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This paper investigates the time and energy consumption of LU (with MKL library) and WZ multithreaded matrix factorization algorithms on Intel and AMD processors, utilizing OneAPI and Clang compilers. The study evaluates how processor architecture and compiler optimizations impact time and energy use during matrix factorization. We describe the experimental setup, including hardware specifications, software configurations, and methods for collecting time and energy metrics. Both algorithms are tested under various conditions to assess their suitability for energy-efficient high-performance computing. The results show variations in execution times and energy consumption based on the processor and compiler used. For LU factorization on Intel Xeon processors, Intel OneAPI optimizations prove most effective, while for WZ factorization on AMD EPYC processors, the Clang compiler demonstrates better performance. Choosing the right compiler options can reduce time and energy consumption by up to 6.5 \(\%\) .

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Time and Energy Consumption of Multithreaded Matrix Factorization Using Various Compilers Optimizations

  • 会议论文

  • 发表日期：

    2025年

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  • Beata Bylina,
  • Monika Piekarz,
  • Jarosław Bylina

摘要

This paper investigates the time and energy consumption of LU (with MKL library) and WZ multithreaded matrix factorization algorithms on Intel and AMD processors, utilizing OneAPI and Clang compilers. The study evaluates how processor architecture and compiler optimizations impact time and energy use during matrix factorization. We describe the experimental setup, including hardware specifications, software configurations, and methods for collecting time and energy metrics. Both algorithms are tested under various conditions to assess their suitability for energy-efficient high-performance computing. The results show variations in execution times and energy consumption based on the processor and compiler used. For LU factorization on Intel Xeon processors, Intel OneAPI optimizations prove most effective, while for WZ factorization on AMD EPYC processors, the Clang compiler demonstrates better performance. Choosing the right compiler options can reduce time and energy consumption by up to 6.5 \(\%\) .