\\phi$ trigger regions. At the last stage, the FTK to Level-2 Interface Cards repackage track records and send them to the high level trigger computing farm. This report aims to give an overview over the functionality of the two systems, their hardware implementation in the Advanced Telecommunications Computing Architecture standard, and the status of their integration into ATLAS.

Running ATLAS workloads within massively parallel distributed applications using Athena Multi-Process framework (AthenaMP)

CERN Document Server

Calafiura, Paolo; The ATLAS collaboration; Seuster, Rolf; Tsulaia, Vakhtang; van Gemmeren, Peter

2015-01-01

AthenaMP is a multi-process version of the ATLAS reconstruction and data analysis framework Athena. By leveraging Linux fork and copy-on-write, it allows the sharing of memory pages between event processors running on the same compute node with little to no change in the application code. Originally targeted to optimize the memory footprint of reconstruction jobs, AthenaMP has demonstrated that it can reduce the memory usage of certain confugurations of ATLAS production jobs by a factor of 2. AthenaMP has also evolved to become the parallel event-processing core of the recently developed ATLAS infrastructure for fine-grained event processing (Event Service) which allows to run AthenaMP inside massively parallel distributed applications on hundreds of compute nodes simultaneously. We present the architecture of AthenaMP, various strategies implemented by AthenaMP for scheduling workload to worker processes (for example: Shared Event Queue and Shared Distributor of Event Tokens) and the usage of AthenaMP in the...

Running ATLAS workloads within massively parallel distributed applications using Athena Multi-Process framework (AthenaMP)

CERN Document Server

Calafiura, Paolo; Seuster, Rolf; Tsulaia, Vakhtang; van Gemmeren, Peter

2015-01-01

AthenaMP is a multi-process version of the ATLAS reconstruction, simulation and data analysis framework Athena. By leveraging Linux fork and copy-on-write, it allows for sharing of memory pages between event processors running on the same compute node with little to no change in the application code. Originally targeted to optimize the memory footprint of reconstruction jobs, AthenaMP has demonstrated that it can reduce the memory usage of certain configurations of ATLAS production jobs by a factor of 2. AthenaMP has also evolved to become the parallel event-processing core of the recently developed ATLAS infrastructure for fine-grained event processing (Event Service) which allows to run AthenaMP inside massively parallel distributed applications on hundreds of compute nodes simultaneously. We present the architecture of AthenaMP, various strategies implemented by AthenaMP for scheduling workload to worker processes (for example: Shared Event Queue and Shared Distributor of Event Tokens) and the usage of Ath...

Report to users of Atlas

International Nuclear Information System (INIS)

Ahmad, I.; Glagola, B.

1996-06-01

This report contains the following topics: Status of the ATLAS Accelerator; Highlights of Recent Research at ATLAS; Program Advisory Committee; ATLAS User Group Executive Committee; FMA Information Available On The World Wide Web; Conference on Nuclear Structure at the Limits; and Workshop on Experiments with Gammasphere at ATLAS

Advanced Technology Lifecycle Analysis System (ATLAS)

Science.gov (United States)

O'Neil, Daniel A.; Mankins, John C.

2004-01-01

Developing credible mass and cost estimates for space exploration and development architectures require multidisciplinary analysis based on physics calculations, and parametric estimates derived from historical systems. Within the National Aeronautics and Space Administration (NASA), concurrent engineering environment (CEE) activities integrate discipline oriented analysis tools through a computer network and accumulate the results of a multidisciplinary analysis team via a centralized database or spreadsheet Each minute of a design and analysis study within a concurrent engineering environment is expensive due the size of the team and supporting equipment The Advanced Technology Lifecycle Analysis System (ATLAS) reduces the cost of architecture analysis by capturing the knowledge of discipline experts into system oriented spreadsheet models. A framework with a user interface presents a library of system models to an architecture analyst. The analyst selects models of launchers, in-space transportation systems, and excursion vehicles, as well as space and surface infrastructure such as propellant depots, habitats, and solar power satellites. After assembling the architecture from the selected models, the analyst can create a campaign comprised of missions spanning several years. The ATLAS controller passes analyst specified parameters to the models and data among the models. An integrator workbook calls a history based parametric analysis cost model to determine the costs. Also, the integrator estimates the flight rates, launched masses, and architecture benefits over the years of the campaign. An accumulator workbook presents the analytical results in a series of bar graphs. In no way does ATLAS compete with a CEE; instead, ATLAS complements a CEE by ensuring that the time of the experts is well spent Using ATLAS, an architecture analyst can perform technology sensitivity analysis, study many scenarios, and see the impact of design decisions. When the analyst is

