Programming Strategies for GPUs and their Power Consumption

Abstract

GPUs are slowly becoming ubiquitous devices in high performance computing. Nvidia's newly released version 4.0 of the CUDA API[2] for GPU programming offers multiple ways to program on GPUs and emphasizes on Multi-GPU environments which are common in modern day compute clusters. However, despite of the subsequent progress in FLOP counts, the bane of large scale computing systems have been increased energy consumption and cooling costs. Since the energy (power X time) of a system has an obvious correlation with the user program, hence different programming techniques on GPUs could have a relation to the overall system energy consumption.