Detalhes bibliográficos
| Ano de defesa: |
2015 |
| Autor(a) principal: |
Adornes, Daniel Couto
 |
| Orientador(a): |
Fernandes, Luiz Gustavo Leão
|
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
eng |
| Instituição de defesa: |
Pontifícia Universidade Católica do Rio Grande do Sul
|
| Programa de Pós-Graduação: |
Programa de Pós-Graduação em Ciência da Computação
|
| Departamento: |
Faculdade de Informática
|
| País: |
Brasil
|
| Palavras-chave em Português: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://tede2.pucrs.br/tede2/handle/tede/6782
|
Resumo: |
In order to improve performance, simplicity and scalability of large datasets processing, Google proposed the MapReduce parallel pattern. This pattern has been implemented in several ways for different architectural levels, achieving significant results for high performance computing. However, developing optimized code with those solutions requires specialized knowledge in each framework’s interface and programming language. Recently, the DSL-POPP was proposed as a framework with a high-level language for patternsoriented parallel programming, aimed at abstracting complexities of parallel and distributed code. Inspired on DSL-POPP, this work proposes the implementation of a unified MapReduce programming interface with rules for code transformation to optimized solutions for shared-memory multi-core and distributed architectures. The evaluation demonstrates that the proposed interface is able to avoid performance losses, while also achieving a code and a development cost reduction from 41.84% to 96.48%. Moreover, the construction of the code generator, the compatibility with other MapReduce solutions and the extension of DSL-POPP with the MapReduce pattern are proposed as future work. |
