To enhance the energy efficiency and performance of algorithms with Graphics Processing Unit （GPU） accelerators in source-code development, we consider the power efficiency based on data transfer bandwidth and power...To enhance the energy efficiency and performance of algorithms with Graphics Processing Unit （GPU） accelerators in source-code development, we consider the power efficiency based on data transfer bandwidth and power consumption in key situations. First, a set of primitives is abstracted from program statements. Then, data transfer bandwidth and power consumption in different granularity sizes are consid- ered and mapped into proper primitives. With these mappings, a programmer can intuitively determine the power efficiency and performance in different running states of a thread. Finally, this intuition enables the programmer to tune the algorithm in order to achieve the best energy efficiency and performance. Using these power-aware principles, two Fast Fourier Transform （FFT） methods are compared. The mapping be- tween power consumption and primitives is helpful for algorithm tuning in source-code levels.展开更多
基金Supported by the National Natural Science Foundation of China (No. 61170053)the Natural Science Foundation of Beijing (No. 4112027)the China HGJ Significant Project (No. 2009ZX01036-001-002-4)
文摘To enhance the energy efficiency and performance of algorithms with Graphics Processing Unit （GPU） accelerators in source-code development, we consider the power efficiency based on data transfer bandwidth and power consumption in key situations. First, a set of primitives is abstracted from program statements. Then, data transfer bandwidth and power consumption in different granularity sizes are consid- ered and mapped into proper primitives. With these mappings, a programmer can intuitively determine the power efficiency and performance in different running states of a thread. Finally, this intuition enables the programmer to tune the algorithm in order to achieve the best energy efficiency and performance. Using these power-aware principles, two Fast Fourier Transform （FFT） methods are compared. The mapping be- tween power consumption and primitives is helpful for algorithm tuning in source-code levels.
