Improving Energy Efficiency of Irregular Workloads with Transformers and Tabular Data Diffusion
Mohammad Ali, Zarif Sadman, Apan Qasem · 2025
This J2C paper is an extension of prior work, "Uncovering Input-Sensitive Energy Bottlenecks in Oversubscribed GPU Workloads" which was published in the Journal of Sustainable Computing (SUSCOM22). The previous paper conducted a study to analyze the energy impact of GPU oversubscription in graph algorithms and developed methods for identifying energy bottlenecks in irregular applications. The proposed work outlined in this paper, extends the previous work by adding the following components to our framework: (i) a data diffusion strategy for generating robust training samples for modeling energy behavior (ii) enhanced prediction with contextualized information using transformer models and (iii) cross-platform optimization for both AMD and NVIDIA platforms.