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Combinatorial scientific computing

[NT 42944] Record Type:	[NT 1579] Language materials, printed : [NT 40817] monographic
[NT 47354] Secondary Intellectual Responsibility:	SchenkOlaf, 1967-
[NT 47354] Secondary Intellectual Responsibility:	NaumannUwe, 1969-
[NT 47351] Place of Publication:	Boca Raton, FL
[NT 47263] Published:	CRC Press;
[NT 47352] Year of Publication:	c2012
[NT 47264] Description:	xxiii, 568 p.ill. : 25 cm.;
[NT 47298] Series:	Chapman & Hall/CRC computational science
[NT 47266] Subject:	Combinatorial analysis. -
[NT 47266] Subject:	Science - Data processing -
[NT 47266] Subject:	Computer programming. -
[NT 51398] 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 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"--Provided by publisher
[NT 50961] ISBN:	978-1-4398-2735-2bound
[NT 50961] ISBN:	1-4398-2735-4bound

Combinatorial scientific computing
Combinatorial scientific computing / edited by Uwe Naumann, Olaf Schenk - Boca Raton, FL : CRC Press, c2012. - xxiii, 568 p. ; ill. ; 25 cm.. - (Chapman & Hall/CRC computational science ; 12).
Includes bibliographical references and index..
ISBN 978-1-4398-2735-2ISBN 1-4398-2735-4
Combinatorial analysis.ScienceComputer programming. -- Data processing

Schenk, Olaf

Combinatorial scientific computing
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210 $a Boca Raton, FL $d c2012 $c CRC Press
215 1 $a xxiii, 568 p. $c ill. $d 25 cm.
225 2 # $a Chapman & Hall/CRC computational science $v 12
320 $a Includes bibliographical references and index.
330 $a "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"--Provided by publisher
606 # $a Combinatorial analysis. $3 69111
606 # $a Science $x Data processing $2 lc $3 271717
606 # $a Computer programming. $3 29563
676 $a 511.6
702 1 $a Schenk $b Olaf $f 1967- $4 edited $3 294737
702 1 $a Naumann $b Uwe $f 1969- $4 edited $3 294736
801 0 $a tw $b 嶺東科技大學圖書館