# GPU Programming Model vs. Vendor Compatibility Overview (preprint)

**URL:** <https://fortran-lang.discourse.group/t/gpu-programming-model-vs-vendor-compatibility-overview-preprint/6504>\
**Category:** Uncategorized\
**Created:** [September 13, 2023, 2:34pm UTC](https://fortran-lang.discourse.group/t/gpu-programming-model-vs-vendor-compatibility-overview-preprint/6504 "2023-09-13T14:34:42Z")\
**Posts on this page:** 2\
**Page:** 1

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**Author:** ![Beliavsky](https://avatars.discourse-cdn.com/v4/letter/b/ba8739/32.png) [@Beliavsky](https://fortran-lang.discourse.group/u/Beliavsky)\
**Post date:** [September 13, 2023, 2:34pm UTC](https://fortran-lang.discourse.group/t/gpu-programming-model-vs-vendor-compatibility-overview-preprint/6504/1 "2023-09-13T14:34:42Z")

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[Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview](https://arxiv.org/abs/2309.05445)  
by Andreas Herten  
arXiv 12 Sep 2023

> **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.

> **Conclusion** :  
> This paper presented a methodology to categorize the support of  
> programming models on HPC GPU devices, assessing the level of  
> support and the provider (vendor or third-party). The results for a  
> number of selected models on GPUs of three vendors (AMD, Intel,  
> NVIDIA) were presented in Figure 1, accompanied by extensive  
> descriptions in section 4. The limitations of the method and some  
> key caveats of the presentation were discussed in section 5.
> 
> The support for NVIDIA GPUs can be considered most comprehensive,  
> founded in their long-time prevalence in the field.CUDA  
> is possibly the most famous GPU programming model, and both  
> other vendors (AMD, Intel) provide tools for converting CUDA  
> C/C++ to their nativemodel (HIP, SYCL). AMDdesigned HIP closely  
> to mimic CUDA-like programming and enable it other platforms.  
> And, indeed, NVIDIA and AMD GPUs can be used from the same  
> source code, and recently also Intel GPUs with chipStar. SYCL is an  
> entirely different programming model compared to CUDA or HIP,  
> but it also supports all three GPU platform; either by the work  
> by Intel or the community (Open SYCL). While OpenACC can be  
> used on NVIDIA and AMD GPUs, support for Intel GPUs does not  
> exist. OpenMP, on the other hand, is supported on all three platforms  
> – and even for both C++ and Fortran. Standard language  
> parallelism appears to be the model with the fastest change at the  
> moment, with multiple new projects in progress for all three platforms.  
> Kokkos and Alpaka both provide higher-level abstractions  
> and support all three platform. Python, a somewhat outlier in the  
> list, is also well-supported by all three platforms.
> 
> While the C++ support appears to be well on the way to good  
> compatibility and portability, the situation looks severely different  
> for Fortran. The only natively supported programming model on  
> all three platforms is OpenMP.  
> …

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**Author:** ![wyphan](https://yyz2.discourse-cdn.com/free1/user_avatar/fortran-lang.discourse.group/wyphan/32/632_2.png) [@wyphan](https://fortran-lang.discourse.group/u/wyphan)\
**Post date:** [September 13, 2023, 4:42pm UTC](https://fortran-lang.discourse.group/t/gpu-programming-model-vs-vendor-compatibility-overview-preprint/6504/2 "2023-09-13T16:42:14Z")

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Nice, this is a formalized version of the original table by the same author here:

> **[GPU Vendor/Programming Model Compatibility Table](https://x-dev.pages.jsc.fz-juelich.de/2022/11/02/gpu-vendor-model-compat.html)**
>
> For a recent talk at DKRZ in the scope of the natESM project, I created a table summarizing the current state of using a certain programming model on a GPU of a certain vendor, for C++ and Fortran. Since it lead to quite a discussion in the session,...