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

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 .

journal_name

BMC Bioinformatics

journal_title

BMC bioinformatics

authors

Peng S,Cheng M,Huang K,Cui Y,Zhang Z,Guo R,Zhang X,Yang S,Liao X,Lu Y,Zou Q,Shi B

doi

10.1186/s12859-018-2276-1

subject

Has Abstract

pub_date

2018-08-13 00:00:00

pages

282

issue

Suppl 9

issn

1471-2105

pii

10.1186/s12859-018-2276-1

journal_volume

19

pub_type

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