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Solving a trillion unknowns per second with HPGMG on Sunway TaihuLight

  • Wenjing Ma
  • Yulong Ao
  • Chao YangEmail author
  • Samuel Williams
Article
  • 63 Downloads

Abstract

Benchmarks for supercomputers are important tools, not only for evaluating and ranking modern supercomputers, but also for providing hints for future architecture design. As a new benchmark, HPGMG (high performance geometric multigrid) solves a linear equation set with a full geometric multi-grid algorithm. It involves computation on different scales, data movement with various volumes, global communication and neighbor communication with both large and small messages, etc., and is more correlated to real world applications than traditional benchmarks such as LINPACK. Therefore, it is desirable to examine how well HPGMG can perform on leadership supercomputers such as Sunway Taihulight. Sunway Taihulight, the No. 1 supercomputer in the Top 500 list from June 2016 to June 2018, which uses a specially designed many-core architecture SW26010, is of great interest to the community of high performance computing. With careful analysis and code design, we came up with an efficient implementation of HPGMG on SW26010 processors. We not only employed traditional optimization techniques such as 2.5D partitioning, double buffering, and collective data load, but also introduced a micro-benchmark to help with the choice of optimization direction and parameter tuning. Another contribution is that we proposed a new procedure for the major operations, by granulating and reordering the smooth function and the ghost exchange operation, leading to reduced memory copy and accelerated communication process. Our optimized implementation of HPGMG on Sunway TaihuLight achieved a ground-breaking performance of \(1.036\times 10^{12}\) Degrees of Freedom per second at the finest level, which is No. 1 on the HPGMG list of Nov 2017.

Keywords

HPGMG Sunway TaihuLight Performance benchmark and optimization Many-core computing 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for helping improve the quality of the paper. This work was supported in part by National Key R&D Plan of China (Grant# 2016YFB0200603) and Beijing Natural Science Foundation (Grant# JQ18001). Dr. Williams was supported by the Advanced Scientific Computing Research Program in the U.S. Department of Energy, Office of Science, under Award Number DE-AC02-05CH11231.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Software & State Key Lab of Computer Science, Chinese Academy of SciencesBeijingChina
  2. 2.CAPT and CCSE, School of Mathematical Sciences & Center for Data SciencePeking UniversityBeijingChina
  3. 3.Peng Cheng LaboratoryShenzhenChina
  4. 4.Computational Research DivisionLawrence Berkeley National LaboratoryBerkeleyUSA

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