PowerDL: A unified in-memory GPU energy profiling and visualization toolkit for deep learning

Sancar, Yasin

doi:10.1016/j.softx.2026.102627

PowerDL: A unified in-memory GPU energy profiling and visualization toolkit for deep learning

Sancar Y.

SoftwareX, cilt.34, ss.102627, 2026 (Scopus)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 34
Basım Tarihi: 2026
Doi Numarası: 10.1016/j.softx.2026.102627
Dergi Adı: SoftwareX
Derginin Tarandığı İndeksler: Scopus
Sayfa Sayıları: ss.102627
Atatürk Üniversitesi Adresli: Evet

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 phase-aware visualizations enable users to conduct energy-aware deep learning experiments. Additionally, optional output export mechanisms are provided to support reproducible research.