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Mimir+: An Optimized Framework of MapReduce on Heterogeneous High-Performance Computing System

  • Nan Hu
  • Zhiguang Chen
  • Yunfei Du
  • Yutong Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11276)

Abstract

In this paper, we present an optimized data processing framework: Mimir+. Mimir+ is an implementation of MapReduce over MPI. In order to take full advantage of heterogeneous computing system, we propose the concept of Pre-acceleration to reconstruct a heterogeneous workflow and implement the interfaces of GPU so that Mimir+ can facilitate data processing through reasonable tasks and data scheduling between CPU and GPU. We evaluate Mimir+ via two benchmarks (i.e. the WordCount and large-scale matrix multiplication) on the Tianhe-2 supercomputing system. Experimental results demonstrate that Mimir+ achieves excellent acceleration effect compared with original Mimir.

Keywords

High-performance computing MapReduce Heterogeneous 

Notes

Acknowledgments

We are grateful to the anonymous reviewers for their valuable suggestions that will be used to improve this paper. This work is partially supported by National Key R&D Program of China 2017YFB0202201, National Natural Science Foundation of China under U1611261, 61433019, U1435217, 61872392 and the Program for Guangdong Introducing Innovative and Entrepreneurial Teams under Grant NO. 2016ZT06D211.

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Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina

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