A new approach complilation from leading researchers, Bioinformatics: High Performance Parallel Computer Architecture discusses how to use bioinformatics applications and algorithms on a variety of modern parallel architectures. Two factors continue to encourage the increased use of modern parallel computer architectures to solve problems in computational biology and bioinformatics:? High-throughput techniques for DNA sequencing and analysis of gene expression that lead to exponential growth in the number of digital data and the biological revolution of multi-and many-core in computer architecture. Presenting key information about how to optimally utilize the parallel architecture, this book: Explain the algorithms and tools, including sequence alignment pairs, multiple sequence alignment, BLAST, to find a motive, pattern matching, sequence assembly, hidden Markov models, proteomics, and evolutionary tree reconstruction Address GPGPU technology and large-scale model related threaded programming CUDA Review architecture and programming FPGA Presents several parallel algorithms for computing alignments on the Cell / BE architecture, including linear-space pairwise alignment, alignment of syntenic, and spliced alignment Assessing the underlying concepts and advances in masterminding the phylogenetic likelihood function on a parallel computer architecture (from FPGA upto the IBM BlueGene / L supercomputer) Includes some effective techniques to fully exploit many-core computing capabilities CUDA-enabled GPUs to speed up protein sequence database searches, multiple sequence alignment, and motif search Describes methods based CUDA parallel to correct sequencing errors in the data base-pair HTSR Because the amount of sequence data available to the public is growing faster than the speed of single-core processor performance, modern tools of bioinformatics to take advantage of parallel computer architecture.