The Simd Model Of Parallel Computation


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The SIMD Model of Parallel Computation


The SIMD Model of Parallel Computation

Author: Robert Cypher

language: en

Publisher: Springer Science & Business Media

Release Date: 2012-12-06


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1.1 Background There are many paradigmatic statements in the literature claiming that this is the decade of parallel computation. A great deal of research is being de voted to developing architectures and algorithms for parallel machines with thousands, or even millions, of processors. Such massively parallel computers have been made feasible by advances in VLSI (very large scale integration) technology. In fact, a number of computers having over one thousand pro cessors are commercially available. Furthermore, it is reasonable to expect that as VLSI technology continues to improve, massively parallel computers will become increasingly affordable and common. However, despite the significant progress made in the field, many funda mental issues still remain unresolved. One of the most significant of these is the issue of a general purpose parallel architecture. There is currently a huge variety of parallel architectures that are either being built or proposed. The problem is whether a single parallel computer can perform efficiently on all computing applications.

Parallel Symbolic Computing: Languages, Systems, and Applications


Parallel Symbolic Computing: Languages, Systems, and Applications

Author: Robert H. Halstead

language: en

Publisher: Springer Science & Business Media

Release Date: 1993-11-03


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Parallel and distributed computing are becoming increasingly important as cost-effective ways to achieve high computational performance. Symbolic computations are notable for their use of irregular data structures and hence parallel symbolic computing has its own distinctive set of technical challenges. The papers in this book are based on presentations made at a workshop at MIT in October 1992. They present results in a wide range of areas including: speculative computation, scheduling techniques, program development tools and environments, programming languages and systems, models of concurrency and distribution, parallel computer architecture, and symbolic applications.

Parallel Computing for Bioinformatics and Computational Biology


Parallel Computing for Bioinformatics and Computational Biology

Author: Albert Y. Zomaya

language: en

Publisher: John Wiley & Sons

Release Date: 2006-04-14


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Discover how to streamline complex bioinformatics applications with parallel computing This publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful parallel computing tools can lead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution. A broad range of bioinformatics applications is covered with demonstrations on how each one can be parallelized to improve performance and gain faster rates of computation. Current parallel computing techniques and technologies are examined, including distributed computing and grid computing. Readers are provided with a mixture of algorithms, experiments, and simulations that provide not only qualitative but also quantitative insights into the dynamic field of bioinformatics. Parallel Computing for Bioinformatics and Computational Biology is a contributed work that serves as a repository of case studies, collectively demonstrating how parallel computing streamlines difficult problems in bioinformatics and produces better results. Each of the chapters is authored by an established expert in the field and carefully edited to ensure a consistent approach and high standard throughout the publication. The work is organized into five parts: * Algorithms and models * Sequence analysis and microarrays * Phylogenetics * Protein folding * Platforms and enabling technologies Researchers, educators, and students in the field of bioinformatics will discover how high-performance computing can enable them to handle more complex data sets, gain deeper insights, and make new discoveries.