Performance, Portability, and Productivity of HIP on GPUs with NAS Parallel Benchmarks

• Gabriell Araujo PUCRS
• Dalvan Griebler PUCRS
• Luiz Gustavo Fernandes PUCRS

Resumo

Graphics Processing Units (GPUs) are powerful, massively parallel processors that have become ubiquitous in modern computing. In recent years, the GPU market has diversified, with vendors like AMD and Intel offering high-performance alternatives to NVIDIA. However, most applications are written using NVIDIA’s CUDA API, which is incompatible with non-NVIDIA GPUs, creating significant challenges for developers who must port their code to different architectures. To address this issue, AMD developed the Heterogeneous-Compute Interface for Portability (HIP), an open-source API for cross-vendor GPU programming. However, HIP is relatively new, leaving gaps in the literature regarding its performance, portability, and productivity. In this paper, we evaluate HIP using the NAS Parallel Benchmarks (NPB), a CFD-based suite maintained by NASA. We present the first HIP-based implementation of NPB and conduct experiments on integrated and discrete GPUs from NVIDIA, AMD, and Intel. Our results provide novel insights into HIP’s performance and portability, particularly for integrated GPUs and Intel discrete GPUs, which have been underrepresented in prior studies. We also assess productivity using different metrics to quantify the programming effort of HIP-based implementations. This work addresses key gaps in the literature, offering valuable data and insights for developers targeting emerging GPU architectures.

Palavras-chave: Productivity, Codes, High performance computing, Computational fluid dynamics, Graphics processing units, Computer architecture, Benchmark testing, Programming, Hip, Software development management, HIP, CUDA, ROCm, oneAPI, GPU, GPU Programming, Code Portability, Programming Productivity, Performance Evaluation, NAS Parallel Benchmarks, Computational Fluid Dynamics, High-Performance Computing, NVIDIA GPUs, AMD GPUs, Intel GPUs