Comparing High Performance Computing Accelerator Programming Models

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

3 Scopus citations

Abstract

Accelerator devices are becoming a norm in High Performance Computing (HPC). With more systems opting for heterogeneous architectures, portable programming models like OpenMP and OpenACC are becoming increasingly important. The SPEC ACCEL 1.2 benchmark suite consists of comparable benchmarks in OpenCL, OpenMP 4.5, and OpenACC 2.5 that can be used to evaluate the performance and support for programming models and frameworks on heterogeneous platforms. In this paper we go beneath the normative metric of performance times and look at the individual kernels to study the usage, strengths, and weaknesses of the two prevalent portable heterogeneous programming models, OpenMP and OpenACC. From our analysis we identify that benchmarks like MRI-Q, SP and BT have better performance using OpenACC, while benchmarks like MiniGhost, LBM and LBDC do consistently better with the OpenMP programming model across super-computers like Titan, and Summit. We deep dive into the kernels of select four benchmarks to answer questions like: Where does the benchmark spend most of its cycles? What is the parallelization strategy used? Why is one programming model more performant than the other? By identifying the similarities and differences we want to contrast between the benchmark implementation strategies in the SPEC ACCEL 1.2 benchmarks and provide more insights into the OpenMP and OpenACC programming models.

Original languageEnglish
Title of host publicationHigh Performance Computing - ISC High Performance 2019 International Workshops, Revised Selected Papers
EditorsMichèle Weiland, Guido Juckeland, Sadaf Alam, Heike Jagode
PublisherSpringer
Pages155-168
Number of pages14
ISBN (Print)9783030343552
DOIs
StatePublished - 2019
Event34th International Conference on High Performance Computing, ISC High Performance 2019 - Frankfurt, Germany
Duration: Jun 16 2019Jun 20 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11887 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference34th International Conference on High Performance Computing, ISC High Performance 2019
Country/TerritoryGermany
CityFrankfurt
Period06/16/1906/20/19

Funding

This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, under contract number DE-AC05-00OR22725. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. We would like to thank Dr. Oscar Hernandez from ORNL for his guidance and support during the writing of this manuscript.

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