Abstract
As parallel computers approach exascale, power efficiency in high-performance computing (HPC) systems is of increasing concern. Exploiting both the hardware features and algorithms is an effective solution to achieve power efficiency, and to address the energy constraints in modern and future HPC systems. In this work, we present a novel design and implementation of an energy-efficient solution for dense linear systems of equations, which are at the heart of large-scale HPC applications. The proposed energy-efficient linear system solvers are based on two main components: (1) iterative refinement techniques, and (2) reduced-precision computing features in modern accelerators and coprocessors. While most of the energy efficiency approaches aim to reduce the consumption with a minimal performance penalty, our method improves both the performance and the energy efficiency. Compared to highly-optimized linear system solvers, our kernels deliver the same accuracy solution up to 2× faster and reduce the energy consumption up to half on Intel Knights Landing (KNL) architectures. By efficiently using the Tensor Cores available in the NVIDIA V100 PCIe GPUs, the speedups can be up to 4×, with more than 80% reduction in the energy consumption.
Original language | English |
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Title of host publication | Computational Science – ICCS 2018 - 18th International Conference, Proceedings |
Editors | Haohuan Fu, Valeria V. Krzhizhanovskaya, Michael Harold Lees, Peter M. Sloot, Jack Dongarra, Yong Shi, Yingjie Tian |
Publisher | Springer Verlag |
Pages | 586-600 |
Number of pages | 15 |
ISBN (Print) | 9783319936970 |
DOIs | |
State | Published - 2018 |
Event | 18th International Conference on Computational Science, ICCS 2018 - Wuxi, China Duration: Jun 11 2018 → Jun 13 2018 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10860 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 18th International Conference on Computational Science, ICCS 2018 |
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Country/Territory | China |
City | Wuxi |
Period | 06/11/18 → 06/13/18 |
Funding
Acknowledgments. This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. The work was also partially supported by NVIDIA and NSF grant No. OAC-1740250.
Keywords
- FP16
- HPC
- Mixed-precision
- Solvers
- Tensor cores