A Comprehensive Analysis of Process Energy Consumption on Multi-Socket Systems with GPUs

TL;DR

This paper introduces two mathematical models to accurately estimate energy consumption of CPU and GPU processes in multi-socket HPC systems, addressing challenges in shared environments like cloud computing.

Contribution

The work presents novel models for process-level energy estimation in HPC systems that do not require process isolation, improving accuracy and applicability in shared environments.

Findings

CPU power prediction error of 1.9%

GPU power prediction with 9.7% relative error

Models enable energy accounting without process isolation

Abstract

Robustly estimating energy consumption in High-Performance Computing (HPC) is essential for assessing the energy footprint of modern workloads, particularly in fields such as Artificial Intelligence (AI) research, development, and deployment. The extensive use of supercomputers for AI training has heightened concerns about energy consumption and carbon emissions. Existing energy estimation tools often assume exclusive use of computing nodes, a premise that becomes problematic with the advent of supercomputers integrating microservices, as seen in initiatives like Acceleration as a Service (XaaS) and cloud computing. This work investigates the impact of executed instructions on overall power consumption, providing insights into the comprehensive behaviour of HPC systems. We introduce two novel mathematical models to estimate a process's energy consumption based on the total node energy, process usage, and a normalised vector of the probability distribution of instruction types for CPU and GPU processes. Our approach enables energy accounting for specific processes without the need for isolation. Our models demonstrate high accuracy, predicting CPU power consumption with a mere 1.9% error. For GPU predictions, the models achieve a central relative error of 9.7%, showing a clear tendency to fit the test data accurately. These results pave the way for new tools to measure and account for energy consumption in shared supercomputing environments.