Towards Fine-Grained Dynamic Tuning of HPC Applications on Modern Multi-Core Architectures

Abstract

There is a consensus that exascale systems should operate within a power envelope of 20MW. Consequently, energy conservation is still considered as the most crucial constraint if such systems are to be realized. So far, most research on this topic has focused on strategies such as power capping and dynamic power management. Although these approaches can reduce power consumption, we believe that they might not be sufficient to reach the exascale energy-efficiency goals. Hence, we aim to adopt techniques from embedded systems, where energy-efficiency has always been the fundamental objective. A successful energy-saving technique used in embedded systems is to integrate fine-grained autotuning with dynamic voltage and frequency scaling. In this paper, we apply a similar technique to a real-world HPC application. Our experimental results on a HPC cluster indicate that such an approach can save up to 19% of energy compared to the baseline configuration, with negligible performance loss.