Understanding the Energy Consumption of HPC Scale Artificial Intelligence

TL;DR

This paper introduces Benchmark-Tracker, a tool for measuring energy consumption and speed of deep learning algorithms in HPC environments, aiding developers in optimizing energy efficiency.

Contribution

The paper presents a novel benchmark tool that measures energy and speed of DL algorithms in HPC, enabling better understanding and optimization of energy consumption.

Findings

Benchmark-Tracker effectively measures energy and speed of DL algorithms.

Experimental results demonstrate its utility in HPC energy analysis.

The tool helps optimize HPC infrastructure for energy efficiency.

Abstract

This paper contributes towards better understanding the energy consumption trade-offs of HPC scale Artificial Intelligence (AI), and more specifically Deep Learning (DL) algorithms. For this task we developed benchmark-tracker, a benchmark tool to evaluate the speed and energy consumption of DL algorithms in HPC environments. We exploited hardware counters and Python libraries to collect energy information through software, which enabled us to instrument a known AI benchmark tool, and to evaluate the energy consumption of numerous DL algorithms and models. Through an experimental campaign, we show a case example of the potential of benchmark-tracker to measure the computing speed and the energy consumption for training and inference DL algorithms, and also the potential of Benchmark-Tracker to help better understanding the energy behavior of DL algorithms in HPC platforms. This work is a step forward to better understand the energy consumption of Deep Learning in HPC, and it also contributes with a new tool to help HPC DL developers to better balance the HPC infrastructure in terms of speed and energy consumption.