Optimization and Portability of a Fusion OpenACC-based FORTRAN HPC Code from NVIDIA to AMD GPUs

Igor Sfiligoi, Emily A. Belli, Jeff Candy, Reuben D. Budiardja

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

NVIDIA has been the main provider of GPU hardware in HPC systems for over a decade. Most applications that benefit from GPUs have thus been developed and optimized for the NVIDIA software stack. Recent exascale HPC systems are, however, introducing GPUs from other vendors, e.g. with the AMD GPU-based OLCF Frontier system just becoming available. AMD GPUs cannot be directly accessed using the NVIDIA software stack, and require a porting effort by the application developers. This paper provides an overview of our experience porting and optimizing the CGYRO code, a widely-used fusion simulation tool based on FORTRAN with OpenACC-based GPU acceleration. While the porting from the NVIDIA compilers was relatively straightforward using the CRAY compilers on the AMD systems, the performance optimization required more fine-tuning. In the optimization effort, we uncovered code sections that had performed well on NVIDIA GPUs, but were unexpectedly slow on AMD GPUs. After AMD-targeted code optimizations, performance on AMD GPUs has increased to meet our expectations. Modest speed improvements were also seen on NVIDIA GPUs, which was an unexpected benefit of this exercise.

Original languageEnglish
Title of host publicationPEARC 2023 - Computing for the common good
Subtitle of host publicationPractice and Experience in Advanced Research Computing
PublisherAssociation for Computing Machinery, Inc
Pages246-250
Number of pages5
ISBN (Electronic)9781450399852
DOIs
StatePublished - Jul 23 2023
Event2023 Practice and Experience in Advanced Research Computing, PEARC 2023 - Portland, United States
Duration: Jul 23 2023Jul 27 2023

Publication series

NamePEARC 2023 - Computing for the common good: Practice and Experience in Advanced Research Computing

Conference

Conference2023 Practice and Experience in Advanced Research Computing, PEARC 2023
Country/TerritoryUnited States
CityPortland
Period07/23/2307/27/23

Funding

This work was partially supported by the U.S. Department of Energy under awards DE-FG02-95ER54309, DE-FC02-06ER54873, and DESC0017992, and by U.S. National Science Foundation (NSF) Grant OAC-1826967. An award of computer time was provided by the INCITE and ALCC programs. This research used resources of the Oak Ridge Leadership Computing Facility, which is an Office of Science User Facility supported under Contract DE-AC05-00OR22725. Computing resources were also provided by the National Energy Research Scientific Computing Center, which is an Office of Science User Facility supported under Contract DE-AC02-05CH11231.

Keywords

  • Benchmarking
  • FFT
  • Fusion science
  • GPU
  • High Performance Computing
  • OpenACC
  • Performance

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