Portability of Fortran's 'do concurrent' on GPUs II

2026-08-20

AI Summary

研究探讨了使用Fortran的'do concurrent'循环在三大GPU厂商上的可移植性，通过标准语言和必要时结合OpenMP等指令API实现GPU加速，促进科学HPC代码性能的跨平台兼容。

Abstract

There continues to be growing interest in using standard language constructs for parallel and accelerated HPC computing, avoiding the need for (sometimes vendor-specific) external APIs. For Fortran applications, language features such as 'do concurrent' loops open the door for compilers to implement multi-threaded, GPU-accelerated, and even distributed multi-node code with only the standard language. Here, we explore the current status of using 'do concurrent' for GPU-accelerated Fortran applications across three major GPU vendors (NVIDIA, AMD, and Intel). Using a production application, we test their current capabilities, showing where the standard language alone can be used, and where augmenting the code with a directive-based API (e.g., OpenMP) is still desirable or required. Multi-GPU tests are performed with GPU-aware MPI libraries. We find that the three GPU vendors can now GPU-accelerate pure Fortran (zero directives), but that manual data movement directives can help with performance and compatibility. The results show that there is rapid advancement towards making GPU-accelerated scientific HPC code performance portable using the Fortran standard language.

Problem

• do concurrent
• GPU acceleration
• Fortran
• HPC
• portability

Innovation

• do concurrent
• GPU acceleration
• Fortran
• standard language
• multi-GPU