An in-depth performance analysis of many-integrated core for communication efficient heterogeneous computing

Jie Zhang, Myoungsoo Jung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many-integrated core (MIC) architecture combines dozens of reduced x86 cores onto a single chip to offer high degrees of parallelism. The parallel user applications executed across many cores that exist in one or more MICs require a series of work related to data sharing and synchronization with the host. In this work, we build a real CPU+MIC heterogeneous cluster and analyze its performance behaviors by examining different communication methods such as message passing method and remote direct memory accesses. Our evaluation results and in-depth studies reveal that (i) aggregating small messages can improve network bandwidth without violating latency restrictions, (ii) while MICs can execute hundreds of hardware cores, the highest network throughput is achieved when only 4 ~ 6 point-to-point connections are established for data communication, (iii) data communication over multiple point-to-point connections between host and MICs introduce severe load unbalancing, which require to be optimized for future heterogeneous computing.

Original languageEnglish
Title of host publicationNetwork and Parallel Computing - 14th IFIP WG 10.3 International Conference, NPC 2017, Proceedings
EditorsXuanhua Shi, Mahmut Kandemir, Hong An, Chao Wang, Hai Jin
PublisherSpringer Verlag
Pages155-159
Number of pages5
ISBN (Print)9783319682099
DOIs
Publication statusPublished - 2017
Event14th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2017 - Hefei, China
Duration: 2017 Oct 202017 Oct 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10578 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2017
CountryChina
CityHefei
Period17/10/2017/10/21

Bibliographical note

Funding Information:
Acknowledgement. This research is mainly supported by NRF 2016R1C1B2015312. This work is also supported in part by IITP-2017-2017-0-01015, NRF-2015M3C4 A7065645, DOE DE-AC02-05CH 11231 and MemRay grant (2015-11-1731). The corresponding author is M. Jung.

Publisher Copyright:
© IFIP International Federation for Information Processing 2017.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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