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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Detalles Bibliográficos
Autores principales:	Bazterra, Víctor Eduardo, Ferraro, Marta Beatriz, Facelli, Julio César
Publicado:	2005
Materias:	Heterogeneous parallel environment Parallel genetic algorithms Performance analysis Parallel genetic algorithm Adaptive algorithms Genetic algorithms Mathematical models Parallel algorithms Probability Set theory Synchronization Theorem proving Parallel processing systems
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
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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

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