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Towards Efficient GPU Sharing on Multicore Processors

Lingyuan Wang, Miaoqing Huang, Tarek El-Ghazawi
ECE Department, George Washington University
The 2nd International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computing Systems (PMBS11), 2011

@article{wang40towards,

   title={Towards Efficient GPU Sharing on Multicore Processors},

   author={Wang, L. and Huang, M. and El-Ghazawi, T.},

   journal={SIGMETRICS Performance Evaluation Review},

   volume={40},

   number={2},

   year={2011}

}

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Scalable systems employing a mix of GPUs with CPUs are becoming increasingly prevalent in high-performance computing (HPC). The presence of such accelerators introduces significant challenges and complexities to both language developers and end users. This paper provides a close study of efficient coordination mechanisms to handle parallel requests from multiple hosts of control to a GPU under hybrid programming. Using a set of microbenchmarks and applications on a GPU cluster, we show that thread- and process-based context hosting have different tradeoffs. Experimental results on application benchmarks suggest that both thread-based context funneling and process-based context switching natively perform similarly on the latest Fermi GPU, while manually guided context funneling is currently the best way to achieve optimal performance.
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