Techniques and tools for measuring energy efficiency of scientific software applications

TL;DR

This paper presents measurement techniques and tools for assessing the energy efficiency of scientific software applications, comparing ARM and Intel architectures through physical and software-based workload analysis.

Contribution

It introduces new measurement methods and profiling tools to accurately evaluate power consumption of scientific workloads on different architectures.

Findings

ARM and Intel architectures show different energy-performance trade-offs.

Profiling tools help identify energy bottlenecks in scientific applications.

Measurement techniques enable better optimization for energy efficiency.

Abstract

The scale of scientific High Performance Computing (HPC) and High Throughput Computing (HTC) has increased significantly in recent years, and is becoming sensitive to total energy use and cost. Energy-efficiency has thus become an important concern in scientific fields such as High Energy Physics (HEP). There has been a growing interest in utilizing alternate architectures, such as low power ARM processors, to replace traditional Intel x86 architectures. Nevertheless, even though such solutions have been successfully used in mobile applications with low I/O and memory demands, it is unclear if they are suitable and more energy-efficient in the scientific computing environment. Furthermore, there is a lack of tools and experience to derive and compare power consumption between the architectures for various workloads, and eventually to support software optimizations for energy efficiency. To that end, we have performed several physical and software-based measurements of workloads from HEP applications running on ARM and Intel architectures, and compare their power consumption and performance. We leverage several profiling tools (both in hardware and software) to extract different characteristics of the power use. We report the results of these measurements and the experience gained in developing a set of measurement techniques and profiling tools to accurately assess the power consumption for scientific workloads.