Portability and Scalability of OpenMP Offloading on State-of-the-Art Accelerators

Yehonatan Fridman, Guy Tamir, Gal Oren

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

Abstract

Over the last decade, most of the increase in computing power has been gained by advances in accelerated many-core architectures, mainly in the form of GPGPUs. While accelerators achieve phenomenal performances in various computing tasks, their utilization requires code adaptations and transformations. Thus, OpenMP, the most common standard for multi-threading in scientific computing applications, introduced offloading capabilities between host (CPUs) and accelerators since v4.0, with increasing support in the successive v4.5, v5.0, v5.1, and the latest v5.2 versions. Recently, two state-of-the-art GPUs – the Intel Ponte Vecchio Max 1100 and the NVIDIA A100 GPUs – were released to the market, with the oneAPI and NVHPC compilers for offloading, correspondingly. In this work, we present early performance results of OpenMP offloading capabilities to these devices while specifically analyzing the portability of advanced directives (using SOLLVE’s OMPVV test suite) and the scalability of the hardware in representative scientific mini-app (the LULESH benchmark). Our results show that the coverage for version 4.5 is nearly complete in both latest NVHPC and oneAPI tools. However, we observed a lack of support in versions 5.0, 5.1, and 5.2, which is particularly noticeable when using NVHPC. From the performance perspective, we found that the PVC1100 and A100 are relatively comparable on the LULESH benchmark. While the A100 is slightly better due to faster memory bandwidth, the PVC1100 reaches the next problem size (4003 ) scalably due to the larger memory size. The results are available at: https://github.com/Scientific-Computing-Lab-NRCN/Accel-OpenMP-Portability-Scalability.

Original languageEnglish
Title of host publicationHigh Performance Computing - ISC High Performance 2023 International Workshops, Revised Selected Papers
EditorsAmanda Bienz, Michèle Weiland, Marc Baboulin, Carola Kruse
PublisherSpringer Science and Business Media Deutschland GmbH
Pages378-390
Number of pages13
ISBN (Print)9783031408427
DOIs
StatePublished - 2023
Externally publishedYes
Event38th International Conference on High Performance Computing, ISC High Performance 2023 - Hamburg, Germany
Duration: 21 May 202325 May 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13999 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference38th International Conference on High Performance Computing, ISC High Performance 2023
Country/TerritoryGermany
CityHamburg
Period21/05/2325/05/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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