Portability of Fortran’s ‘do concurrent’ on GPUs II

Portability of Fortran’s ‘do concurrent’ on GPUs II
Ronald M. Caplan, Miko M. Stulajter, Jon A. Linker, Jeff Larkin, Nikolaos Tselepidis, Harald Servat, Shiquan Su, Giacomo Capodaglio, Johanna Potyka
arXiv:2608.20586v1 Announce Type: cross
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.arXiv:2608.20586v1 Announce Type: cross
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.