OpenCL Task Partitioning in the Presence of GPU Contention

Dominik Grewe, Zheng Wang, and Michael F.P. O’Boyle
School of Informatics, The University of Edinburgh, UK
The 26th International Workshop on Languages and Compilers for Parallel Computing, 2013


   title={OpenCL Task Partitioning in the Presence of GPU Contention},

   author={Grewe, Dominik and Wang, Zheng and O’Boyle, Michael FP},



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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 hetergeneous 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-theart dynamic approaches our model achieves speed-ups of 1.54 and 2.56 respectively.
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