Biosequence Similarity Search on the Mercury System
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Biosequence similarity search is an important application in modern molecular biology. Search algorithms aim to identify sets of sequences whose extensional similarity suggests a common evolutionary origin or function. The most widely used similarity search tool for biosequences is BLAST, a program designed to compare query sequences to a database. Here, we present the design of BLASTN, the version of BLAST that searches DNA sequences, on the Mercury system, an architecture that supports high-volume, high-throughput data movement off a data store and into reconfigurable hardware. An important component of application deployment on the Mercury system is the functional decomposition of the application onto both the reconfigurable hardware and the traditional processor. Both the Mercury BLASTN application design and its performance analysis are described.
KeywordsDNA sequencing comparative annotation biosequence
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- 3.J. Buhler, “Mercury BLAST Dictionaries: Analysis and Performance Measurement,” Technical Report WUCSE-2007-13, Washington University in St. Louis, 2007.Google Scholar
- 6.R. Chamberlain and R. Cytron, “Novel Techniques for Processing Unstructured Data Sets,” in Proc. of IEEE Aerospace Conf., Montana, March 2005.Google Scholar
- 7.R. Chamberlain and B. Shands, “Streaming Data from Disk Store to Application,” in Proc. of 3rd Int’l Workshop on Storage Network Architecture and Parallel I/Os, St. Louis, MO, September 2005, pp. 17–23.Google Scholar
- 8.R. Chamberlain, B. Shands and J. White, “Achieving Real Data Throughput for an FPGA Co-Processor on Commodity Server Platforms,” in Proc. of 1st Workshop on Building Block Engine Architectures for Computers and Networks, Boston, MA, October 2004.Google Scholar
- 9.R.D. Chamberlain, R.K. Cytron, M.A. Franklin and R.S. Indeck, The Mercury System: Exploiting Truly Fast Hardware for Data Search,” in Proc. of Int’l Workshop on Storage Network Architecture and Parallel I/Os, pp. 65–72, September 2003.Google Scholar
- 11.W.J. Dally et al., “Merrimac: Supercomputing with Streams.” in Proc. of Supercomputing Conf., November 2003.Google Scholar
- 13.R.K. Singh et al., “BioSCAN: A Dynamically Reconfigurable Systolic Array for Biosequence Analysis,” in Proc. CERCS 96, 1996.Google Scholar
- 14.M. Franklin, R. Chamberlain, M. Henrichs, B. Shands and J. White, “An Architecture for Fast Processing of Large Unstructured Data Sets,” in Proc. of the 22nd Int’l Conf. on Computer Design, October 2004, pp. 280–287.Google Scholar
- 16.J.D. Hirschberg, R. Hughley and K. Karplus, “Kestrel: A Programmable Array for Sequence Analysis,” in Proc. of IEEE International Conference on Application-Specific Systems, Architecture, and Processors, 1996, pp. 23–34.Google Scholar
- 17.D.T. Hoang, “Searching Genetic Databases on Splash 2,” in IEEE Workshop on FPGAs for Custom Computing Machines, 1993, pp. 185–191.Google Scholar
- 19.G. Knowles and P. Gardner-Stephen, “DASH: Localizing Dynamic Programming for Order of Magnitude Faster, Accurate Sequence Alignment,” in Proc. of the 3rd International IEEE Computer Society Computational Systems Bioinformatics Conference, 2004, pp. 732–735.Google Scholar
- 20.G. Knowles and P. Gardner-Stephen, “A New Hardware Architecture for Genomic and Proteomic Sequence Alignment,” in Proc. of IEEE Computational Systems Bioinformatics Conf., 2004.Google Scholar
- 21.J. Lancaster, J. Buhler and R.D. Chamberlain, “Acceleration of Ungapped Extension in Mercury BLAST.” in Proc. of the 7th Workshop on Media and Streaming Processors, November 2005.Google Scholar
- 22.D. Lavenier, S. Guytant, S. Derrien and S. Rubin, “A Reconfigurable Parallel Disk System for Filtering Genomic Banks,” in ERSA’03, Engineering of Reconfigurable Systems and Algorithms, 2003.Google Scholar
- 24.National Center for Biological Information, “Growth of GenBank,” 2002, http://www.ncbi.nlm.nih.gov/Genbank/genbankstats.html.
- 26.N. Pappas, “Searching Biological Sequence Databases Using Distributed Adaptive Computing,” Master’s thesis, Virginia Polytechnic Institute and State University, 2003.Google Scholar
- 34.B. West, R.D. Chamberlain, R.S. Indeck and Q. Zhang, “An FPGA-Based Search Engine for Unstructured Database,” in Proc. of 2nd Workshop on Application Specific Processors, December 2003, pp. 25–32.Google Scholar
- 35.Y. Yamaguchi, T. Maruyama and A. Konagaya, “High Speed Homology Search with FPGAs,” in Pacific Symposium on Biocomputing, 2002, pp. 271–282.Google Scholar
- 36.Q. Zhang, R.D. Chamberlain, R.S. Indeck, B. West and J. White, “Massively Parallel Data Mining Using Reconfigurable Hardware: Approximate String Matching,” in Proc. Workshop on Massively Parallel Processing, April 2004.Google Scholar