Poster - 32: Atlas Selection for Automated Segmentation of Pelvic CT for Prostate Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

Mallawi, Abrar; Farrell, TomTom; Diamond, Kevin-Ross; Wierzbicki, Marcin [McMaster University / National Guard Health Affairs, Radiation Oncology Department, Riyadh, Saudi Arabia, McMaster University / Juravinski Cancer Centre, McMaster University / Juravinski Cancer Centre, McMaster University / Juravinski Cancer Centre (Saudi Arabia)

2016-08-15

Atlas based-segmentation has recently been evaluated for use in prostate radiotherapy. In a typical approach, the essential step is the selection of an atlas from a database that the best matches of the target image. This work proposes an atlas selection strategy and evaluate it impacts on final segmentation accuracy. Several anatomical parameters were measured to indicate the overall prostate and body shape, all of these measurements obtained on CT images. A brute force procedure was first performed for a training dataset of 20 patients using image registration to pair subject with similar contours; each subject was served as a target image to which all reaming 19 images were affinity registered. The overlap between the prostate and femoral heads was quantified for each pair using the Dice Similarity Coefficient (DSC). Finally, an atlas selection procedure was designed; relying on the computation of a similarity score defined as a weighted sum of differences between the target and atlas subject anatomical measurement. The algorithm ability to predict the most similar atlas was excellent, achieving mean DSCs of 0.78 ± 0.07 and 0.90 ± 0.02 for the CTV and either femoral head. The proposed atlas selection yielded 0.72 ± 0.11 and 0.87 ± 0.03 for CTV and either femoral head. The DSC obtained with the proposed selection method were slightly lower than the maximum established using brute force, but this does not include potential improvements expected with deformable registration. The proposed atlas selection method provides reasonable segmentation accuracy.

Poster - 32: Atlas Selection for Automated Segmentation of Pelvic CT for Prostate Radiotherapy

International Nuclear Information System (INIS)

Mallawi, Abrar; Farrell, TomTom; Diamond, Kevin-Ross; Wierzbicki, Marcin

2016-01-01

Atlas based-segmentation has recently been evaluated for use in prostate radiotherapy. In a typical approach, the essential step is the selection of an atlas from a database that the best matches of the target image. This work proposes an atlas selection strategy and evaluate it impacts on final segmentation accuracy. Several anatomical parameters were measured to indicate the overall prostate and body shape, all of these measurements obtained on CT images. A brute force procedure was first performed for a training dataset of 20 patients using image registration to pair subject with similar contours; each subject was served as a target image to which all reaming 19 images were affinity registered. The overlap between the prostate and femoral heads was quantified for each pair using the Dice Similarity Coefficient (DSC). Finally, an atlas selection procedure was designed; relying on the computation of a similarity score defined as a weighted sum of differences between the target and atlas subject anatomical measurement. The algorithm ability to predict the most similar atlas was excellent, achieving mean DSCs of 0.78 ± 0.07 and 0.90 ± 0.02 for the CTV and either femoral head. The proposed atlas selection yielded 0.72 ± 0.11 and 0.87 ± 0.03 for CTV and either femoral head. The DSC obtained with the proposed selection method were slightly lower than the maximum established using brute force, but this does not include potential improvements expected with deformable registration. The proposed atlas selection method provides reasonable segmentation accuracy.

Generating patient specific pseudo-CT of the head from MR using atlas-based regression

International Nuclear Information System (INIS)

Sjölund, J; Forsberg, D; Andersson, M; Knutsson, H

2015-01-01

Radiotherapy planning and attenuation correction of PET images require simulation of radiation transport. The necessary physical properties are typically derived from computed tomography (CT) images, but in some cases, including stereotactic neurosurgery and combined PET/MR imaging, only magnetic resonance (MR) images are available. With these applications in mind, we describe how a realistic, patient-specific, pseudo-CT of the head can be derived from anatomical MR images. We refer to the method as atlas-based regression, because of its similarity to atlas-based segmentation. Given a target MR and an atlas database comprising MR and CT pairs, atlas-based regression works by registering each atlas MR to the target MR, applying the resulting displacement fields to the corresponding atlas CTs and, finally, fusing the deformed atlas CTs into a single pseudo-CT. We use a deformable registration algorithm known as the Morphon and augment it with a certainty mask that allows a tailoring of the influence certain regions are allowed to have on the registration. Moreover, we propose a novel method of fusion, wherein the collection of deformed CTs is iteratively registered to their joint mean and find that the resulting mean CT becomes more similar to the target CT. However, the voxelwise median provided even better results; at least as good as earlier work that required special MR imaging techniques. This makes atlas-based regression a good candidate for clinical use. (paper)

PC as physics computer for LHC?

