# Taking GPU Programming Models to Task for Performance Portability

arXiv: https://arxiv.org/abs/2402.08950 · HTML: https://mlsanity.com/paper/2402.08950/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [What Irregularity Costs: CUDA C++, Rust, and Triton on a Hash-Blocked GPU Workload](https://mlsanity.com/paper/2608.08287/) (2026)

## Abstract (arXiv)

Portability is critical to ensuring high productivity in developing and maintaining scientific software as the diversity in on-node hardware architectures increases. While several programming models provide portability for diverse GPU systems, they don't make any guarantees about performance portability. In this work, we explore several programming models -- CUDA, HIP, Kokkos, RAJA, OpenMP, OpenACC, and SYCL, to assess the consistency of their performance across NVIDIA and AMD GPUs. We use five proxy applications from different scientific domains, create implementations where missing, and use them to present a comprehensive comparative evaluation of the performance portability of these programming models. We provide a Spack scripting-based methodology to ensure reproducibility of experiments conducted in this work. Finally, we analyze the reasons for why some programming models underperform in certain scenarios and in some cases, present performance optimizations to the proxy applications.