Article
2026
·
0 citations
PowerDL: A unified in-memory GPU energy profiling and visualization toolkit for deep learning
Abstract
This study presents PowerDL, an in-memory software tool designed for deep learning workloads that profiles GPU energy consumption, power usage, and hardware utilization rates. PowerDL provides a unified interface for PyTorch and TensorFlow frameworks, enabling high-time-resolution analysis of GPU behavior during training and inference phases. The software supports interactive exploration of energy and performance metrics without requiring mandatory file writing. Time-series-based power traces, cumulative energy consumption, and phaseaware visualizations enable users to conduct energy-aware deep learning experiments. Additionally, optional output export mechanisms are provided to support reproducible research.
Keywords
GPU energy profiling
Power consumption measurement
In-memory profiling
Energy-aware computing
WoS Categories
Computer Science, Software Engineering
Citation
- Journal
- SOFTWAREX
- Year
- 2026
- Volume
- 34
- ISSN
- 2352-7110
- DOI
- 10.1016/j.softx.2026.102627
- WoS UID
- WOS:001732756600001