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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Veröffentlicht in:The Journal of supercomputing Jg. 78; H. 7; S. 9017 - 9037
Hauptverfasser: Fu, You, Zhou, Wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.05.2022
Springer Nature B.V
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-021-04204-6