Parallelism of adaptive Hungary greedy algorithm for biomolecular networks alignment

Journal of Computer Applications ›› 2013, Vol. 33 ›› Issue (12): 3321-3325.

• 2013 National Open Distributed and Parallel Computing Confernece of China (DPCS2013) • Previous Articles Next Articles

Parallelism of adaptive Hungary greedy algorithm for biomolecular networks alignment

MA Jin¹,XIE Jiang¹,²,DAI Dongbo¹,TAN Jun¹,ZHANG Wu¹,²

1. School of Computer Engineering and Science, Shanghai University, Shanghai 200044, China
2. High Performance Computing Center, Shanghai University, Shanghai 200044, China

Received:2013-08-05 Online:2013-12-31 Published:2013-12-01
Contact: ZHANG Wu
Supported by:
; Key Project of Science and Technology Commission of Shanghai Municipality

用于生物分子网络比对的自适应匈牙利贪心混合算法的并行化

马进¹,谢江¹,²,戴东波¹,谭军¹,张武¹,²

1. 上海大学计算机工程与科学学院，上海 200044；
2. 上海大学高性能计算中心，上海 200044

通讯作者: 张武
作者简介:马进(1991-),男,安徽亳州人,硕士研究生,CCF会员,主要研究方向:生物信息、高性能计算;
谢江(1971-),女,湖北恩施人,副教授,博士,CCF会员,主要研究方向:生物信息、高性能计算;
戴东波(1977-),男,湖南娄底人,讲师,博士,CCF会员,主要研究方向:生物信息、算法设计和分析;
谭军(1987-),男,湖南株洲人,硕士,主要研究方向:高性能计算;
张武(1957-),男,江西武宁人,教授,博士,CCF会员,主要研究方向:高性能计算、生物信息、计算流体学。
基金资助:
教育部博士点基金资助项目;上海市科委重点项目资助

Abstract

Abstract: Biomolecular networks alignment is an important field, and it is an effective way to study biomolecular phenomenon. Adaptive Hungary Greedy Algorithm (AHGA) is one of the valid biomolecular networks alignment algorithms. Commonly, biomolecular networks have large scale and biological background, so the data of biomolecular networks are special. In order to get the alignment results of biomolecular networks in acceptable time, considering the biological significance when aligning them, two methods including MPI (Message Passing Interface) and CUDA (Compute Unified Device Architecture) were used to parallelize the adaptive hybrid algorithm. The methods were analyzed and compared to find the suitable one for biomolecular networks alignment.

Key words: biomolecular networks alignment, adaptive, hybrid algorithm, parallelism

摘要： 生物分子网络比对是生物信息学中一个重要领域，是研究生物现象和生命机理的有效手段，而自适应匈牙利贪心混合算法(AHGA)是其中一个有效的生物分子网络比对算法。但是生物分子网络数据的规模都比较大，而且由于其拥有生物背景，生物分子网络数据具有一些特殊性。为了能够在可以接受的时间范围内获得大规模生物分子网络的比对结果，使用MPI和统一计算架构(CUDA)对自适应混合算法进行了并行化，在比对中充分考虑生物分子网络的生物学意义，对两种方式进行了对比分析，以寻找更合适生物分子网络的比对方法。

关键词: 生物分子网络比对, 自适应, 混合算法, 并行化

CLC Number:

TP391
TP311

MA Jin XIE Jiang DAI Dongbo TAN Jun ZHANG Wu. Parallelism of adaptive Hungary greedy algorithm for biomolecular networks alignment[J]. Journal of Computer Applications, 2013, 33(12): 3321-3325.

马进谢江戴东波谭军张武. 用于生物分子网络比对的自适应匈牙利贪心混合算法的并行化[J]. 计算机应用, 2013, 33(12): 3321-3325.

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Parallelism of adaptive Hungary greedy algorithm for biomolecular networks alignment

用于生物分子网络比对的自适应匈牙利贪心混合算法的并行化

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