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Multiple matching algorithms are used to locate the occurrences of patternsfrom a finite pattern set in a large input string. Aho-Corasick and Wu-Manber,two of the most well known algorithms for multiple matching require anincreased computing power, particularly in cases where large-size datasets mustbe processed, as is common in computational biology applications. Over the pastyears, Graphics Processing Units (GPUs) have evolved to powerful parallelprocessors outperforming Central Processing Units (CPUs) in scientificcalculations. Moreover, multiple GPUs can be used in parallel, forming hybridcomputer cluster configurations to achieve an even higher processingthroughput. This paper evaluates the speedup of the parallel implementation ofthe Aho-Corasick and Wu-Manber algorithms on a hybrid GPU cluster, when used toprocess a snapshot of the Expressed Sequence Tags of the human genome and fordifferent problem parameters.

A Hybrid Parallel Implementation of the Aho-Corasick and Wu-Manber Algorithms Using NVIDIA CUDA and MPI Evaluated on a Biological Sequence Database