Chi, Ed Huai-hsinShoop, ElizabethCarlis, JohnRetzel, ErnestRiedl, John2020-09-022020-09-021997https://hdl.handle.net/11299/215348Molecular 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.en-USmolecular biologyalignmentbiological sequencessimilarity searchparallel processingperformance experimentshared-memory multiprocessorsBLASTReport