Molecular biologists who conduct large-scale genetic sequencing projects are producing an ever-increasing amount
of sequence data. GenBank, the primary repository for DNA sequence data, is doubling in size every 1.3 years.
Keeping pace with the analysis of these data is a difficult task. One of the most successful technique, for analyzing
genetic data is sequence similarity analysis-the comparison of unknown sequences against known sequences kept
in databases. As biologists gather more sequence data, sequence similarity algorithms are more and more useful, but
take longer and longer to run. BLAST is one of the most popular sequence similarity algorithms in me today, but its
running time is approximately proportional to the size of the database. Sequence similarity analysis using BLAST is
becoming a bottleneck in genetic sequence analysis. This paper analyzes the performance of BLAST on SMPs, to
improve our theoretical and practical understanding of the scalability of the algorithm. Since the database sizes are
growing faster than the improvements in processor speed we expect from Moore's law, multiprocessor architectures
appear to be the only way to meet the need for performance.
Chi, Ed Huai-hsin; Shoop, Elizabeth; Carlis, John; Retzel, Ernest; Riedl, John.
Efficiency of Shared-Memory Multiprocessors for a Genetic Sequence Similarity Search Algorithm.
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