Mapping of BLASTP Algorithm onto GPU Clusters

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...

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Bibliographic Details
Main Authors: Weiguo Liu, Schmidt, B., Yongchao Liu, Voss, G., Mueller-Wittig, W.
Format: Conference Proceeding
Language:English
Subjects:
Arrays
BLAST
Clustering algorithms
CUDA
GPU Cluster
Graphics processing unit
Kernel
Message systems
MPI
OpenMP
Runtime
Online Access:Request full text
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Summary: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.
ISSN:1521-9097
2690-5965
DOI:10.1109/ICPADS.2011.79