aspg-3-2601-1791417467 20261007235747 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2024 15 1 NoorUlhuda S. Ahmed Department of Computer Science, College of science for women, University of Babylon, Babil, Iraq; College of Medicine, University of Al-Ameed, Karbala PO Box 198, Iraq Esraa H. Alwan Department of Computer Science, College of science for women, University of Babylon, Babil, Iraq Ahmed B. M. Fanfakh Department of Computer Science, College of science for women, University of Babylon, Babil, Iraq This work investigates the use of ensemble machine-learning algorithms to optimize loop-tiling in computing systems, with the goal of improving performance by predicting optimal tile sizes. It compares two approaches: independent training and averaging (soft voting) and an ensemble technique (hard voting) that employs models such as linear regression, ridge regression, and random forests. Experiments on an Intel Core i7-8565U CPU with several benchmark programs revealed that the hard voting Ensemble Approach beat the soft voting technique, providing more dependable and accurate predictions across a range of computing environments. The hard voting technique reduced execution time by around 87.5% for dynamic features and 89.89% for static features, whereas the soft voting approach showed an average drop of 75.45% for dynamic features and 78.13% for static characteristics. This work demonstrates the effectiveness of hard voting ensemble machine learning approaches in improving cache efficiency and total execution time, opening the way for future advances in high-performance computing settings. 2024 214 226 2601 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.150117 https://www.americaspg.com/journal/3/article/2601 https://www.americaspg.com/storage/11711066740.pdf