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...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2005 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/73283 |
| Acceso en línea: | http://hdl.handle.net/11336/73283 |
| Access Level: | acceso abierto |
| Palabra clave: | HETEROGENEOUS PARALLEL ENVIRONMENT PARALLEL GENETIC ALGORITHMS PERFORMANCE ANALYSIS https://purl.org/becyt/ford/1.3 https://purl.org/becyt/ford/1 |
| Sumario: | 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. |
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