CERN Document Server

Jarp, S; Simmins, A; Yaari, R; Jarp, Sverre; Tang, Hong; Simmins, Antony; Yaari, Refael

1995-01-01

In the last five years, we have seen RISC workstations take over the computing scene that was once controlled by mainframes and supercomputers. In this paper we will argue that the same phenomenon might happen again. A project, active since March this year in the Physics Data Processing group of CERN's CN division is described where ordinary desktop PCs running Windows (NT and 3.11) have been used for creating an environment for running large LHC batch jobs (initially the DICE simulation job of Atlas). The problems encountered in porting both the CERN library and the specific Atlas codes are described together with some encouraging benchmark results when comparing to existing RISC workstations in use by the Atlas collaboration. The issues of establishing the batch environment (Batch monitor, staging software, etc.) are also covered. Finally a quick extrapolation of commodity computing power available in the future is touched upon to indicate what kind of cost envelope could be sufficient for the simulation fa...

Study of an on-line filtering system for the ATLAS detector

International Nuclear Information System (INIS)

Fede, E.

2001-01-01

The first chapter presents today's knowledge about particle physics and a description of the main decay channels and physical signatures associated to the Higgs boson is given. The second chapter is dedicated to the LHC accelerator with a focus on the ATLAS detector and its sub-detectors. The third chapter presents ATLAS triggering system and its data acquisition system. In the fourth chapter the functionalities required for an adequate event filtering system concerning physics issues and data managing are described. The design of a prototype based on a fleet of PC computers linked through an Ethernet network is presented in the fifth chapter

Hippocampal unified multi-atlas network (HUMAN): protocol and scale validation of a novel segmentation tool.

Science.gov (United States)

Amoroso, N; Errico, R; Bruno, S; Chincarini, A; Garuccio, E; Sensi, F; Tangaro, S; Tateo, A; Bellotti, R

2015-11-21

In this study we present a novel fully automated Hippocampal Unified Multi-Atlas-Networks (HUMAN) algorithm for the segmentation of the hippocampus in structural magnetic resonance imaging. In multi-atlas approaches atlas selection is of crucial importance for the accuracy of the segmentation. Here we present an optimized method based on the definition of a small peri-hippocampal region to target the atlas learning with linear and non-linear embedded manifolds. All atlases were co-registered to a data driven template resulting in a computationally efficient method that requires only one test registration. The optimal atlases identified were used to train dedicated artificial neural networks whose labels were then propagated and fused to obtain the final segmentation. To quantify data heterogeneity and protocol inherent effects, HUMAN was tested on two independent data sets provided by the Alzheimer's Disease Neuroimaging Initiative and the Open Access Series of Imaging Studies. HUMAN is accurate and achieves state-of-the-art performance (Dice[Formula: see text] and Dice[Formula: see text]). It is also a robust method that remains stable when applied to the whole hippocampus or to sub-regions (patches). HUMAN also compares favorably with a basic multi-atlas approach and a benchmark segmentation tool such as FreeSurfer.

FTK status and track triggers in ATLAS at HL-LHC

CERN Document Server

ATLAS Collaboration; The ATLAS collaboration

2016-01-01

The expected instantaneous luminosities delivered by the Large Hadron Collider will place continually increasing burdens on the trigger systems of the ATLAS detector. The use of tracking information is key to maintaining a manageable trigger rate while keeping a high efficiency. At the same time, however, track finding is one of the more resource-intensive tasks in the software-based processing farms of the high level trigger system. To support the trigger, ATLAS is building and currently installing the Fast TracK Finder (FTK), a hardware-based system that uses massively parallel pattern recognition in Associative Memory to reconstruct tracks above transverse momenta of 1 GeV across the entire detector at 100 kHz with a latency of ~100 microseconds. In the first-stage of track finding, FTK compares hits in ATLAS silicon detectors against ~1 billion pre-computed track pattern candidates. Track parameters for these candidates, including goodness-of-fit tests, are calculated in FPGAs using a linear approximation...

