A heterogeneous parallel implementation of the Markov clustering algorithm for large-scale biological networks on distributed CPU–GPU clusters

Biological interaction databases accommodate information about interacted proteins or genes. Clustering on the networks formed by the interaction information for finding regions highly connected could reveal the functional affinities or structural similarities between protein or gene entities. With...

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Published in:	The Journal of supercomputing Vol. 78; no. 7; pp. 9017 - 9037
Main Authors:	Fu, You, Zhou, Wei
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.05.2022 Springer Nature B.V
Subjects:	Algorithms Clustering Compilers Computer Science Data transmission Interpreters Processor Architectures Programming Languages Proteins Parallel computing Biological interaction network Compute Unified Device Architecture Heterogenous computing Cluster computing
ISSN:	0920-8542, 1573-0484
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Abstract	Biological interaction databases accommodate information about interacted proteins or genes. Clustering on the networks formed by the interaction information for finding regions highly connected could reveal the functional affinities or structural similarities between protein or gene entities. With the ever-increasing amounts of information in these databases, the runtime of a clustering task is more and more unaffordable. In this paper, we propose a heterogeneous parallel algorithm focusing on accelerating clustering tasks using distributed CPU–GPU clusters. Our parallel implementation is based on the original serial algorithm of the Markov clustering (MCL). In our parallel implementation, we utilize both the CPUs and GPUs to exploit the power of heterogeneous platforms. With the BioGRID biological interaction database, we have tested the proposed algorithm on a computer cluster equipped with NVIDIA Tesla P100 GPU accelerators. The result shows that, the algorithm is efficient in GPU memory usage and inter-node data transmission, and it can complete the clustering task in 3.2 minutes with the best speedup of 70.02 times compared to the serial counterpart.We believe our work can provide key insights for realizing fast MCL analyses on large-scale biological data, with distributed CPU–GPU computer clusters.
AbstractList	Biological interaction databases accommodate information about interacted proteins or genes. Clustering on the networks formed by the interaction information for finding regions highly connected could reveal the functional affinities or structural similarities between protein or gene entities. With the ever-increasing amounts of information in these databases, the runtime of a clustering task is more and more unaffordable. In this paper, we propose a heterogeneous parallel algorithm focusing on accelerating clustering tasks using distributed CPU–GPU clusters. Our parallel implementation is based on the original serial algorithm of the Markov clustering (MCL). In our parallel implementation, we utilize both the CPUs and GPUs to exploit the power of heterogeneous platforms. With the BioGRID biological interaction database, we have tested the proposed algorithm on a computer cluster equipped with NVIDIA Tesla P100 GPU accelerators. The result shows that, the algorithm is efficient in GPU memory usage and inter-node data transmission, and it can complete the clustering task in 3.2 minutes with the best speedup of 70.02 times compared to the serial counterpart.We believe our work can provide key insights for realizing fast MCL analyses on large-scale biological data, with distributed CPU–GPU computer clusters.
Author	Zhou, Wei Fu, You
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Ph.D. thesis – reference: Van RavenzwaaijDCasseyPBrownSDA simple introduction to Markov Chain Monte-Carlo samplingPsychonomic Bull Review201825114315410.3758/s13423-016-1015-8 – reference: HuangLTWeiKCWuCCChenCYWangJAA lightweight BLASTP and its implementation on CUDA GPUsJ Supercomput202177132234210.1007/s11227-020-03267-1 – reference: RoseOughtredThe BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactionsProtein Sci20213018720010.1002/pro.4096 – reference: StarkCBreitkreutzBJRegulyTBoucherLBreitkreutzATyersMBiogrid: a general repository for interaction datasetsNucleic Acids Res200634suppl 1D535D53910.1093/nar/gkj109 – reference: Satuluri V, Parthasarathy S (2009) Scalable Graph Clustering Using Stochastic Flows: applications to Community Discovery. 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J Supercomput pp 1–18 – reference: VlasblomJWodakSJMarkov clustering versus affinity propagation for the partitioning of protein interaction graphsBMC Bioinform20091019910.1186/1471-2105-10-99 – reference: DafirZLamariYSlaouiSCA survey on parallel clustering algorithms for big dataArtif Intell Rev20215442411244310.1007/s10462-020-09918-2 – reference: Lim Y, Yu I, Seo D et al (2019) PS-MCL: parallel shotgun coarsened markov clustering of protein interaction networks. BMC Bioinform 20(Suppl 13) – reference: Butenhof DR (1997) Programming with POSIX threads. Addison-Wesley Professional – reference: ShukurHZeebareeSRAhmedAJZebariRRAhmedOTahirBSASadeeqMAA state of art survey for concurrent computation and clustering of parallel computing for distributed systemsJ Appl Sci Technol Trends20201414815410.38094/jastt1466 – reference: StollDTemplinMBachmannJJoosTProtein microarrays: applications and future challengesCurrent Opin Drug Discov Develop200582239252 – reference: (2019) The top500 systems. Retrieved Jan, 2020. https://www.top500.org/lists/2019/11/ – reference: LiuYSchmidtBLightspmv: faster cuda-compatible sparse matrix-vector multiplication using compressed sparse rowsJ Signal Process Syst2018901698610.1007/s11265-016-1216-4 – reference: HeLLuLWangQAn optimal parallel implementation of Markov Clustering based on the coordination of CPU and GPUJ Intell Fuzzy Syst20173253609361710.3233/JIFS-169296 – reference: Sharan R, Ulitsky I, Shamir R (2007) Network-based prediction of protein function. 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