Optimizing performance and energy of HPC applications on POWER7

Abstract

Power consumption is a critical consideration in high performance computing systems and it is becoming the limiting factor to build and operate Petascale and Exascale systems. When studying the power consumption of existing systems running HPC workloads, we find power, energy and performance are closely related leading to the possibility to optimize energy without sacrificing (much or at all) performance.

This paper presents the power features of the POWER7 and shows how innovative software can use these features to optimize the power and energy consumptions of large cluster running HPC workloads.

This paper starts by presenting the new features which have been introduced in POWER7 to manage power consumption and the tools available to manage and record the power consumption. We then analyze the power consumption and performance of different HPC workloads at various levels of the POWER7 server (processor, memory, io) for different frequencies. We propose a model to predict both the power and energy consumption of real workloads based on their performance characteristics measured by hardware performance counters (HPM). We show that the power estimation model can achieve less than 5% error versus actual measurements. In conclusion, we present how an innovative scheduler can help to optimize both power and energy consumptions of large HPC clusters.