Um componente para exploração da capacidade de processamento de GPUs em grades computacionais

• Ano de defesa: 2010
• Autor(a) principal: Linck, Guilherme
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• dARK ID: ark:/26339/0013000018080
• Idioma: por
• Instituição de defesa: Universidade Federal de Santa Maria; BR; Ciência da Computação; UFSM; Programa de Pós-Graduação em Informática
• Palavras-chave em Português: GPGPU; Grid computing; Grid resources optimization; Computação em grade; Otimização dos recursos da grade; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.ufsm.br/handle/1/5369

Computer grids emerged in the 90 s with the goal of using geographically dispersed computers for high performance computing. Through grids, computational power of a supercomputer can be reached in a simple, efficient and inexpensive way. Such benefits led to highlights in researchs of computer grids. Recently, appeared on market graphics adapter cards whose computational power overcomes, and by a wide margin, even the most modern processors commonly used. This led to researchs that resulted in programming techniques relatively easy to learn and did simplify application programming for these processors. These techniques effectively introduced the processors in the business of high performance computing. The use of these techniques gave rise to General Purpose computing on Graphic Processing Units (GPGPU). Grids applications are generally programmed through a grid computing framework. TUXUR is one of those frameworks and is under development by Master s Program Graduate at the Federal University of Santa Maria. This dissertation discusses the development of a TUXUR s foreseen feature. Such feature allows the computer grid managed by TUXUR to enjoy the benefits of GPGPU applications, particularly regarding to the best use of the nodes s hardware that comprises it. The immediate impact of this synergy is the significant increase in grid computational capacity without adding new computers. The findings of the evaluation highlights the importance of using GPGPU tasks that take advantage of this programming technique, even when performed in a grid.