TY - GEN
T1 - Statistical power and energy modeling of multi-GPU kernels
AU - Ghosh, Sayan
AU - Chandrasekaran, Sunita
AU - Chapman, Barbara M.
PY - 2012
Y1 - 2012
N2 - To improve the energy efficiency of parallel ap- plications on GPGPUs, a better understanding of the energy behavior of various applications is mandatory. In this study we employ statis- tical methods to model power and energy con- sumption of some common optimized high per- formance kernels (DGEMM, FFT, PRNG and FD stencils) on a multi-GPU platform.
AB - To improve the energy efficiency of parallel ap- plications on GPGPUs, a better understanding of the energy behavior of various applications is mandatory. In this study we employ statis- tical methods to model power and energy con- sumption of some common optimized high per- formance kernels (DGEMM, FFT, PRNG and FD stencils) on a multi-GPU platform.
KW - GPGPU
KW - Power
KW - Statistical Modeling
UR - https://www.scopus.com/pages/publications/84876522014
U2 - 10.1109/SC.Companion.2012.298
DO - 10.1109/SC.Companion.2012.298
M3 - Conference contribution
AN - SCOPUS:84876522014
SN - 9780769549569
T3 - Proceedings - 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012
SP - 1516
BT - Proceedings - 2012 SC Companion
T2 - 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, SCC 2012
Y2 - 10 November 2012 through 16 November 2012
ER -