Domain-specific energy modeling for drug discovery and magnetohydrodynamics applications

Lorenzo Carpentieri, Marco D'Antonio, Kaijie Fan, Luigi Crisci, Biagio Cosenza, Federico Ficarelli, Daniele Cesarini, Gianmarco Accordi, Davide Gadioli, Gianluca Palermo, Peter Thoman, Philip Salzmann, Philipp Gschwandtner, Markus Wippler, Filippo Marchetti, Daniele Gregori, Andrea Rosario Beccari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Over the past few years, the adoption of energy efficiency techniques in modern computer systems is becoming increasingly relevant for sustainable computing. A well-known power management software technique for energy-efficient computing is frequency scaling which modulates the device frequency to explore the energy-performance trade-off. To achieve energy savings, a frequency tuning phase is required because different applications can have different energy and runtime behaviors depending on the frequency setting. Machine learning models can be used to predict energy and runtime, and therefore optimal frequency configurations, based on static or dynamic features extracted from the target application. While general-purpose energy models can be very accurate for a wide range of applications, their accuracy can be limited by the specific input of the target application. We present an energy characterization that spans the fields of drug discovery and magnetohydrodynamics by using two real-world applications as case studies: LiGen and Cronos. Additionally, to overcome the limitations of general-purpose approaches, we define two domain-specific energy models, which enhance the general-purpose energy models by leveraging the target application’s input parameter to increase the final accuracy. Experimental results show that for both applications, domain-specific models achieve a ten times lower error compared to the general-purpose energy models.
Original languageEnglish
Title of host publicationSC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis
PublisherAssociation for Computing Machinery
ISBN (Print)9798400707858
Publication statusPublished - 12 Nov 2023
Externally publishedYes
EventSC '23: International Conference for High Performance Computing, Networking, Storage and Analysis - Denver, United States
Duration: 12 Nov 202317 Nov 2023

Publication series

NameSC-W: proceedings


ConferenceSC '23: International Conference for High Performance Computing, Networking, Storage and Analysis
Country/TerritoryUnited States


  • Heterogeneous computing
  • Frequency scaling
  • Modeling
  • Energy efficiency


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