Abstract: With the continuous development of hardware and software,Graphics Processor Units(GPUs)have been used in the general-purpose computation field．They have emerged as a computational accelerator that dramatically reduces the application execution time with CPUs．To achieve high computing performance,a GPU typically includes hundreds of computing units．The high density of computing resource on a chip brings high power consumption．Therefore power consumption has become one of the most important problems for the development of GPUs．This paper analyzed the energy consumption of parallel algorithms executed in GPUs and provided a method to evaluate the energy scalability for parallel algorithms．Then the parallel prefix sum was analyzed to illustrate the method for the energy conservation,and the energy scalability was experimentally evaluated using Sparse Matrix-Vector Multiply(SpMV)．The results show that the optimal number of blocks,memory choice and task scheduling are the important keys to balance the perfor-mance and the energy consumption of GPUs.

Key words: GPU,Parallel algorithms,Energy conservation,Performance