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You will have Days after purchase to access the Full Text PDF Full Text HTML Access this document Buy this document Learn more about purchasing articles and standardsArticle Information An Efficient Parallel Implementation of the Hidden Markov Methods for Genomic SequenceSearch on Massively Parallel System Jiang, K. Thorsen, O. Peters, A. Smith, B. Sosa, You are not logged in. We report the performance of these nonoptimized versions as baselines. Pages15 Digital Object Identifier databases used for sequence comparison and sequence alignment are growing exponentially. This has popularized programs that carry out database searches.
You must log in to access Advanced or Author Search CrossRef Search AbstractPlus Records Full Text HTML Access this document Buy this document Learn more about purchasing articles and standards Learn more about subscription options or how to become an IEEE Member. An Efficient Parallel Implementation of the Hidden Markov Methods for Genomic SequenceSearch on Massively Parallel System Jiang, K. Thorsen, O. Peters, A. Smith, B. Sosa, IEEE Communications Society members If you subscribe to the IEEE Electronic Periodicals Package Plus, you must access your subscription at www. comsoc. org.
Pages15 Digital Object Identifier databases used for sequence comparison and sequence alignment are growing exponentially. We report the performance of these nonoptimized versions as baselines. This has popularized programs that carry out database searches. HMMER uses profile HMMs for sensitive database searching based on statistical descriptions of sequence familys consensus Durbin et al., 1998, Two of the nine programs were further parallelized to take advantage of the large number of processors, namely, hmmsearch and hmmpfam. You are not logged in.
For our study, we start by porting the parallel virtual machine PVM versions of these two programs currently available as part of the HMMER suite of programs.
This has popularized programs that carry out database searches. We report the performance of these nonoptimized versions as baselines. Current implementations of sequence alignment methods based on hidden Markov models HMM have proven to be computationally intensive and, hence, amenable to architectures with multiple processors. Our work also includes the introduction of an alternate sequence file indexing, multiplemaster configuration, dynamic data collection and, finally, load balancing via the indexed sequence files.
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