Unified performance and power modeling of scientific workloads

Abstract

It is expected that scientific applications executing on future large-scale HPC must be optimized not only in terms of performance, but also in terms of power consumption. As power and energy become increasingly constrained resources, researchers and developers must have access to tools that will allow for accurate prediction of both performance and power consumption. Reasoning about performance and power consumption in concert will be critical for achieving maximum utilization of limited resources on future HPC systems. To this end, we present a unified performance and power model for the Nek-Bone mini-application developed as part of the DOE's CESAR Exascale Co-Design Center. Our models consider the impact of computation, point-to-point communication, and collective communication individually and quantitatively predict their impact on both performance and energy efficiency. Further, these models are demonstrated to be accurate on currently available HPC system architectures. In this paper, we present our modeling methodology and performance and power models for the Nek-Bone mini-application. We present validation results that indicate the accuracy of these models.