Quantifying the impact of GPUs on performance and energy efficiency in HPC clusters

Authors:
Jeremy Enos;Craig Steffen;Joshi Fullop;Michael Showerman;Guochun Shi;Kenneth Esler;Volodymyr Kindratenko;John E. Stone;James C. Phillips
Affiliations:
National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, USA;National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, USA;National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, USA;National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, USA;National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, USA;National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, USA;National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, USA;Theoretical and Computational Biophysics Group, Beckman Institute, University of Illinois at Urbana-Champaign, USA;Theoretical and Computational Biophysics Group, Beckman Institute, University of Illinois at Urbana-Champaign, USA
Venue:
GREENCOMP '10 Proceedings of the International Conference on Green Computing
Year:
2010

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Abstract

We present an inexpensive hardware system for monitoring power usage of individual CPU hosts and externally attached GPUs in HPC clusters and the software stack for integrating the power usage data streamed in real-time by the power monitoring hardware with the cluster management software tools. We introduce a measure for quantifying the overall improvement in performance-per-watt for applications that have been ported to work on the GPUs. We use the developed hardware/software infrastructure to demonstrate the overall improvement in performance-per-watt for several HPC applications implemented to work on GPUs.