Citation: Lastovetsky, A.; Manumachu, R.R. Energy-Efficient Parallel Computing: Challenges to Scaling. Information 2023, 14, 248. https://doi.org/10.3390/ info14040248 Academic Editor: Lenore Mullin Received: 13 February 2023 Revised: 14 April 2023 Accepted: 18 April 2023 Published: 20 April 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). information Article Energy-Efficient Parallel Computing: Challenges to Scaling Alexey Lastovetsky † and Ravi Reddy Manumachu * ,† School of Computer Science, University College Dublin, D04V1W8 Dublin, Ireland; alexey.lastovetsky@ucd.ie * Correspondence: ravi.manumachu@ucd.ie † These authors contributed equally to this work. Abstract: The energy consumption of Information and Communications Technology (ICT) presents a new grand technological challenge. The two main approaches to tackle the challenge include the development of energy-efficient hardware and software. The development of energy-efficient software employing application-level energy optimization techniques has become an important category owing to the paradigm shift in the composition of digital platforms from single-core processors to heterogeneous platforms integrating multicore CPUs and graphics processing units (GPUs). In this work, we present an overview of application-level bi-objective optimization methods for energy and performance that address two fundamental challenges, non-linearity and heterogeneity, inherent in modern high-performance computing (HPC) platforms. Applying the methods requires energy profiles of the application’s computational kernels executing on the different compute devices of the HPC platform. Therefore, we summarize the research innovations in the three mainstream component-level energy measurement methods and present their accuracy and performance trade- offs. Finally, scaling the optimization methods for energy and performance is crucial to achieving energy efficiency objectives and meeting quality-of-service requirements in modern HPC platforms and cloud computing infrastructures. We introduce the building blocks needed to achieve this scaling and conclude with the challenges to scaling. Briefly, two significant challenges are described, namely fast optimization methods and accurate component-level energy runtime measurements, especially for components running on accelerators. Keywords: energy-efficient computing; parallel computing; high-performance computing; multicore CPU; GPU 1. Introduction The energy consumption of Information and Communications Technology (ICT) ac- counted for 7% of the global electricity usage in 2020 and is forecast to be around the average of the best-case and expected scenarios (7% and 21%) by 2030 [1]. This trend makes the energy efficiency of digital platforms a large new technological challenge. There are two main approaches to responding to this challenge—hardware and soft- ware. The first approach deals with energy-efficient hardware devices at a transistor (or gate) level and aims to produce electronic devices consuming as little power as possible. The second approach deals with the development of energy-efficient software. On the level of solutions, it can be further subdivided into the system-level and application- level approaches. The system-level approach tries to optimize the execution environment rather than the application. It is currently a mainstream approach using Dynamic Voltage and Frequency Scaling (DVFS), Dynamic Power Management (DPM), and energy-aware scheduling to optimize the energy efficiency of the execution of the application. DVFS reduces the dynamic power a processor consumes by throttling its clock frequency. Briefly, dynamic power is consumed due to the switching activity in the processor’s circuits. Static power is consumed when the processor is idle. DPM turns off the electronic components or moves them to a low-power state when idle to reduce energy consumption. Information 2023, 14, 248. https://doi.org/10.3390/info14040248 https://www.mdpi.com/journal/information