Limited resource scheduling in sparse matrix algorithms

We present analytic models and simulation techniques that describe the performance of the multifrontal method on distributed memory architectures. We focus on particular strategies for partitioning, clustering, and mapping of task nodes to processors in order to minimize the overall parallel executi...

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Bibliographic Details
Published in:Hawaii International Conference on System Sciences, 27th. Vol. 5: Biotechnology Vol. 2; pp. 473 - 482
Main Authors: Pozo, Smith
Format: Conference Proceeding
Language:English
Published: IEEE Comput. Soc. Press 1994
Subjects:
Computation time
Computer performance
Distributed memories
Parallel algorithms
Scheduling
Sparse matrices
ISBN:0818650907, 9780818650901
Online Access:Get full text
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Summary:We present analytic models and simulation techniques that describe the performance of the multifrontal method on distributed memory architectures. We focus on particular strategies for partitioning, clustering, and mapping of task nodes to processors in order to minimize the overall parallel execution time and minimize communication costs. The performance model has bees used to obtain estimates for the speedups of various engineering and scientific problems, on several distributed architectures. The result is that the available parallelism of these problems is strongly dependent on the sparsity structure of the input matrices.< >
ISBN:0818650907
9780818650901
DOI:10.1109/HICSS.1994.323236