Investigation of Energy and Power Characteristics of Various Matrix Multiplication Algorithms

Salem Alsari () and Muhammad Al-Hashimi
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Salem Alsari: Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 25732, Saudi Arabia
Muhammad Al-Hashimi: Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 25732, Saudi Arabia

Energies, 2024, vol. 17, issue 9, 1-22

Abstract: This work studied the energy behavior of six matrix multiplication algorithms with various physical asset usage patterns. Two were variants of the straight inner product of rows and columns. The rest were variants of Strassen’s divide-and-conquer. Cases varied in ways that were expected to affect energy behavior. The study collected data for square matrix dimensions up to 4000. The research used reliable on-chip integrated voltage regulators embedded in a recent HPC-class AMD CPU for power measurements. Inner product methods used much less energy than the others for small to moderately large matrices. The advantage diminished for sufficiently large dimensions. The power draw of the inner product methods was less for small dimensions. After a point, the power advantage shifted significantly in favor of the divide-and-conquer group (average of 24% better), with the more block-optimized versions showing increased power efficiency (at least 8.3% better than the base method). The study explored the interplay between algorithm design, power efficiency, and computational resources. It aims to help advance the cause of power efficiency in HPC and other scenarios that rely on this vital computation.

Keywords: matrix multiplication; power efficiency; AMD EPYC; memory-optimized Strassen multiplication; power-aware algorithm; green HPC (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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