Energy–Performance Trade-Offs of LU Matrix Decomposition in Java Across Heterogeneous Hardware and Operating Systems

The increasing core counts and architectural heterogeneity of modern processors make performance optimization insufficient if energy consumption is not simultaneously considered. By providing a novel characterization of how the interaction between hybrid architectures and system software disrupts the traditional correlation between execution speed and energy efficiency, this research study analyzes the performance–energy trade-offs of parallel LU matrix decomposition algorithms implemented in Java, focusing on the Crout and Doolittle variants. This study is conducted on four different platforms, including ARM-based, Hybrid x86, and many-core accelerators. Execution time and speedup are evaluated for varying thread counts, while energy consumption is measured externally to capture whole-system energy usage. Experimental results show that the configuration yielding the maximum speedup does not necessarily minimize energy consumption. While x86 systems showed energy savings exceeding 80% under optimal parallel configurations, the ARM-based platform required distinct thread counts to minimize energy consumption compared with maximizing speed. These findings demonstrate that energy-efficient configurations represent a distinct optimization space that often contradicts traditional performance metrics. In the era of hybrid computing, green software optimization must transition from a simplistic “race-to-sleep” paradigm toward sophisticated, architecture-aware strategies that account for the specific power profiles of heterogeneous cores to achieve truly sustainable high-performance computing.