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Combinatorial scientific computing

Author: Uwe Naumann; Olaf Schenk
Publisher: Boca Raton, FL : CRC Press, ©2012.
Series: Chapman & Hall/CRC computational science series.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"Foreword the ongoing era of high-performance computing is filled with enormous potential for scientific simulation, but also with daunting challenges. Architectures for high-performance computing may have thousands of processors and complex memory hierarchies paired with a relatively poor interconnecting network performance. Due to the advances being made in computational science and engineering, the applications  Read more...
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Uwe Naumann; Olaf Schenk
ISBN: 9781439827352 1439827354
OCLC Number: 617637682
Description: xxiii, 568 pages : illustrations ; 24 cm.
Contents: 1. Combinatorial scientific computing : past successes, current opportunities, future challenges / Bruce Hendrickson and Alex Pothen --
2. Combinatorial problems in solving linear systems / Iain Duff and Bora Uðcar --
3. Combinatorial preconditioners / Sivan Toledo and Haim Avron --
4. A scalable hybrid linear solver based on combinatorial algorithms / Madan Sathe [and others] --
5. Combinatorial problems in algorithmic differentiation / Uwe Naumann and Andrea Walther --
6. Combinatorial problems in OpenAD / Jean Utke and Uwe Naumann --
7. Getting started with ADOL-C / Andrea Walther and Andreas Griewank --
8. Algorithmic differentiation and nonlinear optimization for an inverse medium problem / Johannes Huber [and others] --
9. Combinatorial aspects/algorithms in computational fluid dynamics / Rainald Lèohner --
10. Unstructured mesh generation / Jonathan Richard Shewchuk --
11. 3D Delaunay mesh generation / Klaus Gèartner [and others] --
12. Two-dimensional approaches to sparse matrix partitioning / Rob H. Bisseling [and others] --
13. Parallel partitioning, coloring, and ordering in scientific computing / E.G. Boman [and others] --
14. Scotch and PT-Scotch graph partitioning software : an overview / Franðcois Pellegrini --
15. Massively parallel graph partitioning : a case in human bone simulations / C. Bekas [and others] --
16. Algorithmic and statistical perspectives on large-scale data analysis / Michael W. Mahoney --
17. Computational challenges in emerging combinatorial scientific computing applications / David A. Bader and Kamesh Madduri --
18. Spectral graph theory / Daniel Spielman --
19. Algorithms for visualizing large networks / Yifan Hu.
Series Title: Chapman & Hall/CRC computational science series.
Responsibility: edited by Uwe Naumann, Olaf Schenk.

Abstract:

"Foreword the ongoing era of high-performance computing is filled with enormous potential for scientific simulation, but also with daunting challenges. Architectures for high-performance computing may have thousands of processors and complex memory hierarchies paired with a relatively poor interconnecting network performance. Due to the advances being made in computational science and engineering, the applications that run on these machines involve complex multiscale or multiphase physics, adaptive meshes and/or sophisticated numerical methods. A key challenge for scientific computing is obtaining high performance for these advanced applications on such complicated computers and, thus, to enable scientific simulations on a scale heretofore impossible. A typical model in computational science is expressed using the language of continuous mathematics, such as partial differential equations and linear algebra, but techniques from discrete or combinatorial mathematics also play an important role in solving these models efficiently. Several discrete combinatorial problems and data structures, such as graph and hypergraph partitioning, supernodes and elimination trees, vertex and edge reordering, vertex and edge coloring, and bipartite graph matching, arise in these contexts. As an example, parallel partitioning tools can be used to ease the task of distributing the computational workload across the processors. The computation of such problems can be represented as a composition of graphs and multilevel graph problems that have to be mapped to different microprocessors"--

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"This text is a deep and--as any scientist working with large datasets will tell you--much-needed treatment of an emerging field, focused specifically on large-scale computing. It is readable and Read more...

 
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