Parallel Algorithms For Numerical Linear Algebra
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Parallel Algorithms for Numerical Linear Algebra
This is the first in a new series of books presenting research results and developments concerning the theory and applications of parallel computers, including vector, pipeline, array, fifth/future generation computers, and neural computers.All aspects of high-speed computing fall within the scope of the series, e.g. algorithm design, applications, software engineering, networking, taxonomy, models and architectural trends, performance, peripheral devices.Papers in Volume One cover the main streams of parallel linear algebra: systolic array algorithms, message-passing systems, algorithms for parallel shared-memory systems, and the design of fast algorithms and implementations for vector supercomputers.
Parallel Numerical Linear Algebra
Contents: A Java-Based Distributed Debugger Supporting MPI and PVM; On Encoding Neural Networks to Estimate the Atmospheric Point Spread Function in a Parallel Environment; A Comparison of Parallel Solvers for Diagonally Dominant and General Narrow-Banded Linear Systems; Mapping Strategies in Data Parallel Programming Models; the Projection Methods; Parallel Multiplication of a Vector by a Kronecker Product of Matrices; Parallel Sparse Matrix Algorithms for Air Pollution Models; Band Preconditioners -- Application to Preconditioned Conjugate Gradient Methods on Parallel Computers.
Parallel Algorithms and Matrix Computation
Author: Jagdish J. Modi
language: en
Publisher: Oxford University Press, USA
Release Date: 1988
An introduction to parallel computation and the application of parallel algorithms to numerical linear algebra, based on a lecture course at the University of Cambridge. The emphasis is on the design and analysis of algorithms which are of importance to industrial and academic research.