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  3. Parallel Scientific Computing in C++ and MPI
  • Cited by 98
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    • Publisher:
      Cambridge University Press
      Publication date:
      October 2013
      June 2003
      ISBN:
      9780511812583
      9780521520805
      DOI:
      https://doi.org/10.1017/CBO9780511812583
      Dimensions:
      Weight & Pages:
      Dimensions:
      (240 x 215 mm)
      Weight & Pages:
      1.07kg, 630 Pages
      • Subjects:
      Mathematics, Computational Science, Computer Science, Scientific Computing, Scientific Software
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    • Selected: Digital
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    Subjects:
    Mathematics, Computational Science, Computer Science, Scientific Computing, Scientific Software
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    Book description

    Numerical algorithms, modern programming techniques, and parallel computing are often taught serially across different courses and different textbooks. The need to integrate concepts and tools usually comes only in employment or in research - after the courses are concluded - forcing the student to synthesise what is perceived to be three independent subfields into one. This book provides a seamless approach to stimulate the student simultaneously through the eyes of multiple disciplines, leading to enhanced understanding of scientific computing as a whole. The book includes both basic as well as advanced topics and places equal emphasis on the discretization of partial differential equations and on solvers. Some of the advanced topics include wavelets, high-order methods, non-symmetric systems, and parallelization of sparse systems. The material covered is suited to students from engineering, computer science, physics and mathematics.

    Reviews

    ‘This book is a valuable addition to the literature on numerical algorithms.’

    Source: Numerical Algorithms

    'There is plenty of material for a two-semester sequence, or selected chapters could be used for a one-semester course on numerical linear algebra. The presentation is clear, practical, and lively … this text would be a very useful reference for statistics students and professionals) seeking to take their statistical computing skills into the parallel realm.'

    Source: Journal of the American Statistical Association

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