Abstract
Multiple pattern matching algorithms are used to locate the occurrences of patterns from a finite pattern set in a large input string. Aho-Corasick, Set Horspool, Set Backward Oracle Matching, Wu-Manber and SOG, five of the most well known algorithms for multiple matching require an increased computing power, particularly in cases where large-size datasets must be processed, as is common in computational biology applications. Over the past years, Graphics Processing Units (GPUs) have evolved to powerful parallel processors outperforming CPUs in scientific applications. This paper evaluates the speedup of the basic parallel strategy and the different optimization strategies for parallelization of Aho-Corasick, Set Horspool, Set Backward Oracle Matching, Wu-Manber and SOG algorithms on a GPU.
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