Abstract
The OpenMP language continues to evolve with every new specification release, as does the need to validate and verify the new features that have been implemented by the different vendors. With the release of OpenMP 5.0 and OpenMP 5.1, new target offload and host-based features have been introduced to the programming model. While OpenMP continues to grow in maturity, there is an observable growth in the number of compiler and hardware vendors that support OpenMP. In this manuscript, the main focus is on evaluating the conformity and OpenMP implementation progress of various compiler vendors such as Cray, IBM, GNU, Clang/LLVM, NVIDIA, and Intel. More specifically, the 4.5, 5.0, and 5.1 versions of the OpenMP specification are analyzed. For our experimental setup, the Crusher and Summit computing systems hosted by Oak Ridge National Lab's Computing Facilities are utilized. The effort of vendor agnostic analysis of these implementations is especially valuable for application developers who are using new OpenMP features to accelerate their scientific codes. Insights are presented into the current implementation status of various vendors, the progression of specific compiler's support for OpenMP overtime, the subset of OpenMP 4.5, 5.0, and 5.1 that is supported by all compilers, and examples of how our test suite has influenced discussion regarding the correct interpretation of the OpenMP specification. By evaluating OpenMP conformity of pre-Exascale computing systems, the aim is to detail progress and status of AMD + Cray ecosystem before the system and their OpenMP implementation is used for mission critical applications when the first Exascale Computer Frontier is made available to applications.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of P3HPC 2022 |
| Subtitle of host publication | 2022 International Workshop on Performance, Portability and Productivity in HPC, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 123-135 |
| Number of pages | 13 |
| ISBN (Electronic) | 9781665460217 |
| DOIs | |
| State | Published - 2022 |
| Event | 5th IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC, P3HPC 2022 - Dallas, United States Duration: Nov 13 2022 → Nov 18 2022 |
Publication series
| Name | Proceedings of P3HPC 2022: 2022 International Workshop on Performance, Portability and Productivity in HPC, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis |
|---|
Conference
| Conference | 5th IEEE/ACM International Workshop on Performance, Portability and Productivity in HPC, P3HPC 2022 |
|---|---|
| Country/Territory | United States |
| City | Dallas |
| Period | 11/13/22 → 11/18/22 |
Funding
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid up, irrevocable, world-wide license to publish or reproduce the published form of the manuscript, or allow others to do so, for U.S. Government purposes. The DOE will provide public access to these results in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). VIII. ACKNOWLEDGMENT This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Officeof Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaboratvi e effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. The research is also supported by the NSF under grant no. 1814609.
Keywords
- GPU
- LLVM
- Offloading
- OpenMP