Improving Performance of Sparse Numerical Linear Algebra Computations : Algorithmic optimization techniques for sparse direct and sparse iterative numerical solvers of large linear equations

Language: English

Published by LAP LAMBERT Academic Publishing, 2010

3838334698 / 9783838334691

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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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nach der Bestellung gedruckt Neuware - Printed after ordering - With new processor families appearing every few years, it is increasingly harder to achieve high performance rates in sparse matrix computations. This monograph studies new methods for sparse matrix factorizations and applies them efficiently while retaining ease of use of existing solutions. The implementations are timed and analyzed using a commonly accepted set of test matrices. Contemporary processors are used for the tests. The new factorization techniques are proven to be quite competitive with state of the art software. In addition, an optimization effort is applied to an iterative algorithm that stands out for its numerical robustness. This also gives satisfactory results on the tested computing platforms in terms of performance improvement. The same set of test matrices is used to enable an easy comparison between both investigated techniques, even though they are customarily treated separately in the literature. Possible extensions of the presented work range from easily conceivable merging with existing solutions to rather more evolved schemes dependent on hard to predict progress in theoretical and algorithmic research. …

Seller Inventory # 9783838334691

Title
Improving Performance of Sparse Numerical Linear Algebra Computations : Algorithmic optimization techniques for sparse direct and sparse iterative numerical solvers of large linear equations
Author
Piotr Luszczek
Publisher
LAP LAMBERT Academic Publishing
Publication year
2010
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3838334698
ISBN 13
9783838334691
Item weight
143 grams
Dimensions
220x150x6 mm

AHA-BUCH GmbH

Einbeck, Germany

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