Abstract
Estimating and analyzing the power consuming features of a program on a hardware platform is important for energy aware High Performance Computing (HPC) optimization, it can help to handle critical design constraints at the level of software, chose preferable algorithm in order to reach the best energy performance. Optimizing the power efficiency of CUDA program on GPU and multicore processing element is a problem in combinatorial optimization because of the complexity of power factors and criteria. A four-tuple global optimization model has been created to indicate the procedure to find optimal energy solution. In addition, an experimental method is illustrated to examine SIMD computing for capturing power parameters, five individual energy optimization methods are provided and implemented. The optimization results have been validated by comparative analysis on real systems.
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Ren, DQ., Suda, R. Global optimization model on power efficiency of GPU and multicore processing element for SIMD computing with CUDA. Comput Sci Res Dev 27, 319–327 (2012). https://doi.org/10.1007/s00450-011-0197-6
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DOI: https://doi.org/10.1007/s00450-011-0197-6