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ACAT 2011

5–9 Sept 2011
Europe/London timezone

Session

Thursday 08th - Computing Technology for Physics Research

8 Sept 2011, 14:00

Presentation materials

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  1. 83. Multicore in Production: Advantages and Limits of the Multi-process Approach.
    Vakhtang Tsulaia (LBL)
    08/09/2011, 14:00
    Track 1: Computing Technology for Physics Research
    Parallel talk
    The shared memory architecture of multicore CPUs provides HENP developers with the opportunity to reduce the memory footprint of their applications by sharing memory pages between the cores in a processor. ATLAS pioneered the multi-process approach to parallelizing HENP applications. Using Linux fork() and the Copy On Write mechanism we implemented a simple event task farm which allows to...
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  2. 90. An Exploration of SciDB in the Context of Emerging Technologies for Data Stores in Particle Physics and Cosmology
    Dr David Malon (High Energy Physics Division-Argonne National Laboratory (ANL))
    08/09/2011, 14:25
    Track 1: Computing Technology for Physics Research
    Parallel talk
    Traditional relational databases have not always been well matched to the needs of data-intensive sciences, but efforts are underway within the database community to attempt to address many of the requirements of large-scale scientific data management. One such effort is the open-source project SciDB. Since its earliest incarnations, SciDB has been designed for scalability in parallel and...
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  3. 91. Lessons from Static Analysis on HEP Software
    Axel Naumann (CERN)
    08/09/2011, 14:50
    Track 1: Computing Technology for Physics Research
    Parallel talk
    Coverity's static analysis tool has been run on most of the LHC experiments' frameworks, as well as several of the packages provided to them (e.g. ROOT, Geant4). I will present how static analysis works and why it is complimentary to dynamic checkers like valgrind or test suites; typical issues discovered by static analysis; and lessons learned.
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  4. 92. Moving ROOT Forward.
    Fons Rademakers (CERN)
    08/09/2011, 15:15
    Track 1: Computing Technology for Physics Research
    Parallel talk
    Now that the LHC has started the LHC experiments crave for stability in ROOT, however progress in computing technology is not stopping and to keep ROOT up to date and compatible with new technologies requires a lot of work. In this presentation we will show what we are currently working on and what new technologies we try to exploit.
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  5. 95. Evaluation of likelihood functions on CPU and GPU devices
    Mr Yngve Sneen Lindal (Norges Teknisk-Naturvitens. Univ. (NTNU) and CERN openlab)
    08/09/2011, 16:10
    Track 1: Computing Technology for Physics Research
    Parallel talk
    In this work we present the parallel implementations of an algorithm used to evaluate the likelihood function of the data analysis. The implementations run on CPU and GPU, respectively, and both devices cooperatively (hybrid). Therefore the execution of the algorithm can take full advantage from users commodity systems, like desktops and laptops, using entirely the hardware at disposal. CPU...
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  6. 96. Do regions of ALICE matter? (Social relationships and data exchanges in the Grid)
    Mr Federico Carminati (CERN, Geneva, Switzerland)
    08/09/2011, 16:35
    Track 1: Computing Technology for Physics Research
    Parallel talk
    Following a previous publication, this study aims at investigating the impact of regional affiliations of centres on the organisation of collaboration within the Distributed Computing ALICE infrastructure, based on social networks methods. A self-administered questionnaire was sent to all centre managers about support, email interactions and wished collaborations in the infrastructure. Several...
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  7. 98. Efficient Pseudo-Random Number Generation for Monte-Carlo Simulations Using Graphic Processors
    Dr Federico Carminati (CERN)
    08/09/2011, 17:00
    Track 1: Computing Technology for Physics Research
    Parallel talk
    The future of high power computing is evolving towards the efficient use of highly parallel computing environment. The class of devices that has been designed having parallelism features in mind is the Graphics Processing Units (GPU) which are highly parallel, multithreaded computing devices. One application where the use of massive parallelism comes instinctively is Monte-Carlo...
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