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Combinatorial scientific computing /

Naumann, Uwe, (1969-)

Combinatorial scientific computing /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Combinatorial scientific computing // edited by Uwe Naumann, Olaf Schenk.
其他作者:	Schenk, Olaf,
出版者:	Boca Raton, FL :CRC Press, : c2012.,
面頁冊數:	xxiii, 568 p., [8] p. of plates :ill. ; : 25 cm.;
標題:	MATHEMATICS / Combinatorics. -
ISBN:	9781439827352 (cloth) :

Combinatorial scientific computing /
Combinatorial scientific computing /edited by Uwe Naumann, Olaf Schenk. - Boca Raton, FL :CRC Press,c2012. - xxiii, 568 p., [8] p. of plates :ill. ;25 cm. - Chapman & Hall/CRC computational science.

Includes bibliographical references and index.

"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"--

ISBN: 9781439827352 (cloth) :NT2737

LCCN: 2011044663Subjects--Topical Terms:

848829
MATHEMATICS / Combinatorics.

LC Class. No.: QA76.6 / .C6275 2012

Dewey Class. No.: 511/.6

Combinatorial scientific computing /
LDR:02390cam a2200241 a 4500 001 715222
003 DLC
005 20120410113223.0
008 121122s2012 fluaf b 001 0 eng
010 $a 2011044663
020 $a 9781439827352 (cloth) : $c NT2737
020 $a 1439827354 (cloth)
035 $a 2011044663
040 $a DLC $c DLC $d DLC $d NFU
042 $a pcc
050 0 0 $a QA76.6 $b .C6275 2012
082 0 0 $a 511/.6 $2 23
084 $a COM051300 $a MAT000000 $a MAT036000 $2 bisacsh
245 0 0 $a Combinatorial scientific computing / $c edited by Uwe Naumann, Olaf Schenk.
260 $a Boca Raton, FL : $c c2012. $b CRC Press,
300 $a xxiii, 568 p., [8] p. of plates : $b ill. ; $c 25 cm.
490 0 $a Chapman & Hall/CRC computational science
504 $a Includes bibliographical references and index.
520 $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"-- $c Provided by publisher.
650 7 $a MATHEMATICS / Combinatorics. $2 bisacsh $3 848829
650 7 $a MATHEMATICS / General. $2 bisacsh $3 848828
650 7 $a COMPUTERS / Programming / Algorithms. $2 bisacsh $3 848827
650 0 $a Combinatorial analysis. $3 527896
650 0 $a Science $x Data processing. $3 528623
650 0 $a Computer programming. $3 527822
700 1 $a Schenk, Olaf, $d 1967- $3 848826
700 1 $a Naumann, Uwe, $d 1969- $3 848825