The power efficiency of large-scale computing on multiprocessing systems is an important issue that interrelated to both of the hardware architectures and the software methodologies. Aiming to design power-efficient high performance program, we have measured the power consumption of large matrices multiplication on multi-core and GPU platform. Based on the obtained power characteristic values of each computing component, we abstract the energy estimations by incorporating physical power constrains from the hardware devices and analysis of the program execution behaviors. We optimize the matrices multiplication algorithm in order to improve its power performance, and the efficiency promotion has been finally validated by measuring the program execution. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Ren, D. Q., & Suda, R. (2010). Modeling and optimizing the power performance of large matrices multiplication on multi-core and GPU platform with CUDA. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6067 LNCS, pp. 421–428). https://doi.org/10.1007/978-3-642-14390-8_44
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