PanDA Beyond ATLAS: Workload Management for Data Intensive Science

CERN Document Server

Schovancova, J; The ATLAS collaboration; Klimentov, A; Maeno, T; Nilsson, P; Oleynik, D; Panitkin, S; Petrosyan, A; Vaniachine, A; Wenaus, T; Yu, D

2013-01-01

The PanDA Production ANd Distributed Analysis system has been developed by ATLAS to meet the experiment's requirements for a data-driven workload management system for production and distributed analysis processing capable of operating at LHC data processing scale. After 7 years of impressively successful PanDA operation in ATLAS there are also other experiments which can benefit from PanDA in the Big Data challenge, with several at various stages of evaluation and adoption. The new project "Next Generation Workload Management and Analysis System for Big Data" is extending PanDA to meet the needs of other data intensive scientific applications in HEP, astro-particle and astrophysics communities, bio-informatics and other fields as a general solution to large scale workload management. PanDA can utilize dedicated or opportunistic computing resources such as grids, clouds, and High Performance Computing facilities, and is being extended to leverage next generation intelligent networks in automated workflow mana...

Probabilistic atlas based labeling of the cerebral vessel tree

Science.gov (United States)

Van de Giessen, Martijn; Janssen, Jasper P.; Brouwer, Patrick A.; Reiber, Johan H. C.; Lelieveldt, Boudewijn P. F.; Dijkstra, Jouke

2015-03-01

Preoperative imaging of the cerebral vessel tree is essential for planning therapy on intracranial stenoses and aneurysms. Usually, a magnetic resonance angiography (MRA) or computed tomography angiography (CTA) is acquired from which the cerebral vessel tree is segmented. Accurate analysis is helped by the labeling of the cerebral vessels, but labeling is non-trivial due to anatomical topological variability and missing branches due to acquisition issues. In recent literature, labeling the cerebral vasculature around the Circle of Willis has mainly been approached as a graph-based problem. The most successful method, however, requires the definition of all possible permutations of missing vessels, which limits application to subsets of the tree and ignores spatial information about the vessel locations. This research aims to perform labeling using probabilistic atlases that model spatial vessel and label likelihoods. A cerebral vessel tree is aligned to a probabilistic atlas and subsequently each vessel is labeled by computing the maximum label likelihood per segment from label-specific atlases. The proposed method was validated on 25 segmented cerebral vessel trees. Labeling accuracies were close to 100% for large vessels, but dropped to 50-60% for small vessels that were only present in less than 50% of the set. With this work we showed that using solely spatial information of the vessel labels, vessel segments from stable vessels (>50% presence) were reliably classified. This spatial information will form the basis for a future labeling strategy with a very loose topological model.

Nonlocal atlas-guided multi-channel forest learning for human brain labeling.

Science.gov (United States)

Ma, Guangkai; Gao, Yaozong; Wu, Guorong; Wu, Ligang; Shen, Dinggang

2016-02-01

It is important for many quantitative brain studies to label meaningful anatomical regions in MR brain images. However, due to high complexity of brain structures and ambiguous boundaries between different anatomical regions, the anatomical labeling of MR brain images is still quite a challenging task. In many existing label fusion methods, appearance information is widely used. However, since local anatomy in the human brain is often complex, the appearance information alone is limited in characterizing each image point, especially for identifying the same anatomical structure across different subjects. Recent progress in computer vision suggests that the context features can be very useful in identifying an object from a complex scene. In light of this, the authors propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image). In particular, the authors employ a multi-channel random forest to learn the nonlinear relationship between these hybrid features and target labels (i.e., corresponding to certain anatomical structures). Specifically, at each of the iterations, the random forest will output tentative labeling maps of the target image, from which the authors compute spatial label context features and then use in combination with original appearance features of the target image to refine the labeling. Moreover, to accommodate the high inter-subject variations, the authors further extend their learning-based label fusion to a multi-atlas scenario, i.e., they train a random forest for each atlas and then obtain the final labeling result according to the consensus of results from all atlases. The authors have comprehensively evaluated their method on both public LONI_LBPA40 and IXI datasets. To quantitatively evaluate the labeling accuracy, the authors use the dice similarity coefficient

