Supporting Energy-Efficient Computing on Heterogeneous CPU-GPU Architectures
Kyle Siehl, Xinghui Zhao · 2017
Modern high performance computing and cloud computing infrastructures often leverage Graphic Processing Units (GPUs) to provide accelerated, massively parallel computational power. This performance gain, however, may also introduce higher energy consumption. The energy challenge has become more and more pronounced when the system scales. To address this challenge, we propose Archon, a framework for supporting energy-efficient computing on CPU-GPU heterogeneous architectures. Specifically, Archon takes user's programs as input, automatically distribute the workload between CPU and GPU, and dynamically tunes the distribution ratio at runtime for an energy-efficient execution. Experiments have been carried out to evaluate the effectiveness of Archon, and the results show that it can achieve considerable energy savings at runtime, without significant efforts from the programmers.