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.

翻译：近年来，GPU已成为高性能计算领域计算性能的关键驱动力。随着Frontier超级计算机的部署，它们开启了百亿亿次计算时代；更大规模的系统（如Aurora、El Capitan、JUPITER）正在建设中。然而，早期NVIDIA及其CUDA编程模型的主导地位已发生改变：当前HPC GPU领域包含三家厂商（AMD、Intel、NVIDIA），各自拥有原生及衍生编程模型。虽然选择众多，但并非所有模型都支持所有平台，尤其当需要支持Fortran时；此外，某些限制可能适用。对科研程序员而言，面对如此丰富的选择与限制，导航变得困难。本文通过将GPU平台与支持的编程模型进行匹配，并以简洁表格呈现及详细注释进一步阐述，提供了一份指南。同时，对模型在平台上的支持程度进行了评估。