Author ORCID Identifier
0000-0001-9218-3307
Document Type
Conference Paper
Disciplines
Statistics
Abstract
The growing reliance on machine learning algorithms across domains such as healthcare, transportation, and finance has led to their increased deployment on high-performance computing platforms. While performance optimization remains a central concern, energy efficiency is emerging as a critical design consideration, particularly in light of global sustainability goals. This study presents a comparative analysis of the energy consumption and performance of serial and parallel implementations of four machine learning algorithms, K-means clustering, Ant Colony Optimization, Logistic Regression, and Random Search. Experiments were conducted on an HPC testbed using both hardware-based and software-based power meters to measure energy consumption. The results demonstrate that parallel implementations not only achieve substantial reductions in execution time but also lead to lower overall energy consumption compared to their serial counterparts.
DOI
https://doi.org/10.21427/tbe1-wz03
Recommended Citation
Asgher, Urooj and Malik, Tania, "Benchmarking Energy and Performance of Parallel Machine Learning Models Using Hardware and Software Power Meters" (2025). SAML-25 Workshop on Statistical and Machine Learning. 6.
https://arrow.tudublin.ie/saml/6
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Publication Details
Statistical and Machine Learning: Methods and Applications (SAML-25) on June 5th and 6th, 2025 at TU Dublin, Ireland. doi:10.21427/tbe1-wz03