Static Power Optimization for Homogeneous Multiple GPUs Based on Task Partition

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 145)


Recently, GPU has been widely used in High Performance Computing (HPC). In order to improve computational performance, several GPUs are integrated into one computer node in practical system. However, power consumption of GPUs is very high and becomes as bottleneck to its further development. In doing so, optimizing power consumption have been draw broad attention in the research area and industry community. In this paper, we present an energy optimization model considering performance constraint for homogeneous multi-GPUs, and propose a performance prediction model when task partitioning policy is specified. Experiment results validate that the model can accurately predict the execution of program for single or multiple GPUs, and thus reduce static power consumption by the guide of task partition.


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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.National Laboratory of Parallel and Distributed ProcessingNational University of Defense TechnologyChangshaChina

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