Energy cost evaluation of parallel algorithms for multiprocessor systems

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 in high power consumption. Therefore power consumption has become one of the most important problems for the development of GPUs. This paper analyzes the energy consumption of parallel algorithms executed in GPUs and provides a method to evaluate the energy scalability for parallel algorithms. Then the parallel prefix sum is analyzed to illustrate the method for the energy conservation, and the energy scalability is 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 performance and the energy consumption of GPUs.