The ATLAS fast tracker processor design

CERN Document Server

Volpi, Guido; Albicocco, Pietro; Alison, John; Ancu, Lucian Stefan; Anderson, James; Andari, Nansi; Andreani, Alessandro; Andreazza, Attilio; Annovi, Alberto; Antonelli, Mario; Asbah, Needa; Atkinson, Markus; Baines, J; Barberio, Elisabetta; Beccherle, Roberto; Beretta, Matteo; Biesuz, Nicolo Vladi; Blair, R E; Bogdan, Mircea; Boveia, Antonio; Britzger, Daniel; Bryant, Partick; Burghgrave, Blake; Calderini, Giovanni; Camplani, Alessandra; Cavaliere, Viviana; Cavasinni, Vincenzo; Chakraborty, Dhiman; Chang, Philip; Cheng, Yangyang; Citraro, Saverio; Citterio, Mauro; Crescioli, Francesco; Dawe, Noel; Dell'Orso, Mauro; Donati, Simone; Dondero, Paolo; Drake, G; Gadomski, Szymon; Gatta, Mauro; Gentsos, Christos; Giannetti, Paola; Gkaitatzis, Stamatios; Gramling, Johanna; Howarth, James William; Iizawa, Tomoya; Ilic, Nikolina; Jiang, Zihao; Kaji, Toshiaki; Kasten, Michael; Kawaguchi, Yoshimasa; Kim, Young Kee; Kimura, Naoki; Klimkovich, Tatsiana; Kolb, Mathis; Kordas, K; Krizka, Karol; Kubota, T; Lanza, Agostino; Li, Ho Ling; Liberali, Valentino; Lisovyi, Mykhailo; Liu, Lulu; Love, Jeremy; Luciano, Pierluigi; Luongo, Carmela; Magalotti, Daniel; Maznas, Ioannis; Meroni, Chiara; Mitani, Takashi; Nasimi, Hikmat; Negri, Andrea; Neroutsos, Panos; Neubauer, Mark; Nikolaidis, Spiridon; Okumura, Y; Pandini, Carlo; Petridou, Chariclia; Piendibene, Marco; Proudfoot, James; Rados, Petar Kevin; Roda, Chiara; Rossi, Enrico; Sakurai, Yuki; Sampsonidis, Dimitrios; Saxon, James; Schmitt, Stefan; Schoening, Andre; Shochet, Mel; Shoijaii, Jafar; Soltveit, Hans Kristian; Sotiropoulou, Calliope-Louisa; Stabile, Alberto; Swiatlowski, Maximilian J; Tang, Fukun; Taylor, Pierre Thor Elliot; Testa, Marianna; Tompkins, Lauren; Vercesi, V; Wang, Rui; Watari, Ryutaro; Zhang, Jianhong; Zeng, Jian Cong; Zou, Rui; Bertolucci, Federico

2015-01-01

The extended use of tracking information at the trigger level in the LHC is crucial for the trigger and data acquisition (TDAQ) system to fulfill its task. Precise and fast tracking is important to identify specific decay products of the Higgs boson or new phenomena, as well as to distinguish the contributions coming from the many collisions that occur at every bunch crossing. However, track reconstruction is among the most demanding tasks performed by the TDAQ computing farm; in fact, complete reconstruction at full Level-1 trigger accept rate (100 kHz) is not possible. In order to overcome this limitation, the ATLAS experiment is planning the installation of a dedicated processor, the Fast Tracker (FTK), which is aimed at achieving this goal. The FTK is a pipeline of high performance electronics, based on custom and commercial devices, which is expected to reconstruct, with high resolution, the trajectories of charged-particle tracks with a transverse momentum above 1 GeV, using the ATLAS inner tracker info...

Deployment of job priority mechanisms in the Italian Cloud of the ATLAS experiment

International Nuclear Information System (INIS)

Doria, Alessandra; Carlino, Gianpaolo; Salvo, Alessandro De; Musto, Elisa; Barchiesi, Alex; Campana, Simone; Ciocca, Claudia; Italiano, Alessandro; Rinaldi, Lorenzo; Salomoni, Davide; Perini, Laura; Pistolese, Massimo; Vaccarossa, Luca; Vilucchi, Elisabetta

2010-01-01

An optimized use of the Grid computing resources in the ATLAS experiment requires the enforcement of a mechanism of job priorities and of resource sharing among the different activities inside the ATLAS VO. This mechanism has been implemented through the VOViews publication in the information system and the fair share implementation per UNIX group in the batch system. The VOView concept consists of publishing resource information, such as running and waiting jobs, as a function of VO groups and roles. The ATLAS Italian Cloud is composed of the CNAF Tier1 and Roma Tier2, with farms based on the LSF batch system, and the Tier2s of Frascati, Milano and Napoli based on PBS/Torque. In this paper we describe how test and deployment of the job priorities has been performed in the cloud, where the VOMS-based regional group /atlas/it has been created. We show that the VOViews are published and correctly managed by the WMS and that the resources allocated to generic VO users, users with production role and users of the /atlas/it group correspond to the defined share.

