High-Performance Scientific Computing: Algorithms and Applications - Softcover

9781447124382: High-Performance Scientific Computing: Algorithms and Applications

Softcover

ISBN 10: 1447124383 ISBN 13: 9781447124382

Publisher: Springer, 2012

This specific ISBN edition is currently not available.

Parallel Numerical Computing from Illiac IV to Exascale: The Contributions of Ahmed H. Sameh
K. Gallivan, E. Gallopoulos, A. Grama, B. Philippe, E. Polizzi, Y. Saad, F. Saied, and D. Sorensen

Computational Capacity-Based Co-design of Computer Systems
David J. Kuck

Measuring Computer Performance
William Jalby, David C. Wong, David J. Kuck, Jean-Thomas Acquaviva, and Jean-Christophe Beyler

A Compilation Framework for the Automatic Restructuring of Pointer-Linked Data Structures
Harmen L.A. van der Spek, C.W. Mattias Holm, and Harry A.G. Wijshoff

Dense Linear Algebra on Accelerated Multicore Hardware
Jack Dongarra, Jakub Kurzak, Piotr Luszczek, and Stanimire Tomov

The Explicit SPIKE Algorithm: Iterative Solution of the Reduced System
Carl Christian Kjelgaard Mikkelsen

The SPIKE Factorization as Domain Decomposition Method: Equivalent and Variant Approaches
Victor Eijkhout, and Robert van de Geijn

Parallel Solution of Sparse Linear Systems
Murat Manguoglu

Parallel Block-Jacobi SVD Methods
Martin Bečka, Gabriel Okša, and Mari�n Vajteršic

Robust and Efficient Multifrontal Solver for Large Discretized PDEs
Jianlin Xia

A Preconditioned Scheme for Nonsymmetric Saddle-Point Problems
Abdelkader Baggag

Effect of Ordering for Iterative Solvers in Structural Mechanics Problems
Sami A. Kilic

Scaling Hypre's Multigrid Solvers to 100,000 Cores
Allison H. Baker, Robert D. Falgout, Tzanio V. Kolev, and Ulrike Meier Yang

A Riemannian Dennis-Mor� Condition
K.A. Gallivan, C. Qi, and P.-A. Absil

A Jump-Start of Non-Negative Least Squares Solvers
Mu Wang, and Xiaoge Wang

Fast Nonnegative Tensor Factorization with an Active-Set-Like Method
Jingu Kim, and Haesun Park

Knowledge Discovery Using Nonnegative Tensor Factorization with Visual Analytics
Andrey A. Puretskiy and Michael W. Berry

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From the Back Cover

Advances in the development of parallel algorithms and system software now enable the ever-increasing power of scalable high-performance computers to be harnessed for scientific computing applications at fidelities that rival – and in many cases exceed – experimental methodologies.

This comprehensive text/reference, inspired by the visionary work of Prof. Ahmed H. Sameh, represents the state of the art in parallel numerical algorithms, applications, architectures, and system software. Articles in this collection address solutions to various challenges arising from concurrency, scale, energy efficiency, and programmability. These solutions are discussed in the context of diverse applications, ranging from scientific simulations to large-scale data analysis and mining.

Topics and features:

Includes contributions from an international selection of world-class authorities, inspired by the work of Prof. Ahmed H. Sameh
Examines various aspects of parallel algorithm-architecture interaction through articles on computational capacity-based codesign and automatic restructuring of programs using compilation techniques
Reviews emerging applications of numerical methods in information retrieval and data mining
Discusses the latest issues in dense and sparse matrix computations for modern high-performance systems, multicores, manycores and GPUs, and several perspectives on the Spike family of algorithms for solving linear systems
Presents outstanding challenges and developing technologies, and puts these in their historical context

This authoritative reference is a must-read for researchers and graduate students in disciplines as diverse as computational fluid dynamics, signal processing, and structural mechanics. Professionals involved in applications that rely on high-performance computers will also find the text an essential resource.

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