Overview
- Editors:
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Are Magnus Bruaset
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Simula Research Laboratory, Lysaker, Fornebu, Norway
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Aslak Tveito
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Simula Research Laboratory, Lysaker, Fornebu, Norway
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Table of contents (13 papers)
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Parallel Computing
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- Ricky A. Kendall, Masha Sosonkina, William D. Gropp, Robert W. Numrich, Thomas Sterling
Pages 3-54
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- James D. Teresco, Karen D. Devine, Joseph E. Flaherty
Pages 55-88
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- Martin Rumpf, Robert Strzodka
Pages 89-132
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Parallel Algorithms
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Front Matter
Pages 133-133
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- Luca Formaggia, Marzio Sala, Fausto Saleri
Pages 135-163
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- Frank Hülsemann, Markus Kowarschik, Marcus Mohr, Ulrich Rüde
Pages 165-208
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Parallel Software Tools
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Front Matter
Pages 265-265
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- Robert D. Falgout, Jim E. Jones, Ulrike Meier Yang
Pages 267-294
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- Xing Cai, Hans Petter Langtangen
Pages 295-325
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- Lois Curfman McInnes, Benjamin A. Allan, Robert Armstrong, Steven J. Benson, David E. Bernholdt, Tamara L. Dahlgren et al.
Pages 327-381
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Parallel Applications
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Front Matter
Pages 383-383
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- Xing Cai, Glenn Terje Lines
Pages 385-411
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- Matthew G. Knepley, Richard F. Katz, Barry Smith
Pages 413-438
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- Carolin Körner, Thomas Pohl, Ulrich Rüde, Nils Thürey, Thomas Zeiser
Pages 439-466
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Back Matter
Pages 467-487
About this book
Since the dawn of computing, the quest for a better understanding of Nature has been a driving force for technological development. Groundbreaking achievements by great scientists have paved the way from the abacus to the supercomputing power of today. When trying to replicate Nature in the computer’s silicon test tube, there is need for precise and computable process descriptions. The scienti?c ?elds of Ma- ematics and Physics provide a powerful vehicle for such descriptions in terms of Partial Differential Equations (PDEs). Formulated as such equations, physical laws can become subject to computational and analytical studies. In the computational setting, the equations can be discreti ed for ef?cient solution on a computer, leading to valuable tools for simulation of natural and man-made processes. Numerical so- tion of PDE-based mathematical models has been an important research topic over centuries, and will remain so for centuries to come. In the context of computer-based simulations, the quality of the computed results is directly connected to the model’s complexity and the number of data points used for the computations. Therefore, computational scientists tend to ?ll even the largest and most powerful computers they can get access to, either by increasing the si e of the data sets, or by introducing new model terms that make the simulations more realistic, or a combination of both. Today, many important simulation problems can not be solved by one single computer, but calls for parallel computing.