Efficient computation of motif discovery on Intel Many Integrated Core (MIC) Architecture

Shaoliang Peng*, Minxia Cheng, Kaiwen Huang, Ying Bo Cui, Zhiqiang Zhang, Runxin Guo, Xiaoyu Zhang, Shunyun Yang, Xiangke Liao, Yutong Lu, Quan Zou, Benyun SHI

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Background: Novel sequence motifs detection is becoming increasingly essential in computational biology. However, the high computational cost greatly constrains the efficiency of most motif discovery algorithms. Results: In this paper, we accelerate MEME algorithm targeted on Intel Many Integrated Core (MIC) Architecture and present a parallel implementation of MEME called MIC-MEME base on hybrid CPU/MIC computing framework. Our method focuses on parallelizing the starting point searching method and improving iteration updating strategy of the algorithm. MIC-MEME has achieved significant speedups of 26.6 for ZOOPS model and 30.2 for OOPS model on average for the overall runtime when benchmarked on the experimental platform with two Xeon Phi 3120 coprocessors. Conclusions: Furthermore, MIC-MEME has been compared with state-of-arts methods and it shows good scalability with respect to dataset size and the number of MICs. Source code: https://github.com/hkwkevin28/MIC-MEME.

Original languageEnglish
Article number282
JournalBMC Bioinformatics
Volume19
DOIs
Publication statusPublished - 13 Aug 2018

Scopus Subject Areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

User-Defined Keywords

  • MEME
  • MIC
  • Motif discovery
  • Offload mode

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