Dynamic Scheduling of Parallel Code for Heterogeneous Systems
Department of Computer Science, University of Virginia
University of Virginia, 2010
@article{brantley2010dynamic,
title={Dynamic Scheduling of Parallel Code for Heterogeneous Systems},
author={Brantley, J. and Gregg, C.},
year={2010}
}
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.
October 30, 2011 by hgpu
