Energy Analysis of Parallel Scientific Kernels on Multiple GPUs

A dramatic improvement in energy efficiency is mandatory for sustainable supercomputing and has been identified as a major challenge. Affordable energy solution continues to be of great concern in the development of the next generation of supercomputers. Low power processors, dynamic control of processor frequency and heterogeneous systems are being proposed to mitigate energy costs. However, the entire software stack must be re-examined with respect to its ability to improve efficiency in terms of energy as well as performance. In order to address this need, a better understanding of the energy behavior of applications is essential. In this paper we explore the energy efficiency of some common kernels used in high performance computing on a multi-GPU platform, and compare our results with multicore CPUs. We implement these kernels using optimized libraries like FFTW, CUBLAS and MKL. Our experiments demonstrate a relationship between energy consumption and computation-communication factors of certain application kernels. In general, we observe that the correlation of energy consumption to GPU global memory accesses is 0.73 and power consumption to operations per unit time is 0.84, signifying a strong positive relationship between them. We believe that our results will assist the HPC community in understanding the power/energy behavior of scientific kernels on multi-GPU platforms.