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An Introduction to Parallel and Vector Scientific Computation

$59.99

textbook

Part of Cambridge Texts in Applied Mathematics

  • Date Published: August 2006
  • availability: In stock
  • format: Paperback
  • isbn: 9780521683371

$59.99
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About the Authors
  • In this text, students of applied mathematics, science and engineering are introduced to fundamental ways of thinking about the broad context of parallelism. The authors begin by giving the reader a deeper understanding of the issues through a general examination of timing, data dependencies, and communication. These ideas are implemented with respect to shared memory, parallel and vector processing, and distributed memory cluster computing. Threads, OpenMP, and MPI are covered, along with code examples in Fortran, C, and Java. The principles of parallel computation are applied throughout as the authors cover traditional topics in a first course in scientific computing. Building on the fundamentals of floating point representation and numerical error, a thorough treatment of numerical linear algebra and eigenvector/eigenvalue problems is provided. By studying how these algorithms parallelize, the reader is able to explore parallelism inherent in other computations, such as Monte Carlo methods.

    • Contains exercises and programming problems as well as suggestions for term projects
    • Use of directed acyclic graphs helps students visualize timing and data dependencies which can be critical when using parallel code
    • Instruction on programming parallel, vector and distributed memory machines in Fortran, C and Java
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    Product details

    • Date Published: August 2006
    • format: Paperback
    • isbn: 9780521683371
    • length: 300 pages
    • dimensions: 229 x 154 x 18 mm
    • weight: 0.432kg
    • contains: 10 tables 120 exercises
    • availability: In stock
  • Table of Contents

    Part I. Machines and Computation:
    1. Introduction - the nature of high performance computation
    2. Theoretical considerations - complexity
    3. Machine implementations
    Part II. Linear Systems:
    4. Building blocks - floating point numbers and basic linear algebra
    5. Direct methods for linear systems and LU decomposition
    6. Direct methods for systems with special structure
    7. Error analysis and QR decomposition
    8. Iterative methods for linear systems
    9. Finding eigenvalues and eigenvectors
    Part III. Monte Carlo Methods:
    10. Monte Carlo simulation
    11. Monte Carlo optimization
    Appendix: programming examples.

  • Authors

    Ronald W. Shonkwiler, Georgia Institute of Technology
    Ronald W. Shonkwiler is a Professor in the School of Mathematics at the Georgia Institute of Technology. He has authored or co-authored over 50 research papers in areas of functional analysis, mathematical biology, image processing algorithms, fractal geometry, neural networks and Monte Carlo optimization methods. His algorithm for monochrome image comparison is part of a US patent for fractal image compression. He has co-authored two other books, An Introduction to the Mathematics of Biology and The Handbook of Stochastic Analysis and Applications.

    Lew Lefton, Georgia Institute of Technology
    Lew Lefton is the Director of Information Technology at the Georgia Institute of Technology where he has built and maintained several computing clusters which are used to implement parallel computations. Prior to that he was a tenured faculty member in the Department of Mathematics at the University of New Orleans. His academic interests are in differential equations, applied mathematics, numerical analysis (in particular, finite element methods) and scientific computing.

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