A Tool for Performance Modeling of Parallel Programs

Current performance prediction analytical models try to characterize the performance behavior of actual machines through a small set of parameters. In practice, substantial deviations are observed. These differences are due to factors as memory hierarchies or network latency. A natural approach is t...

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Main Authors: J.A. González, C. Rodríguez, G. Rodríguez, F. de Sande, M. Printista
Format: Article
Language:English
Published: Hindawi Limited 2003-01-01
Series:Scientific Programming
Online Access:http://dx.doi.org/10.1155/2003/720402
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spelling doaj-174cc0825ff44aaea220c4ee7ef1a5a72021-07-02T02:10:10ZengHindawi LimitedScientific Programming1058-92441875-919X2003-01-0111319119810.1155/2003/720402A Tool for Performance Modeling of Parallel ProgramsJ.A. González0C. Rodríguez1G. Rodríguez2F. de Sande3M. Printista4Dpto. Estadística, I.O. y Computación, Universidad de La Laguna, La Laguna, 38271, SpainDpto. Estadística, I.O. y Computación, Universidad de La Laguna, La Laguna, 38271, SpainDpto. Estadística, I.O. y Computación, Universidad de La Laguna, La Laguna, 38271, SpainDpto. Estadística, I.O. y Computación, Universidad de La Laguna, La Laguna, 38271, SpainUniversidad Nacional de San Luis, Ejército de los Andes 950, San Luis, ArgentinaCurrent performance prediction analytical models try to characterize the performance behavior of actual machines through a small set of parameters. In practice, substantial deviations are observed. These differences are due to factors as memory hierarchies or network latency. A natural approach is to associate a different proportionality constant with each basic block, and analogously, to associate different latencies and bandwidths with each "communication block". Unfortunately, to use this approach implies that the evaluation of parameters must be done for each algorithm. This is a heavy task, implying experiment design, timing, statistics, pattern recognition and multi-parameter fitting algorithms. Software support is required. We present a compiler that takes as source a C program annotated with complexity formulas and produces as output an instrumented code. The trace files obtained from the execution of the resulting code are analyzed with an interactive interpreter, giving us, among other information, the values of those parameters.http://dx.doi.org/10.1155/2003/720402
collection DOAJ
language English
format Article
sources DOAJ
author J.A. González
C. Rodríguez
G. Rodríguez
F. de Sande
M. Printista
spellingShingle J.A. González
C. Rodríguez
G. Rodríguez
F. de Sande
M. Printista
A Tool for Performance Modeling of Parallel Programs
Scientific Programming
author_facet J.A. González
C. Rodríguez
G. Rodríguez
F. de Sande
M. Printista
author_sort J.A. González
title A Tool for Performance Modeling of Parallel Programs
title_short A Tool for Performance Modeling of Parallel Programs
title_full A Tool for Performance Modeling of Parallel Programs
title_fullStr A Tool for Performance Modeling of Parallel Programs
title_full_unstemmed A Tool for Performance Modeling of Parallel Programs
title_sort tool for performance modeling of parallel programs
publisher Hindawi Limited
series Scientific Programming
issn 1058-9244
1875-919X
publishDate 2003-01-01
description Current performance prediction analytical models try to characterize the performance behavior of actual machines through a small set of parameters. In practice, substantial deviations are observed. These differences are due to factors as memory hierarchies or network latency. A natural approach is to associate a different proportionality constant with each basic block, and analogously, to associate different latencies and bandwidths with each "communication block". Unfortunately, to use this approach implies that the evaluation of parameters must be done for each algorithm. This is a heavy task, implying experiment design, timing, statistics, pattern recognition and multi-parameter fitting algorithms. Software support is required. We present a compiler that takes as source a C program annotated with complexity formulas and produces as output an instrumented code. The trace files obtained from the execution of the resulting code are analyzed with an interactive interpreter, giving us, among other information, the values of those parameters.
url http://dx.doi.org/10.1155/2003/720402
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