Energy–Performance Trade-Offs of LU Matrix Decomposition in Java Across Heterogeneous Hardware and Operating Systems.
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| Název: | Energy–Performance Trade-Offs of LU Matrix Decomposition in Java Across Heterogeneous Hardware and Operating Systems. |
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| Autoři: | Rosa, Francisco J., de la Torre, Juan Carlos, Aragón-Jurado, José M., Valderas-González, Alberto, Dorronsoro, Bernabé |
| Zdroj: | Applied Sciences (2076-3417); Jan2026, Vol. 16 Issue 2, p1002, 26p |
| Témata: | ENERGY consumption, HETEROGENEOUS computing, MATHEMATICAL optimization, PARALLEL algorithms, MECHANICAL efficiency, JAVA programming language, MATRIX decomposition, COMPUTER operating systems |
| Abstrakt: | 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. [ABSTRACT FROM AUTHOR] |
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| Databáze: | Complementary Index |
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