Title: An optimised dataflow engine for GPGPU stream processing

Authors: Marcos Paulo Rocha; Felipe M.G. França; Alexandre Solon Nery; Leandro S. Guedes

Addresses: Engenharia de Sistemas e Ciência da Computação, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil ' Engenharia de Sistemas e Ciência da Computação, Universidade Federal do Rio de Janeiro, Rio de Janeiro, RJ, Brazil ' Departamento de Engenharia Elétrica, Universidade de Brasília, Brasília, DF, Brazil ' Departamento de Informática, Instituto Federal de Educação, Ciência e Tecnologia de Mato Grosso do Sul, Corumbá, MS, Brazil

Abstract: Stream processing applications have high-demanding performance requirements that are hard to tackle using traditional parallel models on modern many-core architectures, such as GPUs. On the other hand, recent dataflow computing models can naturally expose and facilitate the parallelism exploitation for a wide class of applications. Thus, instead of following the program order, different operations can be run in parallel as soon as their input operands become available. This work presents an extension to an existing dataflow library for Java. The library extension implements high-level constructs with multiple command queues to enable the superposition of memory operations and kernel executions on GPUs. Experimental results show that significant speedup can be achieved for a subset of well-known stream processing applications: Volume Ray-Casting, Path-Tracing and Sobel Filter. Moreover, new contributions in respect to concurrency analysis and the Stream processing parallel model in dataflow are presented.

Keywords: dataflow; heterogeneous systems; high-performance computing.

DOI: 10.1504/IJGUC.2019.099689

International Journal of Grid and Utility Computing, 2019 Vol.10 No.3, pp.248 - 257

Received: 13 Mar 2018
Accepted: 14 Sep 2018
Published online: 20 May 2019 *

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