A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm

This paper presents a general model to define, measure and predict the efficiency of applications running on heterogeneous parallel computer systems. Using this framework, it is possible to understand the influence that the heterogeneity of the hardware has on the efficiency of an algorithm. This me...

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Autores principales: Bazterra, Víctor Eduardo, Ferraro, Marta Beatriz, Facelli, Julio César
Publicado: 2005
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Acceso en línea:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_07437315_v65_n1_p48_Bazterra
http://hdl.handle.net/20.500.12110/paper_07437315_v65_n1_p48_Bazterra
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spelling paper:paper_07437315_v65_n1_p48_Bazterra2023-06-08T15:44:47Z A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm Bazterra, Víctor Eduardo Ferraro, Marta Beatriz Facelli, Julio César Heterogeneous parallel environment Parallel genetic algorithms Performance analysis Heterogeneous parallel environment Parallel genetic algorithm Performance analysis Adaptive algorithms Genetic algorithms Mathematical models Parallel algorithms Probability Set theory Synchronization Theorem proving Parallel processing systems This paper presents a general model to define, measure and predict the efficiency of applications running on heterogeneous parallel computer systems. Using this framework, it is possible to understand the influence that the heterogeneity of the hardware has on the efficiency of an algorithm. This methodology is used to compare an existing parallel genetic algorithm with a new adaptive parallel model. All the performance measurements were taken in a loosely coupled cluster of processors. © 2004 Elsevier Inc. All rights reserved. Fil:Bazterra, V.E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Ferraro, M.B. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Facelli, J.C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2005 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_07437315_v65_n1_p48_Bazterra http://hdl.handle.net/20.500.12110/paper_07437315_v65_n1_p48_Bazterra
institution Universidad de Buenos Aires
institution_str I-28
repository_str R-134
collection Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic Heterogeneous parallel environment
Parallel genetic algorithms
Performance analysis
Heterogeneous parallel environment
Parallel genetic algorithm
Performance analysis
Adaptive algorithms
Genetic algorithms
Mathematical models
Parallel algorithms
Probability
Set theory
Synchronization
Theorem proving
Parallel processing systems
spellingShingle Heterogeneous parallel environment
Parallel genetic algorithms
Performance analysis
Heterogeneous parallel environment
Parallel genetic algorithm
Performance analysis
Adaptive algorithms
Genetic algorithms
Mathematical models
Parallel algorithms
Probability
Set theory
Synchronization
Theorem proving
Parallel processing systems
Bazterra, Víctor Eduardo
Ferraro, Marta Beatriz
Facelli, Julio César
A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
topic_facet Heterogeneous parallel environment
Parallel genetic algorithms
Performance analysis
Heterogeneous parallel environment
Parallel genetic algorithm
Performance analysis
Adaptive algorithms
Genetic algorithms
Mathematical models
Parallel algorithms
Probability
Set theory
Synchronization
Theorem proving
Parallel processing systems
description This paper presents a general model to define, measure and predict the efficiency of applications running on heterogeneous parallel computer systems. Using this framework, it is possible to understand the influence that the heterogeneity of the hardware has on the efficiency of an algorithm. This methodology is used to compare an existing parallel genetic algorithm with a new adaptive parallel model. All the performance measurements were taken in a loosely coupled cluster of processors. © 2004 Elsevier Inc. All rights reserved.
author Bazterra, Víctor Eduardo
Ferraro, Marta Beatriz
Facelli, Julio César
author_facet Bazterra, Víctor Eduardo
Ferraro, Marta Beatriz
Facelli, Julio César
author_sort Bazterra, Víctor Eduardo
title A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
title_short A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
title_full A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
title_fullStr A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
title_full_unstemmed A general framework to understand parallel performance in heterogeneous clusters: Analysis of a new adaptive parallel genetic algorithm
title_sort general framework to understand parallel performance in heterogeneous clusters: analysis of a new adaptive parallel genetic algorithm
publishDate 2005
url https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_07437315_v65_n1_p48_Bazterra
http://hdl.handle.net/20.500.12110/paper_07437315_v65_n1_p48_Bazterra
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