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Computers 2013, 2(4), 176-214; doi:10.3390/computers2040176

Exploring Graphics Processing Unit (GPU) Resource Sharing Efficiency for High Performance Computing

NSF Center for High-Performance Reconfigurable Computing (CHREC), Department of Electrical and Computer Engineering, The George Washington University, 801 22nd Street NW, Washington, DC, 20052, USA
Author to whom correspondence should be addressed.
Received: 20 September 2013 / Revised: 23 October 2013 / Accepted: 31 October 2013 / Published: 19 November 2013


The increasing incorporation of Graphics Processing Units (GPUs) as accelerators has been one of the forefront High Performance Computing (HPC) trends and provides unprecedented performance; however, the prevalent adoption of the Single-Program Multiple-Data (SPMD) programming model brings with it challenges of resource underutilization. In other words, under SPMD, every CPU needs GPU capability available to it. However, since CPUs generally outnumber GPUs, the asymmetric resource distribution gives rise to overall computing resource underutilization. In this paper, we propose to efficiently share the GPU under SPMD and formally define a series of GPU sharing scenarios. We provide performance-modeling analysis for each sharing scenario with accurate experimentation validation. With the modeling basis, we further conduct experimental studies to explore potential GPU sharing efficiency improvements from multiple perspectives. Both further theoretical and experimental GPU sharing performance analysis and results are presented. Our results not only demonstrate the significant performance gain for SPMD programs with the proposed efficient GPU sharing, but also the further improved sharing efficiency with the optimization techniques based on our accurate modeling. View Full-Text
Keywords: GPU; resource sharing; SPMD; performance modeling; high performance computing GPU; resource sharing; SPMD; performance modeling; high performance computing

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Li, T.; Narayana, V.K.; El-Ghazawi, T. Exploring Graphics Processing Unit (GPU) Resource Sharing Efficiency for High Performance Computing. Computers 2013, 2, 176-214.

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