Abstract
As heterogeneous architectures are becoming mainstream for HPC systems, application programmers are looking for programming model implementations that offer both performance and portability across platforms. Two directive-based programming models for accelerator programming that aim at doing this are OpenMP 4/4.5 and OpenACC. Many users want to know the difference between these two programming models, the state of their implementations, how to use them, and evaluate how suitable they are for their applications. The Standard Performance Evaluation Corporation (SPEC) ACCEL benchmarks, developed by the SPEC High Performance Group (HPG), recently released SPEC ACCEL 1.2 benchmark suite to help the evaluation of OpenCL, OpenMP 4.5 and OpenACC on different platforms. In this paper we present our preliminary results that evaluates OpenMP 4.5 and OpenACC on a variety of accelerator-based systems: POWER9 with NVIDIA V100 GPUs (Summit), Intel Xeon Phi 7230 (Percival), and AMD Bulldozer Opteron with NVIDIA K20x (Titan). Comparing these benchmarks on different systems gives us insight into the support for OpenMP and OpenACC and their execution times provide insights about their quality of implementations provided by different vendors. We also compare best of OpenMP and OpenACC to see if a particular programming model favors a particular type of benchmark kernel.
| Original language | English |
|---|---|
| Title of host publication | High Performance Computing - ISC High Performance 2018 International Workshops, Revised Selected Papers |
| Editors | Michèle Weiland, Rio Yokota, John Shalf, Sadaf Alam |
| Publisher | Springer Verlag |
| Pages | 711-723 |
| Number of pages | 13 |
| ISBN (Print) | 9783030024642 |
| DOIs | |
| State | Published - 2018 |
| Event | International Conference on High Performance Computing, ISC High Performance 2018 - Frankfurt, Germany Duration: Jun 28 2018 → Jun 28 2018 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11203 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | International Conference on High Performance Computing, ISC High Performance 2018 |
|---|---|
| Country/Territory | Germany |
| City | Frankfurt |
| Period | 06/28/18 → 06/28/18 |
Funding
This manuscript has been co-authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy. gov/downloads/doe-public-access-plan). Acknowledgments. 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.