PowerDL: A unified in-memory GPU energy profiling and visualization toolkit for deep learning
Özet
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.
Atıf için künye
Sancar, Y. (2026). PowerDL: A unified in-memory GPU energy profiling and visualization toolkit for deep learning. SOFTWAREX, 34. https://doi.org/10.1016/j.softx.2026.102627
BibTeX
@article{sancar2026-76903,
author = {Sancar, Yasin},
title = {PowerDL: A unified in-memory GPU energy profiling and visualization toolkit for deep learning},
journal = {SOFTWAREX},
year = {2026},
volume = {34},
issn = {2352-7110},
doi = {10.1016/j.softx.2026.102627},
url = {https://akademik.atauni.edu.tr/yayinlar/76903-powerdl-a-unified-in-memory-gpu-energy-profiling-and-visualization-toolkit-for}
}