Dynamic Scheduling of Parallel Code for Heterogeneous Systems

Jeff Brantley, Chris Gregg
Department of Computer Science, University of Virginia
University of Virginia, 2010


   title={Dynamic Scheduling of Parallel Code for Heterogeneous Systems},

   author={Brantley, J. and Gregg, C.},



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A typical consumer desktop computer has a multi-core CPU with at least two and possibly up to eight processing elements over four processors, and a multi-core GPU with up to 512 processing elements. Both the CPU and the GPU are capable of running parallel code, and this project demonstrates a method for dynamically deciding whether to run a given parallel workload on the CPU or the GPU depending on the state of the system when the code is launched. To achieve this, we tested a selection of parallel OpenCL code on a multi-core CPU and a multi-core GPU, as part of a larger program that runs on the CPU. When the parallel code is launched, the runtime makes a dynamic decision about which processor to run the code on, given system state and historical data. We demonstrate a method for using meta-data available to the runtime and historical data from code profiling to make the dynamic decision. We also discuss the limitations inherenet in attempting to make dynamic predictions.
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