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Mapping of BLASTP Algorithm onto GPU Clusters

 
: Liu, Weiguo; Schmidt, Bertil; Liu, Yongchao; Voss, Gerrit; Müller-Wittig, Wolfgang K.

:

Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE 17th International Conference on Parallel and Distributed Systems, ICPADS 2011. Vol.1 : Tainan, Taiwan, 7 - 9 December 2011
New York, NY: IEEE, 2011
ISBN: 978-0-7695-4576-9
ISBN: 978-1-4577-1875-5
pp.236-243
International Conference on Parallel and Distributed Systems (ICPADS) <17, 2011, Tainan>
English
Conference Paper
Fraunhofer IGD ()
Compute Unified Device Architecture (CUDA); Graphics Processing Unit (GPU); clustering

Abstract
Searching protein sequence database is a fundamental and often repeated task in computational biology and bioinformatics. However, the high computational cost and long runtime of many database scanning algorithms on sequential architectures heavily restrict their applications for large-scale protein databases, such as GenBank. The continuing exponential growth of sequence databases and the high rate of newly generated queries further deteriorate the situation and establish a strong requirement for time-efficient scalable database searching algorithms. In this paper, we demonstrate how GPU clusters, powered by the Compute Unified Device Architecture (CUDA), OpenMP, and MPI parallel programming models can be used as an efficient computational platform to accelerate the popular BLASTP algorithm. Compared to GPU-BLAST 1.0-2.2.24, our implementation achieves speedups up to 1.6 on a single GPU and up to 6.6 on the 6 GPUs of a Tesla S1060 quad-GPU computing system. The source code is available at: http://sites.google.com/site/liuweiguohome/mpicuda-blastp

: http://publica.fraunhofer.de/documents/N-198669.html