Torus data flow for parallel computation of missized matrix problems

R. E. Funderlic; George A. Geist

doi:10.1016/0024-3795(86)90166-7

Torus data flow for parallel computation of missized matrix problems

R. E. Funderlic, George A. Geist

National Center for Computational Scienc

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

A data-flow approach is used to solve dense symmetric systems of equations on a torus-connected 2-D mesh of processors. A torus mapping of the matrix onto this processor array allows the Cholesky decomposition to be completed in 3n - 2 time steps using only n²/4 processors (less than half the number needed in previously reported results). New definitions for missized problems and parallel algorithm performance are given along with various time-step, efficiency, and processor utilization plots.

Original language	English
Pages (from-to)	149-163
Number of pages	15
Journal	Linear Algebra and Its Applications
Volume	77
Issue number	C
DOIs	https://doi.org/10.1016/0024-3795(86)90166-7
State	Published - May 1986

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title = "Torus data flow for parallel computation of missized matrix problems",

abstract = "A data-flow approach is used to solve dense symmetric systems of equations on a torus-connected 2-D mesh of processors. A torus mapping of the matrix onto this processor array allows the Cholesky decomposition to be completed in 3n - 2 time steps using only n2/4 processors (less than half the number needed in previously reported results). New definitions for missized problems and parallel algorithm performance are given along with various time-step, efficiency, and processor utilization plots.",

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AU - Funderlic, R. E.

AU - Geist, George A.

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N2 - A data-flow approach is used to solve dense symmetric systems of equations on a torus-connected 2-D mesh of processors. A torus mapping of the matrix onto this processor array allows the Cholesky decomposition to be completed in 3n - 2 time steps using only n2/4 processors (less than half the number needed in previously reported results). New definitions for missized problems and parallel algorithm performance are given along with various time-step, efficiency, and processor utilization plots.

AB - A data-flow approach is used to solve dense symmetric systems of equations on a torus-connected 2-D mesh of processors. A torus mapping of the matrix onto this processor array allows the Cholesky decomposition to be completed in 3n - 2 time steps using only n2/4 processors (less than half the number needed in previously reported results). New definitions for missized problems and parallel algorithm performance are given along with various time-step, efficiency, and processor utilization plots.

UR - https://www.scopus.com/pages/publications/46149131021

U2 - 10.1016/0024-3795(86)90166-7

DO - 10.1016/0024-3795(86)90166-7

M3 - Article

AN - SCOPUS:46149131021

SN - 0024-3795

VL - 77

SP - 149

EP - 163

JO - Linear Algebra and Its Applications

JF - Linear Algebra and Its Applications

IS - C

ER -

Torus data flow for parallel computation of missized matrix problems

Abstract

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