Uncovering input-sensitive energy bottlenecks in oversubscribed GPU workloads

Abstract

Graph algorithms are at the core of data-intensive applications in many computational domains. Although accelerator-based implementations have the potential for delivering improved energy efficiency on today’s large-scale graph analytics, there are many challenges to reaching the desired goals. This paper examines the issues of GPU oversubscription and input sensitivity in energy efficient graph processing. We introduce the notion of energy signatures, a collection of runtime events that can determine fluctuations in power consumption as a function of input graph characteristics. The study shows that variations in volume, density, diameter and dispersion in graphs can alter the runtime behavior of GPU applications and these changes in program behavior in turn impact the energy efficiency of the application. Based on the insight gained from our study, we develop three input-aware optimization techniques to mitigate energy bottlenecks. On experiments conducted on a Unified Memory supported GPU, the optimizations yield a 11.97% energy savings on average on Breadth-First Search and Single-Source Shortest Path algorithms.