Energy efficiency of a GPU-based computing system for High Energy Physics experiments

TL;DR

This paper introduces energy efficiency as a new metric for evaluating GPU hardware and algorithms in High Energy Physics experiments, specifically applied to the LHCb experiment's trigger system.

Contribution

It develops a method to compute energy efficiency for GPU-based HEP systems, enabling sustainable computing decisions across experiments.

Findings

Energy efficiency metric relates throughput to GPU specifications.

Model applied to LHCb's high level trigger system.

Framework can be extended to other HEP experiments.

Abstract

In this paper we introduce the energy efficiency as a new metric for evaluating both hardware platforms based on Graphic Processor Units (GPU), and algorithm optimisations at High Energy Physics (HEP) experiments. We develop a method to compute the energy efficiency for the case of the first high level trigger (HLT1) of the LHCb experiment, relating the throughput with GPU specifications such as the number of cores, clock frequency, memory bandwidth and thermal design power. The model can be extended to other HEP experiments to make decisions and reach sustainable computing ecosystems.