9 May 2025

Abstract

As computing power demands continue to grow, achieving energy efficiency in high-performance systems has become a key challenge. One of the most promising software techniques for energy efficiency is Dynamic Voltage and Frequency Scaling (DVFS) which optimize the energy-performance trade-off by changing hardware frequencies. 

This presentation introduces two complementary approaches that advance the state-of-the-art in energy-efficient heterogeneous computing through fine-grained and phase-aware frequency tuning.

The first approach, SYnergy, leverages a novel compiler- and runtime-integrated methodology built upon the SYCL programming model to enable fine-grained frequency scaling on heterogeneous hardware. SYnergy allows developers to specify energy goals for each individual kernel such as minimizing Energy-Delay Product (EDP) or achieving predefined energy-performance tradeoffs. Through compiler integration and a machine learning model, the frequency of each kernel is statically optimized based on the specific energy goal. To extend this fine-grained control to large-scale systems, SYnergy includes a custom SLURM plugin that enables execution across all available devices in a cluster, ensuring scalable energy savings.

While fine-grained frequency scaling at the kernel level can significantly improve energy efficiency, it also introduces overhead due to frequent frequency changes—an overhead that can, in some cases, outweigh the potential benefits. To address this, we propose a novel Phase-aware method that detects different phases through application profiling and DAG analysis and sets an optimal frequency for each phase. Our methodology also considers MPI programs, where the overhead can be hidden by overlapping frequency-change with communication. 

Bio

Lorenzo Carpentieri received his master’s degrees from the University of Salerno, Italy in 2022. He is now a PhD student in the Department of Computer Science at University of Salerno, Italy, under the supervision of Prof. Biagio Cosenza. His research interests include high-performance computing, compiler technology, and programming models having a particular interest in energy efficient and approximate computing.