Use este identificador para citar ou linkar para este item: http://hdl.handle.net/1843/80548
Tipo: Artigo de Evento
Título: A framework for direct and transparent data exchange of filter-stream applications in multi-GPUs architectures
Autor(es): Gabriel Ramos
Guilherme Neri Andrade
Rafael Sachetto
Daniel Madeira
Renan Carvalho
Renato Antonio Celso Ferreira
Fernando Mourão
Leonardo Rocha
Resumo: The massive data generation has been pushing for significant advances in computing architectures, reflecting in heterogeneous architectures composed by different types of processing units. The filter-stream paradigm is typically used to exploit the parallel processing power of these new architectures. The efficiency of applications in this paradigm is achieved by exploring a set of interconnected computers (cluster) using filters and communication between them in a coordinated way. In this work we propose, implement and test a generic abstraction for direct and transparent data exchange of filter-stream applications in heterogeneous cluster with multi-GPU (Graphics Processing Units). This abstraction allows hiding from the programmers all the low-level implementation details related to GPU communication and the control related to the location of filters. Further, we consolidate such abstraction into a framework. Empirical assessments using a real application show that the proposed abstraction layer eases the implementation of filter-stream applications without compromising the overall application performance.
Assunto: Computação
Cluster (Sistema de computador)
Ciência da computação
Idioma: eng
País: Brasil
Editor: Universidade Federal de Minas Gerais
Sigla da Instituição: UFMG
Departamento: ICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
Tipo de Acesso: Acesso Aberto
Identificador DOI: https://doi.org/10.1016/j.procs.2017.05.144
URI: http://hdl.handle.net/1843/80548
Data do documento: 9-Jun-2017
metadata.dc.url.externa: https://www.sciencedirect.com/science/article/pii/S1877050917307160
metadata.dc.relation.ispartof: International Conference on Computational Science
Aparece nas coleções:Artigo de Evento



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