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A data parallel strategy for aligning multiple biological sequences on multi-core computers
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DOI：10.1016/j.compbiomed.2012.12.009.png)
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Abstract
En 中文
In this paper, we address the large-scale biological sequence alignment problem, which has an increasing demand in computational biology. We employ data parallelism paradigm that is suitable for handling large-scale processing on multi-core computers to achieve a high degree of parallelism. Using the data parallelism paradigm, we propose a general strategy which can be used to speed up any multiple sequence alignment method. We applied five different clustering algorithms in our strategy and implemented rigorous tests on an 8-core computer using four traditional benchmarks and artificially generated sequences. The results show that our multi-core-based implementations can achieve up to 151-fold improvements in execution time while losing 2.19% accuracy on average. The source code of the proposed strategy, together with the test sets used in our analysis, is available on request. (c) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Data parallelism
Parallel algorithm
Multi-core
Multiple sequence alignment
Biological sequences
Clustering
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