The matching problem is central to graph theory and the theory of algorithms. This book provides a comprehensive and straightforward introduction to the basic methods for designing efficient parallel algorithms for graph matching problems. Written for students at the beginning graduate level, the exposition is largely self-contained and example-driven; prerequisites have been kept to a minimum by including relevant background material. The book contains full details of several new techniques and will be of interest to researchers in computer science, operations research, discrete mathematics, and electrical engineering. The main theoretical tools are presented in three independent chapters, devoted to combinatorial tools, probabilistic tools, and algebraic tools. One of the goals of the book is to show how these three approaches can be combined to develop efficient parallel algorithms. The book represents a meeting point of interesting algorithmic techniques and opens up new algebraic and geometric areas.

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. The book offers a state-of-the-art overview of the latest research, tool development, and applications. It focuses on load balancing and parallelization on high-performance computers, large-scale optimization, algorithmic differentiation of numerical simulation code, sparse matrix software tools, and combinatorial challenges and applications in large-scale social networks. The authors unify these seemingly disparate areas through a common set of abstractions and algorithms based on combinatorics, graphs, and hypergraphs. Combinatorial algorithms have long played a crucial enabling role in scientific and engineering computations and their importance continues to grow with the demands of new applications and advanced architectures. By addressing current challenges in the field, this volume sets the stage for the accelerated development and deployment of fundamental enabling technologies in high-performance scientific computing.

Mathematics of Computing -- Parallelism.

Parallel-Algorithms for Regular Architectures is the first book to concentrate exclusively on algorithms and paradigms for programming parallel computers such as the hypercube, mesh, pyramid, and mesh-of-trees.

The papers in this book were presented at the CMU Conference on VLSI Systems and Computations, held October 19-21, 1981 in Pittsburgh, Pennsylvania. The conference was organized by the Computer Science Department, Carnegie-Mellon University and was partially supported by the National Science Foundation and the Office of Naval Research. These proceedings focus on the theory and design of computational systems using VLSI. Until very recently, integrated-circuit research and development were concentrated in the device physics and fabrication design disciplines and in the integrated-circuit industry itself. Within the last few years, a community of researchers is growing to address issues closer to computer science: the relationship between computing structures and the physical structures that implement them; the specification and verification of computational procosses implemented in VLSI; the use of massively parallel computing made possible by VLSI; the design of special purpose computing architectures; and the changes in general-purpose computer architecture that VLSI makes possible. It is likely that the future exploitation of VLSI technology depends as much on structural and design innovations as on advances in fabrication technology. The book is divided into nine sections: - Invited Papers. Six distinguished researchers from industry and academia presented invited papers. - Models of Computation. The papers in this section deal with abstracting the properties of VLSI circuits into models that can be used to analyze the chip area, time or energy required for a particular computation.

This book provides a comprehensive analysis of the most important topics in parallel computation. It is written so that it may be used as a self-study guide to the field, and researchers in parallel computing will find it a useful reference for many years to come. The first half of the book consists of an introduction to many fundamental issues in parallel computing. The second half provides lists of P-complete- and open problems. These lists will have lasting value to researchers in both industry and academia. The lists of problems, with their corresponding remarks, the thorough index, and the hundreds of references add to the exceptional value of this resource. While the exciting field of parallel computation continues to expand rapidly, this book serves as a guide to research done through 1994 and also describes the fundamental concepts that new workers will need to know in coming years. It is intended for anyone interested in parallel computing, including senior level undergraduate students, graduate students, faculty, and people in industry. As an essential reference, the book will be needed in all academic libraries.

There is a software gap between the hardware potential and the performance that can be attained using today's software parallel program development tools. The tools need manual intervention by the programmer to parallelize the code. Programming a parallel computer requires closely studying the target algorithm or application, more so than in the traditional sequential programming we have all learned. The programmer must be aware of the communication and data dependencies of the algorithm or application. This book provides the techniques to explore the possible ways to program a parallel computer for a given application.