Abstract
Motivated by maturing programming models and portability for heterogeneous computing, we describe the challenges posed by hardware architectures and programming models when migrating an optimized implementation of nonuniform reduction from CUDA to HIP and SYCL. We explain the migration experience, evaluate the performance of the reduction on GPU -based computing platforms, and provide feedback on improving portability for the development of the SYCL programming model.
Original language | English |
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Title of host publication | Proceedings of DRBSD-8 2022 |
Subtitle of host publication | 8th International Workshop on Data Analysis and Reduction for Big Scientific Data, 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 | 37-43 |
Number of pages | 7 |
ISBN (Electronic) | 9781665463379 |
DOIs | |
State | Published - 2022 |
Event | 8th IEEE/ACM International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-8 2022 - Dallas, United States Duration: Nov 13 2022 → Nov 18 2022 |
Publication series
Name | Proceedings of DRBSD-8 2022: 8th International Workshop on Data Analysis and Reduction for Big Scientific Data, Held in conjunction with SC 2022: The International Conference for High Performance Computing, Networking, Storage and Analysis |
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Conference
Conference | 8th IEEE/ACM International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-8 2022 |
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Country/Territory | United States |
City | Dallas |
Period | 11/13/22 → 11/18/22 |
Funding
ACKNOWLEDGMENT We appreciate the reviewers’ comments and suggestions. This research used resources of the Experimental Computing Lab at Oak Ridge National Laboratory. This research was supported by the US Department of Energy Advanced Scientific Computing Research program under Contract No. DE-AC05-00OR22725. Notice: 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). We appreciate the reviewers comments and suggestions. This research used resources of the Experimental Computing Lab at Oak Ridge National Laboratory. This research was supported by the US Department of Energy Advanced Scientific Computing Research program under Contract No. DE-AC05-00OR22725.
Keywords
- Nonuniform reduction
- heterogeneous computing
- programming model