This title appears in the Scientific Report :
2023
Please use the identifier:
http://dx.doi.org/10.1145/3624062.3624178 in citations.
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2023-05040 in citations.
Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview
Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview
In 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 t...
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Personal Name(s): | Herten, Andreas (Corresponding author) |
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Contributing Institute: |
Jülich Supercomputing Center; JSC |
Published in: |
Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis |
Imprint: |
ACM New York, NY, USA
2023
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Physical Description: |
1019–1026 |
DOI: |
10.1145/3624062.3624178 |
DOI: |
10.34734/FZJ-2023-05040 |
Conference: | SC-W 2023: Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, Denver, CO (USA), 2023-11-12 - 2023-11-17 |
Document Type: |
Contribution to a book Contribution to a conference proceedings |
Research Program: |
Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups Future Computing & Big Data Systems |
Link: |
OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.34734/FZJ-2023-05040 in citations.
In 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. |