SCHEDULE: NOV 10-16, 2012
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Statistical Power and Energy Modeling of Multi-GPU kernels
SESSION: Research Poster Reception
EVENT TYPE: Posters and Electronic Posters
TIME: 5:15PM - 7:00PM
SESSION CHAIR: Torsten Hoefler
AUTHOR(S):Sayan Ghosh, Sunita Chandrasekaran, Barbara Chapman
Current high performance computing systems consume a lot of energy. Although there have been substantial increase in computation performance, the same is not reflected in case of energy efficiency. To have an exascale computer by end of this decade, tremendous improvements in energy efficiency is mandatory. It is not possible to have sophisticated instruments to measure energy or power at such a large scale, but estimation could be useful. In this work, we have developed a statistical model using limited performance counters providing an estimation of power/energy components. The data collected range from different types of application kernel such asFFT, DGEMM, Stencils and Pseudo-Random-Number-Generators; widely used in various disciplines of high performance computing. A power analyzer has been used to analyze/extract the electrical power usage information of the multi-GPU node under inspection. An API was also written to remotely interface with the analyzer and get the instantaneous power readings.
Torsten Hoefler (Chair) - ETH Zurich
Sayan Ghosh - University of Houston
Sunita Chandrasekaran - University of Houston
Barbara Chapman - University of Houston