EfiMon: A Process Analyser for Granular Power Consumption Prediction
<|reference_start|>EfiMon: A Process Analyser for Granular Power Consumption Prediction: High-performance computing (HPC) and supercomputing are critical in Artificial Intelligence (AI) research, development, and deployment. The extensive use of supercomputers for training complex AI models, which can take from days to months, raises significant concerns about energy consumption and carbon emissions. Traditional methods for estimating the energy consumption of HPC workloads rely on metering reports from computing nodes power supply units, assuming exclusive use of the entire node. This assumption is increasingly untenable with the advent of next-generation supercomputers that share resources to accelerate workloads, as seen in initiatives like Acceleration as a Service (XaaS) and cloud computing.   This paper introduces EfiMon, an agnostic and non-invasive tool designed to extract detailed information about process execution, including instructions executed within specific time windows and CPU and RAM usage. Additionally, it captures comprehensive system metrics, such as power consumption reported by CPU sockets and PSUs. This data enables the development of prediction models to estimate the energy consumption of individual processes without requiring isolation.   Using a regression-based mathematical model, our tool is able to estimate single processes' power consumption in isolated and shared resource environments. In shared scenarios, the model demonstrates robust performance, deviating by a maximum of 2.2% on Intel-based machines and 4.4% on AMD systems compared to non-shared cases. This significant accuracy showcases EfiMon's potential for enhancing energy accounting in supercomputing, contributing to more efficient and energy-aware optimisation strategies in HPC.<|reference_end|>
@article{león-vega2024efimon:,
    title={EfiMon: A Process Analyser for Granular Power Consumption Prediction},
    author={Luis G. Le'on-Vega, Niccol`o Tosato, Stefano Cozzini},
    journal={arXiv preprint arXiv:2409.17368},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17368},
    primaryClass={cs.DC cs.PF}
}