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001032519 005__ 20250822121304.0
001032519 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-06308
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001032519 041__ $$aEnglish
001032519 1001_ $$0P:(DE-Juel1)187395$$aJohn, Chelsea Maria$$b0$$ufzj
001032519 1112_ $$aOpenGPT-X Forum$$cBerlin$$d2024-11-05 - 2024-11-05$$wGermany
001032519 245__ $$aCARAML: Systematic Evaluation of AI Workloads on Accelerators
001032519 260__ $$c2024
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001032519 520__ $$aThe rapid advancement of machine learning (ML) technologies has driven the development of specialized hardware accelerators designed to facilitate more efficient model training. This paper introduces the CARAML benchmark suite, which is employed to assess performance and energy consumption during the training of transformer-based large language models and computer vision models on a range of hardware accelerators, including systems from NVIDIA, AMD, and Graphcore. CARAML provides a compact, automated, extensible, and reproducible framework for assessing the performance and energy of ML workloads across various novel hardware architectures. The design and implementation of CARAML, along with a custom power measurement tool called jpwr, are discussed in detail.
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001032519 536__ $$0G:(EU-Grant)955513$$aMAELSTROM - MAchinE Learning for Scalable meTeoROlogy and cliMate (955513)$$c955513$$fH2020-JTI-EuroHPC-2019-1$$x3
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001032519 7001_ $$0P:(DE-Juel1)172888$$aNassyr, Stepan$$b1$$eCorresponding author$$ufzj
001032519 7001_ $$0P:(DE-Juel1)145478$$aHerten, Andreas$$b2$$eCorresponding author$$ufzj
001032519 7001_ $$0P:(DE-Juel1)192254$$aPenke, Carolin$$b3$$eCorresponding author$$ufzj
001032519 8564_ $$uhttps://juser.fz-juelich.de/record/1032519/files/CARAML%20Poster.pdf$$yOpenAccess
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001032519 9141_ $$y2024
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