The ATLAS High Level Trigger Steering Framework and the Trigger 
Configuration System.

CERN Document Server

Pérez Cavalcanti, Tiago; The ATLAS collaboration

2011-01-01

The ATLAS High Level Trigger Steering Framework and the Trigger 
Configuration System.
 
The ATLAS detector system installed in the Large Hadron Collider (LHC) 
at CERN is designed to study proton-proton and nucleus-nucleus 
collisions with a maximum center of mass energy of 14 TeV at a bunch 
collision rate of 40MHz.  In March 2010 the four LHC experiments saw 
the first proton-proton collisions at 7 TeV. Still within the year a 
collision rate of nearly 10 MHz is expected. At ATLAS, events of 
potential interest for ATLAS physics are selected by a three-level 
trigger system, with a final recording rate of about 200 Hz. The first 
level (L1) is implemented in custom hardware; the two levels of 
the high level trigger (HLT) are software triggers, running on large 
farms of standard computers and network devices. 

Within the ATLAS physics program more than 500 trigger signatures are 
defined. The HLT tests each signature on each L1-accepted event; the 
test outcome is recor...

A quality control atlas for scintillation camera systems

International Nuclear Information System (INIS)

Busemann Sokole, E.; Graham, L.S.; Todd-Pokropek, A.; Wegst, A.; Robilotta, C.C.

2002-01-01

Full text: The accurate interpretation of quality control and clinical nuclear medicine image data is coupled to an understanding of image patterns and quantitative results. Understanding is gained by learning from different examples, and knowledge of underlying principles of image production. An Atlas of examples has been created to assist with interpreting quality control tests and recognizing artifacts in clinical examples. The project was initiated and supported by the International Atomic Energy Agency (IAEA). The Atlas was developed and written by Busemann Sokole from image examples submitted from nuclear medicine users from around the world. The descriptive text was written in a consistent format to accompany each image or image set. Each example in the atlas finally consisted of the images; a brief description of the data acquisition, radionuclide/radiopharmaceutical, specific circumstances under which the image was produced; results describing the images and subsequent conclusions; comments, where appropriate, giving guidelines for follow-up strategies and trouble shooting; and occasional literature references. Hardcopy images required digitizing into JPEG format for inclusion into a digital document. Where possible, an example was contained on one page. The atlas was reviewed by an international group of experts. A total of about 250 examples were compiled into 6 sections: planar, SPECT, whole body, camera/computer interface, environment/radioactivity, and display/hardcopy. Subtle loss of image quality may be difficult to detect. SPECT examples, therefore, include simulations demonstrating effects of deterioration in camera performance (e.g. center-of-rotation offset, non-uniformity) or suboptimal clinical performance. The atlas includes normal results, results from poor adjustment of the camera system, poor results obtained at acceptance testing, artifacts due to system malfunction, and artifacts due to environmental situations. Some image patterns are

The ATLAS High Level Trigger Steering Framework and the Trigger Configuration System.

CERN Document Server

Perez Cavalcanti, Tiago; The ATLAS collaboration

2011-01-01

The ATLAS detector system installed in the Large Hadron Collider (LHC) at CERN is designed to study proton-proton and nucleus-nucleus collisions with a maximum centre of mass energy of 14 TeV at a bunch collision rate of 40MHz. In March 2010 the four LHC experiments saw the first proton-proton collisions at 7 TeV. Still within the year a collision rate of nearly 10 MHz is expected. At ATLAS, events of potential interest for ATLAS physics are selected by a three-level trigger system, with a final recording rate of about 200 Hz. The first level (L1) is implemented in custom hardware; the two levels of the high level trigger (HLT) are software triggers, running on large farms of standard computers and network devices. Within the ATLAS physics program more than 500 trigger signatures are defined. The HLT tests each signature on each L1-accepted event; the test outcome is recorded for later analysis. The HLT-Steering is responsible for this. It foremost ensures the independent test of each signature, guarantying u...