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GPUSync: A Framework for Real-Time GPU Management

Glenn A. Elliott, Bryan C. Ward, James H. Anderson
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}

}

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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.
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