Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Computing on GPU with CUDA and ELLPACK-R Sparse Format

Markov clustering (MCL) is becoming a key algorithm within bioinformatics for determining clusters in networks. However, with increasing vast amount of data on biological networks, performance and scalability issues are becoming a critical limiting factor in applications. Meanwhile, GPU computing, w...

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Vydáno v:IEEE/ACM transactions on computational biology and bioinformatics Ročník 9; číslo 3; s. 679 - 692
Hlavní autoři: Bustamam, A., Burrage, K., Hamilton, N. A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2012
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Bioinformatics
Cluster Analysis
Clustering
Computation
Computational Biology - methods
Computer Graphics
Computer Simulation
CUDA
ELLPACK-R sparse format
Format
GPU computing
Graphics processing unit
graphs and networks
Instruction sets
Markov Chains
Markov clustering
Markov processes
Multicore processing
Networks
Oligonucleotide Array Sequence Analysis
Parallel processing
parallelism and concurrency
performance evaluation
PPI networks
Proteins
scalable parallel programming
Studies
ISSN:1545-5963, 1557-9964, 1557-9964
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Shrnutí:Markov clustering (MCL) is becoming a key algorithm within bioinformatics for determining clusters in networks. However, with increasing vast amount of data on biological networks, performance and scalability issues are becoming a critical limiting factor in applications. Meanwhile, GPU computing, which uses CUDA tool for implementing a massively parallel computing environment in the GPU card, is becoming a very powerful, efficient, and low-cost option to achieve substantial performance gains over CPU approaches. The use of on-chip memory on the GPU is efficiently lowering the latency time, thus, circumventing a major issue in other parallel computing environments, such as MPI. We introduce a very fast Markov clustering algorithm using CUDA (CUDA-MCL) to perform parallel sparse matrix-matrix computations and parallel sparse Markov matrix normalizations, which are at the heart of MCL. We utilized ELLPACK-R sparse format to allow the effective and fine-grain massively parallel processing to cope with the sparse nature of interaction networks data sets in bioinformatics applications. As the results show, CUDA-MCL is significantly faster than the original MCL running on CPU. Thus, large-scale parallel computation on off-the-shelf desktop-machines, that were previously only possible on supercomputing architectures, can significantly change the way bioinformaticians and biologists deal with their data.
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ISSN:1545-5963
1557-9964
1557-9964
DOI:10.1109/TCBB.2011.68