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  1. Article ; Online: Porting HEP Parameterized Calorimeter Simulation Code to GPUs.

    Dong, Zhihua / Gray, Heather / Leggett, Charles / Lin, Meifeng / Pascuzzi, Vincent R / Yu, Kwangmin

    Frontiers in big data

    2021  Volume 4, Page(s) 665783

    Abstract: The High Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), traditionally consume large amounts of CPU cycles for detector simulations and data analysis, but rarely use compute accelerators such as GPUs. As the LHC is ... ...

    Abstract The High Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), traditionally consume large amounts of CPU cycles for detector simulations and data analysis, but rarely use compute accelerators such as GPUs. As the LHC is upgraded to allow for higher luminosity, resulting in much higher data rates, purely relying on CPUs may not provide enough computing power to support the simulation and data analysis needs. As a proof of concept, we investigate the feasibility of porting a HEP parameterized calorimeter simulation code to GPUs. We have chosen to use FastCaloSim, the ATLAS fast parametrized calorimeter simulation. While FastCaloSim is sufficiently fast such that it does not impose a bottleneck in detector simulations overall, significant speed-ups in the processing of large samples can be achieved from GPU parallelization at both the particle (intra-event) and event levels; this is especially beneficial in conditions expected at the high-luminosity LHC, where extremely high per-event particle multiplicities will result from the many simultaneous proton-proton collisions. We report our experience with porting FastCaloSim to NVIDIA GPUs using CUDA. A preliminary Kokkos implementation of FastCaloSim for portability to other parallel architectures is also described.
    Language English
    Publishing date 2021-06-25
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2624-909X
    ISSN (online) 2624-909X
    DOI 10.3389/fdata.2021.665783
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Evolution of HEP Processing Frameworks

    Jones, Christopher D. / Knoepfel, Kyle / Calafiura, Paolo / Leggett, Charles / Tsulaia, Vakhtang

    2022  

    Abstract: HEP data-processing software must support the disparate physics needs of many experiments. For both collider and neutrino environments, HEP experiments typically use data-processing frameworks to manage the computational complexities of their large-scale ...

    Abstract HEP data-processing software must support the disparate physics needs of many experiments. For both collider and neutrino environments, HEP experiments typically use data-processing frameworks to manage the computational complexities of their large-scale data processing needs. Data-processing frameworks are being faced with new challenges this decade. The computing landscape has changed from the past three decades of homogeneous single-core x86 batch jobs running on grid sites. Frameworks must now work on a heterogeneous mixture of different platforms: multi-core machines, different CPU architectures, and computational accelerators; and different computing sites: grid, cloud, and high-performance computing. We describe these challenges in more detail and how frameworks may confront them. Given their historic success, frameworks will continue to be critical software systems that enable HEP experiments to meet their computing needs. Frameworks have weathered computing revolutions in the past; they will do so again with support from the HEP community

    Comment: Contribution to Snowmass 2021
    Keywords Computer Science - Distributed ; Parallel ; and Cluster Computing ; High Energy Physics - Experiment ; Physics - Data Analysis ; Statistics and Probability
    Publishing date 2022-03-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Portable Programming Model Exploration for LArTPC Simulation in a Heterogeneous Computing Environment

    Lin, Meifeng / Dong, Zhihua / Wang, Tianle / Atif, Mohammad / Battacharya, Meghna / Knoepfel, Kyle / Leggett, Charles / Viren, Brett / Yu, Haiwang

    OpenMP vs. SYCL

    2023  

    Abstract: The evolution of the computing landscape has resulted in the proliferation of diverse hardware architectures, with different flavors of GPUs and other compute accelerators becoming more widely available. To facilitate the efficient use of these ... ...

    Abstract The evolution of the computing landscape has resulted in the proliferation of diverse hardware architectures, with different flavors of GPUs and other compute accelerators becoming more widely available. To facilitate the efficient use of these architectures in a heterogeneous computing environment, several programming models are available to enable portability and performance across different computing systems, such as Kokkos, SYCL, OpenMP and others. As part of the High Energy Physics Center for Computational Excellence (HEP-CCE) project, we investigate if and how these different programming models may be suitable for experimental HEP workflows through a few representative use cases. One of such use cases is the Liquid Argon Time Projection Chamber (LArTPC) simulation which is essential for LArTPC detector design, validation and data analysis. Following up on our previous investigations of using Kokkos to port LArTPC simulation in the Wire-Cell Toolkit (WCT) to GPUs, we have explored OpenMP and SYCL as potential portable programming models for WCT, with the goal to make diverse computing resources accessible to the LArTPC simulations. In this work, we describe how we utilize relevant features of OpenMP and SYCL for the LArTPC simulation module in WCT. We also show performance benchmark results on multi-core CPUs, NVIDIA and AMD GPUs for both the OpenMP and the SYCL implementations. Comparisons with different compilers will also be given where appropriate.

