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<p>In the pursuit of sustainable computational approaches, research on parallel computing has been progressively orienting efforts towards the definition of energy-aware methodologies to solve real-world problems. A commonly adopted approach in this context lies in the adjustment of operating frequencies to seek power-time tradeoffs. However, the characteristics of the applications impose restrictions on the benefits that can be achieved through this technique, thus demanding the design of accurate algorithmic strategies to enhance energy efficiency. This work explores the combination of frequency scaling and multi-threaded vector-based algorithms to improve energy consumption in an important problem from the evolutionary biology domain: phylogenetic inference. Different technologies, such as AVX512, AVX2, and GPU vector types, are investigated to define refined parallel designs based on OpenMP, for CPUs, and CUDA, for GPUs. Experimental results on five biological datasets give account of energy improvements of up to 20<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\times\)</EquationSource> </InlineEquation> when tackling complex problem instances on CPUs, while also observing significant benefits on different GPU processing scenarios. Consequently, the proposed strategies effectively facilitate the definition of energy-aware execution environments for phylogenetics, improving other literature approaches in terms of time and energy consumption.</p>

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Multi-threaded SIMD strategies and frequency scaling for sustainable evolutionary biology

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月17日

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  • Sergio Santander-Jiménez,
  • Miguel A. Vega-Rodríguez

摘要

In the pursuit of sustainable computational approaches, research on parallel computing has been progressively orienting efforts towards the definition of energy-aware methodologies to solve real-world problems. A commonly adopted approach in this context lies in the adjustment of operating frequencies to seek power-time tradeoffs. However, the characteristics of the applications impose restrictions on the benefits that can be achieved through this technique, thus demanding the design of accurate algorithmic strategies to enhance energy efficiency. This work explores the combination of frequency scaling and multi-threaded vector-based algorithms to improve energy consumption in an important problem from the evolutionary biology domain: phylogenetic inference. Different technologies, such as AVX512, AVX2, and GPU vector types, are investigated to define refined parallel designs based on OpenMP, for CPUs, and CUDA, for GPUs. Experimental results on five biological datasets give account of energy improvements of up to 20 \(\times\) when tackling complex problem instances on CPUs, while also observing significant benefits on different GPU processing scenarios. Consequently, the proposed strategies effectively facilitate the definition of energy-aware execution environments for phylogenetics, improving other literature approaches in terms of time and energy consumption.