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001018971 037__ $$aFZJ-2023-05040
001018971 041__ $$aEnglish
001018971 1001_ $$0P:(DE-Juel1)145478$$aHerten, Andreas$$b0$$eCorresponding author
001018971 1112_ $$aSC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis$$cDenver, CO$$d2023-11-12 - 2023-11-17$$gSC-W23$$wUSA
001018971 245__ $$aMany Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview
001018971 260__ $$bACM New York, NY, USA$$c2023
001018971 29510 $$aProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
001018971 300__ $$a1019–1026
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001018971 500__ $$aarXiv: https://arxiv.org/abs/2309.05445 HTML-version of table: https://x-dev.pages.jsc.fz-juelich.de/models/ Data repository: https://github.com/AndiH/gpu-lang-compat
001018971 520__ $$aIn recent history, GPUs became a key driver of compute performance in HPC. With the installation of the Frontier supercomputer, they became the enablers of the Exascale era; further largest-scale installations are in progress (Aurora, El Capitan, JUPITER). But the early-day dominance by NVIDIA and their CUDA programming model has changed: The current HPC GPU landscape features three vendors (AMD, Intel, NVIDIA), each with native and derived programming models. The choices are ample, but not all models are supported on all platforms, especially if support for Fortran is needed; in addition, some restrictions might apply. It is hard for scientific programmers to navigate this abundance of choices and limits. This paper gives a guide by matching the GPU platforms with supported programming models, presented in a concise table and further elaborated in detailed comments. An assessment is made regarding the level of support of a model on a platform.
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