@inproceedings{10.1145/3624062.3624261,
  author    = {Carpentieri, Lorenzo and D'Antonio, Marco and Fan, Kaijie and Crisci, Luigi and Cosenza, Biagio and Ficarelli, Federico and Cesarini, Daniele and Accordi, Gianmarco and Gadioli, Davide and Palermo, Gianluca and Thoman, Peter and Salzmann, Philip and Gschwandtner, Philipp and Wippler, Markus and Marchetti, Filippo and Gregori, Daniele and Beccari, Andrea Rosario},
  title     = {Domain-Specific Energy Modeling for Drug Discovery and Magnetohydrodynamics Applications},
  year      = {2023},
  isbn      = {9798400707858},
  publisher = {Association for Computing Machinery},
  address   = {New York, NY, USA},
  url       = {https://doi.org/10.1145/3624062.3624261},
  doi       = {10.1145/3624062.3624261},
  abstract  = {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.},
  booktitle = {Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis},
  pages     = {1790–1800},
  numpages  = {11},
  keywords  = {Energy efficiency, Frequency scaling, Heterogeneous computing, Modeling},
  location  = {Denver, CO, USA},
  series    = {SC-W '23}
}