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OpenCL task partitioning in the presence of GPU contention

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Publication date2014
Host publicationLanguages and compilers for parallel computing: 26th International Workshop, LCPC 2013, San Jose, CA, USA, September 25--27, 2013. Revised Selected Papers
EditorsCălin Cașcaval, Pablo Montesinos
Number of pages15
ISBN (Electronic)9783319099675
ISBN (Print)9783319099668
Original languageEnglish

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Heterogeneous multi- and many-core systems are increasingly prevalent in the desktop and mobile domains. On these systems it is common for programs to compete with co-running programs for resources. While multi-task scheduling for CPUs is a well-studied area, how to partitioning and map computing tasks onto the heterogeneous system in the presence of GPU contention (i.e. multiple programs compete for the GPU) remains an outstanding problem.
In this paper we consider the problem of partitioning OpenCL kernels on a CPU-GPU based system in the presence of contention on the GPU. We propose a machine learning-based approach that predicts the optimal partitioning of OpenCL kernels, explicitly taking GPU contention into account. Our predictive model achieves a speed-up of 1.92 over a scheme that always uses the GPU. When compared to two state-of-the-art dynamic approaches our model achieves speed-ups of 1.54 and 2.56 respectively.