# Many Cores, Many Models: GPU Programming Model vs. Vendor Compatibility Overview

arXiv: https://arxiv.org/abs/2309.05445 · HTML: https://mlsanity.com/paper/2309.05445/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2023.

## Built on by (most cited first, 3 of 3)

- [From Legacy Fortran to Portable Kokkos: An Autonomous Agentic AI Workflow](https://mlsanity.com/paper/2509.12443/) (2025)
- [Performance and Power: Systematic Evaluation of AI Workloads on Accelerators with CARAML](https://mlsanity.com/paper/2409.12994/) (2024)
- [VkVIO: Cross-platform GPU Acceleration for Visual-Inertial Odometry with Vulkan](https://mlsanity.com/paper/2609.30459/) (2026)

## Abstract (arXiv)

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.