Predicting the performance of a parallel heuristic solution for the Steiner Tree Problem
Nowadays, there is an increasing number of computer intensive applications, which exceed the capacity of a standard stand-alone computer. An alternative is to parallelize the application and run it in a cluster; there has been much work in this sense, specially in platforms and tools to build a clus...
| Autores: | , |
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| Tipo de documento: | relatório científico |
| Estado: | Versão publicada |
| Data de publicação: | 2003 |
| País: | Uruguay |
| Recursos: | Universidad de la República |
| Repositório: | COLIBRI |
| OAI Identifier: | oai:colibri.udelar.edu.uy:20.500.12008/3489 |
| Acesso em linha: | http://hdl.handle.net/20.500.12008/3489 |
| Access Level: | Acceso aberto |
| Palavra-chave: | PERFORMANCE ESTIMATION PARALLEL PETRI NET MODELS STEINER TREE COMBINATORIAL OPTIMIZATION |
| Resumo: | Nowadays, there is an increasing number of computer intensive applications, which exceed the capacity of a standard stand-alone computer. An alternative is to parallelize the application and run it in a cluster; there has been much work in this sense, specially in platforms and tools to build a cluster from commodity components, and to develop parallel applications. One of the problems that subsist is the one faced by the analyst when designing a new application in this environment. He must solve the trade-off between the cost of building the cluster, and the application's running time; if he under-dimensions the cluster, the running time might be too long; if he over-dimensions it, the cost might not be acceptable. This work presents an example of how analytical performance models can be applied in this context. In particular, we develop a parallel implementation of a combinatorial optimization heuristic for solving the Steiner Tree Problem, and a Petri net model which can be used to predict the running time of the application on a cluster of PCs, on the basis of measurements on stand-alone equipment. The model is validated experimentally, showing that it adequately predicts optimistic and pessimistic bounds for the measured running time. |
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