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Bioinformatics High Performance Parallel Computer Architectures (Embedded Multi-Core Systems)

Автор: Bo0mB0om » 21 апреля 2016
Bioinformatics High Performance Parallel Computer Architectures (Embedded Multi-Core Systems)
Bioinformatics: High Performance Parallel Computer Architectures (Embedded Multi-Core Systems) by Bertil Schmidt
English | July 15, 2010 | ISBN: 1439814880 | 370 Pages | PDF | 8 MB

New sequencing technologies have broken many experimental barriers to genome scale sequencing, leading to the extraction of huge quantities of sequence data. This expansion of biological databases established the need for new ways to harness and apply the astounding amount of available genomic information and convert it into substantive biological understanding.



A complilation of recent approaches from prominent researchers, Bioinformatics: High Performance Parallel Computer Architectures discusses how to take advantage of bioinformatics applications and algorithms on a variety of modern parallel architectures. Two factors continue to drive the increasing use of modern parallel computer architectures to address problems in computational biology and bioinformatics: high-throughput techniques for DNA sequencing and gene expression analysis-which have led to an exponential growth in the amount of digital biological data-and the multi- and many-core revolution within computer architecture.
Presenting key information about how to make optimal use of parallel architectures, this book:
Describes algorithms and tools including pairwise sequence alignment, multiple sequence alignment, BLAST, motif finding, pattern matching, sequence assembly, hidden Markov models, proteomics, and evolutionary tree reconstruction
Addresses GPGPU technology and the associated massively threaded CUDA programming model
Reviews FPGA architecture and programming
Presents several parallel algorithms for computing alignments on the Cell/BE architecture, including linear-space pairwise alignment, syntenic alignment, and spliced alignment
Assesses underlying concepts and advances in orchestrating the phylogenetic likelihood function on parallel computer architectures (ranging from FPGAs upto the IBM BlueGene/L supercomputer)
Covers several effective techniques to fully exploit the computing capability of many-core CUDA-enabled GPUs to accelerate protein sequence database searching, multiple sequence alignment, and motif finding
Explains a parallel CUDA-based method for correcting sequencing base-pair errors in HTSR data




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