Combinatorial scientific computing:
"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 inte...
Gespeichert in:
| Weitere beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton
CRC Press
2012
|
| Schriftenreihe: | Chapman & Hall/CRC computational science series
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781439827369/?ar |
| Zusammenfassung: | "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"-- |
| Beschreibung: | Includes bibliographical references |
| Umfang: | 1 Online-Ressource (xxiii, 549 Seiten, 8 unnumbered Seiten of plates) illustrations (some color) |
| ISBN: | 9781439827369 1439827362 1439827354 9781439827352 9781466547933 1466547936 |
Internformat
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| 245 | 1 | 0 | |a Combinatorial scientific computing |c edited by Uwe Naumann, Olaf Schenk |
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| isbn | 9781439827369 1439827362 1439827354 9781439827352 9781466547933 1466547936 |
| language | English |
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| spelling | Combinatorial scientific computing edited by Uwe Naumann, Olaf Schenk Boca Raton CRC Press 2012 1 Online-Ressource (xxiii, 549 Seiten, 8 unnumbered Seiten of plates) illustrations (some color) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Chapman & Hall/CRC computational science series Includes bibliographical references "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"-- Computer programming Science Data processing Combinatorial analysis Programmation (Informatique) Sciences ; Informatique Analyse combinatoire computer programming COMPUTERS ; Programming ; Algorithms MATHEMATICS ; General MATHEMATICS ; Combinatorics Science ; Data processing Naumann, Uwe 1969- MitwirkendeR ctb Schenk, Olaf 1967- MitwirkendeR ctb |
| spellingShingle | Combinatorial scientific computing Computer programming Science Data processing Combinatorial analysis Programmation (Informatique) Sciences ; Informatique Analyse combinatoire computer programming COMPUTERS ; Programming ; Algorithms MATHEMATICS ; General MATHEMATICS ; Combinatorics Science ; Data processing |
| title | Combinatorial scientific computing |
| title_auth | Combinatorial scientific computing |
| title_exact_search | Combinatorial scientific computing |
| title_full | Combinatorial scientific computing edited by Uwe Naumann, Olaf Schenk |
| title_fullStr | Combinatorial scientific computing edited by Uwe Naumann, Olaf Schenk |
| title_full_unstemmed | Combinatorial scientific computing edited by Uwe Naumann, Olaf Schenk |
| title_short | Combinatorial scientific computing |
| title_sort | combinatorial scientific computing |
| topic | Computer programming Science Data processing Combinatorial analysis Programmation (Informatique) Sciences ; Informatique Analyse combinatoire computer programming COMPUTERS ; Programming ; Algorithms MATHEMATICS ; General MATHEMATICS ; Combinatorics Science ; Data processing |
| topic_facet | Computer programming Science Data processing Combinatorial analysis Programmation (Informatique) Sciences ; Informatique Analyse combinatoire computer programming COMPUTERS ; Programming ; Algorithms MATHEMATICS ; General MATHEMATICS ; Combinatorics Science ; Data processing |
| work_keys_str_mv | AT naumannuwe combinatorialscientificcomputing AT schenkolaf combinatorialscientificcomputing |