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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.17526 |
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| _version_ | 1866915752984969216 |
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| author | Atif, Mohammad Bhattacharya, Meghna Dewing, Mark Dong, Zhihua Esseiva, Julien Gutsche, Oliver Kortelainen, Matti Kwok, Ka Hei Martin Leggett, Charles Lin, Meifeng Strelchenko, Aleksei Tsulaia, Vakhang Viren, Brett Wang, Tianle Yu, Haiwang |
| author_facet | Atif, Mohammad Bhattacharya, Meghna Dewing, Mark Dong, Zhihua Esseiva, Julien Gutsche, Oliver Kortelainen, Matti Kwok, Ka Hei Martin Leggett, Charles Lin, Meifeng Strelchenko, Aleksei Tsulaia, Vakhang Viren, Brett Wang, Tianle Yu, Haiwang |
| contents | GPUs have become the dominant source of computing power for high performance computing and are increasingly being used across the High Energy Physics computing landscape for a wide variety of tasks. Though NVIDIA is currently the main provider of GPUs, AMD and Intel are rapidly increasing their market share. As a result, programming using a vendor-specific language such as CUDA can significantly reduce deployment choices. There are a number of portability layers such as Kokkos, Alpaka, SYCL, OpenMP and std::par that permit execution on a broad range of GPU and CPU architectures, significantly increasing the flexibility of application programmers. However, each of these portability layers has its own characteristics, performing better at some tasks and worse at others, or placing limitations on aspects of the application. In this presentation, we report on a study of application and kernel characteristics that can influence the choice of a portability layer and show how each layer handles these characteristics. We have analyzed representative heterogeneous applications from CMS (patatrack and p2r), DUNE (Wire-Cell Toolkit), and ATLAS (FastCaloSim) to identify key application characteristics that have different behaviors for the various portability technologies. Using these results, developers can make more informed decisions on which GPU portability technology is best suited to their application. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_17526 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Evaluating Application Characteristics for GPU Portability Layer Selection Atif, Mohammad Bhattacharya, Meghna Dewing, Mark Dong, Zhihua Esseiva, Julien Gutsche, Oliver Kortelainen, Matti Kwok, Ka Hei Martin Leggett, Charles Lin, Meifeng Strelchenko, Aleksei Tsulaia, Vakhang Viren, Brett Wang, Tianle Yu, Haiwang High Energy Physics - Experiment GPUs have become the dominant source of computing power for high performance computing and are increasingly being used across the High Energy Physics computing landscape for a wide variety of tasks. Though NVIDIA is currently the main provider of GPUs, AMD and Intel are rapidly increasing their market share. As a result, programming using a vendor-specific language such as CUDA can significantly reduce deployment choices. There are a number of portability layers such as Kokkos, Alpaka, SYCL, OpenMP and std::par that permit execution on a broad range of GPU and CPU architectures, significantly increasing the flexibility of application programmers. However, each of these portability layers has its own characteristics, performing better at some tasks and worse at others, or placing limitations on aspects of the application. In this presentation, we report on a study of application and kernel characteristics that can influence the choice of a portability layer and show how each layer handles these characteristics. We have analyzed representative heterogeneous applications from CMS (patatrack and p2r), DUNE (Wire-Cell Toolkit), and ATLAS (FastCaloSim) to identify key application characteristics that have different behaviors for the various portability technologies. Using these results, developers can make more informed decisions on which GPU portability technology is best suited to their application. |
| title | Evaluating Application Characteristics for GPU Portability Layer Selection |
| topic | High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2601.17526 |