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

doi:10.1186/s12859-018-2276-1

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

Department of Computer Science

Research output: Contribution to journal › Journal article › peer-review

10 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 language	English
Article number	282
Journal	BMC Bioinformatics
Volume	19
DOIs	https://doi.org/10.1186/s12859-018-2276-1
Publication status	Published - 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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10.1186/s12859-018-2276-1

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@article{6610186985cd4c579c9114d63a5cb7f4,

title = "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.",

keywords = "MEME, MIC, Motif discovery, Offload mode",

author = "Shaoliang Peng and Minxia Cheng and Kaiwen Huang and Cui, {Ying Bo} and Zhiqiang Zhang and Runxin Guo and Xiaoyu Zhang and Shunyun Yang and Xiangke Liao and Yutong Lu and Quan Zou and Benyun Shi",

note = "Funding Information: This work was supported by National Key R&D Program of China 2017YFB0202602, 2017YFC1311003, 2016YFC1302500, 2016YFB0200400, 2017YFB0202104; NSFC Grants 61772543, U1435222, 61625202, 61272056, 61771331; and Guangdong Provincial Department of Science and Technology under grant No. 2016B090918122.",

year = "2018",

month = aug,

day = "13",

doi = "10.1186/s12859-018-2276-1",

language = "English",

volume = "19",

journal = "BMC Bioinformatics",

issn = "1471-2105",

publisher = "BioMed Central Ltd.",

}

TY - JOUR

T1 - Efficient computation of motif discovery on Intel Many Integrated Core (MIC) Architecture

AU - Peng, Shaoliang

AU - Cheng, Minxia

AU - Huang, Kaiwen

AU - Cui, Ying Bo

AU - Zhang, Zhiqiang

AU - Guo, Runxin

AU - Zhang, Xiaoyu

AU - Yang, Shunyun

AU - Liao, Xiangke

AU - Lu, Yutong

AU - Zou, Quan

AU - Shi, Benyun

N1 - Funding Information: This work was supported by National Key R&D Program of China 2017YFB0202602, 2017YFC1311003, 2016YFC1302500, 2016YFB0200400, 2017YFB0202104; NSFC Grants 61772543, U1435222, 61625202, 61272056, 61771331; and Guangdong Provincial Department of Science and Technology under grant No. 2016B090918122.

PY - 2018/8/13

Y1 - 2018/8/13

N2 - 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.

AB - 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.

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KW - Motif discovery

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AN - SCOPUS:85051585873

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JO - BMC Bioinformatics

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M1 - 282

ER -

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

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