    Comment: 6 pages, 6 figures. Proceedings of 21st International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2022)
    Keywords High Energy Physics - Experiment ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Physics - Computational Physics
    Subject code 000
    Publishing date 2023-04-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  4. Book ; Online: Porting HEP Parameterized Calorimeter Simulation Code to GPUs

    Dong, Zhihua / Gray, Heather / Leggett, Charles / Lin, Meifeng / Pascuzzi, Vincent R. / Yu, Kwangmin

    2021  

    Abstract: The High Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), traditionally consume large amounts of CPU cycles for detector simulations and data analysis, but rarely use compute accelerators such as GPUs. As the LHC is ... ...

    Abstract The High Energy Physics (HEP) experiments, such as those at the Large Hadron Collider (LHC), traditionally consume large amounts of CPU cycles for detector simulations and data analysis, but rarely use compute accelerators such as GPUs. As the LHC is upgraded to allow for higher luminosity, resulting in much higher data rates, purely relying on CPUs may not provide enough computing power to support the simulation and data analysis needs. As a proof of concept, we investigate the feasibility of porting a HEP parameterized calorimeter simulation code to GPUs. We have chosen to use FastCaloSim, the ATLAS fast parametrized calorimeter simulation. While FastCaloSim is sufficiently fast such that it does not impose a bottleneck in detector simulations overall, significant speed-ups in the processing of large samples can be achieved from GPU parallelization at both the particle (intra-event) and event levels; this is especially beneficial in conditions expected at the high-luminosity LHC, where extremely high per-event particle multiplicities will result from the many simultaneous proton-proton collisions. We report our experience with porting FastCaloSim to NVIDIA GPUs using CUDA. A preliminary Kokkos implementation of FastCaloSim for portability to other parallel architectures is also described.

    Comment: 15 pages, 1 figure, 8 tables, 2 listings, submitted to Frontiers in Big Data (Big Data in AI and High Energy Physics)
    Keywords High Energy Physics - Experiment ; Computer Science - Distributed ; Parallel ; and Cluster Computing ; Physics - Computational Physics
    Subject code 004
    Publishing date 2021-03-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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    Inter-library loan at ZB MED

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  5. Book ; Online: Evaluating Portable Parallelization Strategies for Heterogeneous Architectures in High Energy Physics

    Atif, Mohammad / Battacharya, Meghna / Calafiura, Paolo / Childers, Taylor / Dewing, Mark / Dong, Zhihua / Gutsche, Oliver / Habib, Salman / Knoepfel, Kyle / Kortelainen, Matti / Kwok, Ka Hei Martin / Leggett, Charles / Lin, Meifeng / Pascuzzi, Vincent / Strelchenko, Alexei / Tsulaia, Vakhtang / Viren, Brett / Wang, Tianle / Yeo, Beomki /
    Yu, Haiwang

    2023  

    Abstract: High-energy physics (HEP) experiments have developed millions of lines of code over decades that are optimized to run on traditional x86 CPU systems. However, we are seeing a rapidly increasing fraction of floating point computing power in leadership- ... ...

    Abstract High-energy physics (HEP) experiments have developed millions of lines of code over decades that are optimized to run on traditional x86 CPU systems. However, we are seeing a rapidly increasing fraction of floating point computing power in leadership-class computing facilities and traditional data centers coming from new accelerator architectures, such as GPUs. HEP experiments are now faced with the untenable prospect of rewriting millions of lines of x86 CPU code, for the increasingly dominant architectures found in these computational accelerators. This task is made more challenging by the architecture-specific languages and APIs promoted by manufacturers such as NVIDIA, Intel and AMD. Producing multiple, architecture-specific implementations is not a viable scenario, given the available person power and code maintenance issues. The Portable Parallelization Strategies team of the HEP Center for Computational Excellence is investigating the use of Kokkos, SYCL, OpenMP, std::execution::parallel and alpaka as potential portability solutions that promise to execute on multiple architectures from the same source code, using representative use cases from major HEP experiments, including the DUNE experiment of the Long Baseline Neutrino Facility, and the ATLAS and CMS experiments of the Large Hadron Collider. This cross-cutting evaluation of portability solutions using real applications will help inform and guide the HEP community when choosing their software and hardware suites for the next generation of experimental frameworks. We present the outcomes of our studies, including performance metrics, porting challenges, API evaluations, and build system integration.

    Comment: 18 pages, 9 Figures, 2 Tables
    Keywords High Energy Physics - Experiment ; Computer Science - Distributed ; Parallel ; and Cluster Computing
    Subject code 004
    Publishing date 2023-06-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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