Analyzing the Impact of DVFS on Performance and Energy of Parallel Applications on GPUs

• Thiago dos S. Gonçalves UFRGS
• Arthur F. Lorenzon UFRGS

DOI: https://doi.org/10.5753/eradrs.2025.6824

Abstract

The parallel processing capability of Graphics Processing Units (GPUs) has made their use essential in accelerating artificial intelligence applications. With a strong presence of matrix multiplication in these applications, new strategies are needed to achieve better energy efficiency. In this way, we analyze the impact of cache metrics on a GPU and show a 19.87% difference in energy consumption with small gains in performance.

Keywords: Parallel and Distributed Algorithms, Evaluation, Measurement, and Performance Prediction, Heterogeneous Computing

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