Combinatorial Scientific Computing

by Uwe Naumann (Editor), Olaf Schenk (Editor)

Overview

Combinatorial Scientific Computing explores the latest research on creating algorithms and software tools to solve key combinatorial problems on large-scale high-performance computing architectures. It includes contributions from international researchers who are pioneers in designing software and applications for high-performance computing systems

Product Details

ISBN-13:	9781040070390
Publisher:	CRC Press
Publication date:	01/25/2012
Sold by:	Barnes & Noble
Format:	eBook
Pages:	600
File size:	14 MB
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About the Author

Uwe Naumann is an associate professor of computer science at RWTH Aachen University. Dr. Naumann has published more than 80 peer-reviewed papers and chaired several workshops. His research focuses on algorithmic differentiation, combinatorial graph algorithms, high-performance scientific computing, and the application of corresponding methods to real-world problems in computational science, engineering, and finance.

Olaf Schenk is an associate professor of computer science at the University of Lugano. Dr. Schenk has published more than 70 peer-reviewed book chapters, journal articles, and conference contributions. In 2008, he received an IBM Faculty Award on Cell Processors for Biomedical Hyperthermia Applications. His research interests include algorithmic and architectural problems in computational mathematics, scientific computing, and high-performance computing.

Table of Contents

Combinatorial Scientific Computing: Past Successes, Current Opportunities, Future Challenges. Combinatorial Problems in Solving Linear Systems. Combinatorial Preconditioners. Scalable Hybrid Linear Solvers. Combinatorial Problems in Algorithmic Differentiation. Combinatorial Problems in OpenAD. Getting Started with ADOL-C. Algorithmic Differentiation and Nonlinear Optimization for an Inverse Medium Problem. Combinatorial Aspects/Algorithms in Computational Fluid Dynamics. Unstructured Mesh Generation. 3D Delaunay Mesh Generation. Two-Dimensional Approaches to Sparse Matrix Partitioning. Parallel Partitioning, Ordering, and Coloring in Scientific Computing. Scotch and PT-Scotch Graph Partitioning Software: An Overview. Massively Parallel Graph Partitioning: A Case in Human Bone Simulations. Algorithmic and Statistical Perspectives on Large-Scale Data Analysis. Computational Challenges in Emerging Combinatorial Scientific Computing Applications. Spectral Graph Theory. Algorithms for Visualizing Large Networks.

Editorial Reviews

"This text is a deep and—as any scientist working with large datasets will tell you—much-needed treatment of an emerging field, focused specifically on large-scale computing. It is readable and practical, and covers areas that have many open problems, making it an excellent resource both for scientific researchers looking for solutions and computing researchers looking for computational and combinatoric problems to solve."
— Computing Reviews, May 2013

From the Publisher