The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: The 1st International Workshop on the Environmental Sustainability of High-Performance Software
Authors: Lorenzo Carpentieri, Marco D'Antonio, Kaijie Fan, Luigi Crisci, and Biagio Cosenza (University of Salerno); Federico Ficarelli and Daniele Cesarini (CINECA); Gianmarco Accordi, Davide Gadioli, and Gianluca Palermo (Polytechnic University of Milan); Peter Thoman and Philip Salzmann (University of Innsbruck); Philipp Gschwandtner (University of Innsbruck, PH3 GmbH); Markus Wippler (PH3 GmbH); Filippo Marchetti and Daniele Gregori (E4); and Andrea Rosario Beccari (Dompé Farmaceutici Spa)
Abstract: Frequency scaling is a well-known energy-saving power management technique that modulates the device frequency to explore the trade-off between energy and performance. Higher energy savings require a frequency tuning phase since different applications can have different energy and time behavior depending on the frequency setting. Machine learning models can be used to predict the optimal frequency configuration based on static or dynamic features extracted from the target application. While general-purpose energy models can be very accurate on 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. 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 accuracy.