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Optimal Cube-connected Cube Multiprocessors

Optimal Cube-connected Cube Multiprocessors
Author: Xian-He Sun
Publisher:
Total Pages: 24
Release: 1993
Genre: Hypercube
ISBN:

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Abstract: "Many CFD (computational fluid dynamics) and other scientific applications can be partitioned into subproblems. However, in general the partitioned subproblems are very large. They demand high performance computing power themselves, and the solutions of the subproblems have to be combined at each time step. In this paper, the cube-connect cube (CCCube) architecture is studied. The CCCube architecture is an extended hypercube structure with each node represented as a cube. It requires fewer physical links between nodes than the hypercube, and provides the same communication support as the hypercube does on many applications. The reduced physical links can be used to enhance the bandwidth of the remanding links and, therefore, enhance the overall performance. The concept and the method to obtain optimal CCCubes, which are the CCCubes with a minimum number of links under a given total number of nodes, are proposed. The superiority of optimal CCCubes over standard hypercubes has also been shown in terms of the link usage in the embedding of a binomial tree. A useful computation structure based on a semi-binomial tree for divide-and-conquer type of parallel algorithms has been identified. We have shown that this structure can be implemented in optimal CCCubes without performance degradation compared with regular hypercubes. The result presented in this paper should provide a useful approach to design of scientific parallel computers."


Parallel Computing on Distributed Memory Multiprocessors

Parallel Computing on Distributed Memory Multiprocessors
Author: Füsun Özgüner
Publisher: Springer Science & Business Media
Total Pages: 327
Release: 2012-12-06
Genre: Computers
ISBN: 3642580661

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Advances in microelectronic technology have made massively parallel computing a reality and triggered an outburst of research activity in parallel processing architectures and algorithms. Distributed memory multiprocessors - parallel computers that consist of microprocessors connected in a regular topology - are increasingly being used to solve large problems in many application areas. In order to use these computers for a specific application, existing algorithms need to be restructured for the architecture and new algorithms developed. The performance of a computation on a distributed memory multiprocessor is affected by the node and communication architecture, the interconnection network topology, the I/O subsystem, and the parallel algorithm and communication protocols. Each of these parametersis a complex problem, and solutions require an understanding of the interactions among them. This book is based on the papers presented at the NATO Advanced Study Institute held at Bilkent University, Turkey, in July 1991. The book is organized in five parts: Parallel computing structures and communication, Parallel numerical algorithms, Parallel programming, Fault tolerance, and Applications and algorithms.


Hypercube Multiprocessors 1986

Hypercube Multiprocessors 1986
Author: Michael T. Heath
Publisher: SIAM
Total Pages: 304
Release: 1986-01-01
Genre: Hypercube networks (Computer networks)
ISBN: 9780898712094

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Proceedings -- Parallel Computing.


Hypercube Multiprocessors, 1987

Hypercube Multiprocessors, 1987
Author: Michael T. Heath
Publisher: SIAM
Total Pages: 796
Release: 1987-01-01
Genre: Computers
ISBN: 9780898712155

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Proceedings -- Parallel Computing.


Large Scale Eigenvalue Problems

Large Scale Eigenvalue Problems
Author: J. Cullum
Publisher: Elsevier
Total Pages: 339
Release: 1986-01-01
Genre: Mathematics
ISBN: 0080872387

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Results of research into large scale eigenvalue problems are presented in this volume. The papers fall into four principal categories: novel algorithms for solving large eigenvalue problems, novel computer architectures, computationally-relevant theoretical analyses, and problems where large scale eigenelement computations have provided new insight.