Estimating Power Consumption of GPU Application Using Machine Learning Tool

As Graphic Processing Units (GPU)s play an increasingly important role in High-Performance Computing (HPC) and data-intensive Machine Learning (ML) tasks, accurate power prediction is essential. Traditional methods, relying on architecture-specific models like DVFS and hardware counters, limit cross-architecture applicability of these models. We propose a static analysis framework that predicts an application's power usage across different NVIDIA GPU architectures without execution. Extensive experiments with state-of-the-art ML approaches show promising results, demonstrating generalizability in predicting power consumption for a newer architecture without the need for complete retraining.<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>This research is partially supported by the New Faculty Seed Grant of BITS Pilani under Grant No.NFSG/GOA/2023/G0916.