370 pages | 119 B/W Illus.
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:
Because the amount of publicly available sequence data is growing faster than single processor core performance speed, modern bioinformatics tools need to take advantage of parallel computer architectures. Now that the era of the many-core processor has begun, it is expected that future mainstream processors will be parallel systems. Beneficial to anyone actively involved in research and applications, this book helps you to get the most out of these tools and create optimal HPC solutions for bioinformatics.
Algorithms for Bioinformatics, B. Schmidt
Introduction to GPGPUs and Massively Threaded Programming, R.M. Farber
FPGA: Architecture and Programming, D. Maskell
Parallel Algorithms for Alignments on the Cell BE, A. Sarje and S. Aluru
Orchestrating the Phylogenetic Likelihood Function on Emerging Parallel Architectures, A. Stamatakis
Parallel Bioinformatics Algorithms for CUDA-enabled GPUs, Y. Liu, B. Schmidt, and D. Maskell
CUDA Error Correction Method for High-Throughput Short-Read Sequencing Data, H. Shi, W. Liu, and B. Schmidt
FPGA Acceleration of Seeded Similarity Searching, A.C. Jacob, J.M. Lancaster, J.D. Buhler, and R.D. Chamberlain
Seed-Based Parallel Protein Sequence Comparison Combining Multithreading, GPU, and FPGA Technologies, D. Lavenier and V.-H. Nguyen
Database Searching with Profi le Hidden Markov Models on Reconfi gurable and Many-Core Architectures, J.P.Walters, V. Chaudhary, and B. Schmidt
COPACOBANA: A Massively Parallel FPGA-Based Computer Architecture, M. Schimmler, L. Wienbrandt, T. Güneysu, and J. Bissel
Accelerating String Set Matching for Bioinformatics Using FPGA Hardware, Y.S. Dandass
Reconfi gurable Neural System and its Application to Dimeric Protein Binding Site Identification, F. Lin and M. Stepanova
Parallel FPGA Search Engine for Protein Identification, D. Coca, I. Bogdan, and R.J. Beynon