Detalhes bibliográficos
| Ano de defesa: |
2013 |
| Autor(a) principal: |
Silva, Samuel Reghim |
| Orientador(a): |
Guardia, Hélio Crestana
 |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal de São Carlos
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Ciência da Computação - PPGCC
|
| Departamento: |
Não Informado pela instituição
|
| País: |
BR
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
https://repositorio.ufscar.br/handle/20.500.14289/532
|
Resumo: |
Multicore processors allow applications to explore thread-level parallelism in order to enable improvements on the elapsed time. The sharing of the memory subsystem and the discrepancy between the speeds of processors and memory access operations, however, may entail limitations to the scalability caused by thread competition for the resources. The automatic determination of the appropriate number of threads for an application that ensure efficient executions, although widely desired, is a non-trivial problem. This work aimed to evaluate the factors limiting the scalability of OpenMP parallel applications related to the contention for shared resources in multicore processors, with the goal of identifying the characteristics of applications that limit their scalability. It was found that memory accesses are a major limitation to the performance gains with parallelism. The granularity, indicating the ratio of memory accesses to processing, has been verified as being an important performance factor of parallel executions. Estimates of granularity can be obtained from the applications' source code. Different data access modes, however, point to the need to estimate the combination of granularity with information about the data access locality to properly determine the scalability of applications. |
