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MPI framework for parallel searching in large biological databases

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Bibliographic Details
Title: MPI framework for parallel searching in large biological databases
Authors: Battré, Dominic dominic@battre.de, Angulo, David Sigfredo1 dangulo@cti.depaul.edu
Source: Journal of Parallel & Distributed Computing. Dec2006, Vol. 66 Issue 12, p1503-1511. 9p.
Subjects: Information storage & retrieval systems, Databases, Amino acid sequence, Mass spectrometry
Abstract: Abstract: In this paper, we address the problem of searching huge biological databases on the scale of at least several gigabytes by utilizing parallel processing. Biological databases storing DNA sequences, protein sequences, or mass spectra are growing exponentially. Searches through these databases consume exponentially growing computational resources as well. We demonstrate herein a general use, MPI based, framework for generically splitting databases amongst several computational nodes. The combined RAM of the nodes working in tandem is often sufficient to keep the entire database in memory, and therefore to search it efficiently without paging to disk. The framework runs as a persistent service, processing all submitted queries. This allows for query reordering and better utilization of the memory. Thereby, we achieve superlinear speedups compared to single processor implementations. We demonstrate the utility and speedup of the framework using a real biological database and an actual searching algorithm for mass spectrometry. [Copyright &y& Elsevier]
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Database: Engineering Source
Description
Abstract:Abstract: In this paper, we address the problem of searching huge biological databases on the scale of at least several gigabytes by utilizing parallel processing. Biological databases storing DNA sequences, protein sequences, or mass spectra are growing exponentially. Searches through these databases consume exponentially growing computational resources as well. We demonstrate herein a general use, MPI based, framework for generically splitting databases amongst several computational nodes. The combined RAM of the nodes working in tandem is often sufficient to keep the entire database in memory, and therefore to search it efficiently without paging to disk. The framework runs as a persistent service, processing all submitted queries. This allows for query reordering and better utilization of the memory. Thereby, we achieve superlinear speedups compared to single processor implementations. We demonstrate the utility and speedup of the framework using a real biological database and an actual searching algorithm for mass spectrometry. [Copyright &y& Elsevier]
ISSN:07437315
DOI:10.1016/j.jpdc.2006.08.003