GPUSync: A Framework for Real-Time GPU Management
Department of Computer Science, University of North Carolina at Chapel Hill
University of North Carolina at Chapel Hill, 2012
@article{elliott2012gpusync,
title={GPUSync: A Framework for Real-Time GPU Management},
author={Elliott, G.A. and Ward, B.C. and Anderson, J.H.},
year={2012}
}
The integration of graphics processing units (GPUs) into real-time systems has recently become an active area of research. However, prior research on this topic has failed to produce real-time GPU allocation methods that fully exploit the available parallelism in GPU-enabled systems. In this paper, a GPU management framework called GPUSync is described that enables increased parallelism while providing predictable real-time behavior. GPUSync can be applied in multi-GPU real-time systems, is cognizant of the system bus architecture, and fully exposes the parallelism offered by modern GPUs, even when closed-source GPU drivers are used. Schedulability tests presented herein that incorporate empirically measured overheads, including those due to bus contention, demonstrate that GPUSync offers real-time predictability and performs well in practice.
October 23, 2012 by hgpu
