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Summary
Written by internationally recognized experts, this book introduces emerging parallel architectures, their corresponding programming paradigms, and their bioinformatics applications. Providing two introductory chapters to supply necessary background on sequential bioinformatics algorithms and parallel architectures such as multi-cores, Cell B.E., GPUs, FPGAs, it then presents parallelization strategies and performance evaluations for a number of state-of-the-art bioinformatics algorithms and tools. Topics include pairwise sequence alignment, multiple sequence alignment, BLAST, motif finding, pattern matching, sequence assembly, hidden Markov models, molecular dynamics, and evolutionary tree reconstruction.
Table of Contents
| Preface | p. vii |
| Editor | p. xi |
| Contributors | p. xiii |
| Algorithms for Bioinformatics | p. 1 |
| Introduction to GPGPUs and Massively Threaded Programming | p. 29 |
| FPGA: Architecture and Programming | p. 49 |
| Parallel Algorithms for Alignments on the Cell BE | p. 59 |
| Orchestrating the Phylogenetic Likelihood Function on Emerging Parallel Architectures | p. 85 |
| Parallel Bioinformatics Algorithms for CUDA-Enabled GPUs | p. 117 |
| CUDA Error Correction Method for High-Throughput Short-Read Sequencing Data | p. 139 |
| FPGA Acceleration of Seeded Similarity Searching | p. 157 |
| Seed-Based Parallel Protein Sequence Comparison Combining Multithreading, GPU, and FPGA Technologies | p. 181 |
| Database Searching with Profile-Hidden Markov Models on Reconfigurable and Many-Core Architectures | p. 203 |
| COPACOBANA: A Massively Parallel FPGA-Based Computer Architecture | p. 223 |
| Accelerating String Set Matching for Bioinformatics Using FPGA Hardware | p. 263 |
| Reconfigurable Neural System and Its Application to Dimeric Protein Binding Site Identification | p. 285 |
| Parallel FPGA Search Engine for Protein Identification | p. 313 |
| Index | p. 337 |
| Table of Contents provided by Ingram. All Rights Reserved. |
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