Contribution to a conference proceedings/Contribution to a book

FZJ-2023-05040

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview

Herten, A. (Corresponding author)FZJ*

2023
ACM New York, NY, USA

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Please use a persistent id in citations: doi:10.1145/3624062.3624178  doi:10.34734/FZJ-2023-05040

Abstract: 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.

Note: arXiv: 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

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5122 - Future Computing & Big Data Systems (POF4-512) (POF4-512)
2. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
3. ATML-X-DEV - ATML Accelerating Devices (ATML-X-DEV) (ATML-X-DEV)

Appears in the scientific report 2023

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