生物效应大数据评估聚类算法的并行优化

doi:10.11959/j.issn.2096-0271.2018027

Big Data Research ›› 2018, Vol. 4 ›› Issue (3): 24-36.doi: 10.11959/j.issn.2096-0271.2018027

• TOPIC：BIOMEDICAL BIG DATA • Previous Articles Next Articles

Parallel optimization for clustering algorithm of large-scale biological effect evaluation

Shaoliang PENG^1,²,Shunyun YANG²,Zhe SUN¹,Minxia CHENG¹,Yingbo CUI²,Xiaowei WANG²,Fei LI³,Xiaochen BO³,Xiangke LIAO²

¹ College of Computer Science and Electronic Engineering ＆National Supercomputer Centre in Changsha,Hunan University,Changsha 410082,China
² Department of Computer Science,National University of Defense Technology,Changsha 410073,China
³ Academy of Military Medical Sciences,Beijing 100850,China

Online:2018-05-15 Published:2018-05-30
Supported by:
The National Key Research and Development Program of China(2017YFB0202603);The National Key Research and Development Program of China(2017YFC1311003);The National Key Research and Development Program of China(2016YFC1302500);The National Key Research and Development Program of China(2016YFB0200400);The National Key Research and Development Program of China(2017YFB0202104);The National Natural Science Foundation of China(61772543);The National Natural Science Foundation of China(U1435222);The National Natural Science Foundation of China(61625202);The National Natural Science Foundation of China(61272056);Guangdong Provincial Department of Science and Technology(2016B090918122);The Funds of State Key Laboratory of Chemo/Biosensing and Chemometrics

Abstract

Abstract:

The biological assessment,including matching algorithm,is realized by measuring and analyzing the human cells’ transcription reaction after stimulated by biological agents,to quickly determine the relevant detection markers and treatment targets.Similarly,the big data strategy was used to estimate the sudden biological effect model.MPI,OpenMP two-level parallel acceleration was considered,transplantation and optimization of the GSEA alignment algorithm and clustering algorithm were used.The potential scalability and the ability of dealing with massive data by testing different scales of data and parallelisms were improved.

Key words: GSEA, clustering, MPI, OpenMP

CLC Number:

TP391

Shaoliang PENG, Shunyun YANG, Zhe SUN, Minxia CHENG, Yingbo CUI, Xiaowei WANG, Fei LI, Xiaochen BO, Xiangke LIAO. Parallel optimization for clustering algorithm of large-scale biological effect evaluation[J]. Big Data Research, 2018, 4(3): 24-36.

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References 15

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进程数/个	线程数/个	总核数/个	载入时间/s			计算时间/s	写时间/s
进程数/个	线程数/个	总核数/个	无通信	点对点	全局	计算时间/s	写时间/s
5	3	15	1.280	2.685	1.691	675.496	20.990
	6	30	1.878	3.099	1.678	351.376	21.077
	9	45	2.094	4.035	1.361	247.510	20.861
	12	60	2.505	4.892	1.421	189.706	20.627
10	3	30	1.810	4.918	1.254	344.862	12.012
	6	60	2.654	5.555	1.391	174.435	10.507
	9	90	2.534	5.632	1.432	123.210	10.24
	12	120	2.395	5.352	1.253	94.438	10.477
20	3	60	1.045	9.124	1.143	173.747	5.327
	6	120	1.349	9.813	0.912	91.402	5.280
	9	180	1.784	9.212	0.900	64.873	5.371
	12	240	2.264	10.213	0.992	49.286	6.025

Parallel optimization for clustering algorithm of large-scale biological effect evaluation

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