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Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques (2011)
Galveston, Texas USA
Oct. 10, 2011 to Oct. 14, 2011
ISSN: 1089-795X
ISBN: 978-0-7695-4566-0
pp: 218
ABSTRACT
GPUs are slowly becoming ubiquitous devices in high performance computing. Nvidia's newly released version 4.0 of the CUDA API[2] for GPU programming offers multiple ways to program on GPUs and emphasizes on Multi-GPU environments which are common in modern day compute clusters. However, despite of the subsequent progress in FLOP counts, the bane of large scale computing systems have been increased energy consumption and cooling costs. Since the energy (power X time) of a system has an obvious correlation with the user program, hence different programming techniques on GPUs could have a relation to the overall system energy consumption.
INDEX TERMS
Nvidia, CUDA 4.0, Multi-GPU, GPU, Power, Energy
CITATION
Sayan Ghosh, Barbara Chapman, "Programming Strategies for GPUs and their Power Consumption", Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques, vol. 00, no. , pp. 218, 2011, doi:10.1109/PACT.2011.51
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