Install

pip install powerlog

Python 3.8 or newer, no Python dependencies. Power sources are detected at runtime: NVIDIA (NVML), AMD (ROCm SMI or amdgpu sysfs), Intel (Level Zero) and CPU RAPL on Linux. Anything unavailable is reported as n/a rather than failing the run.

Use

powerlog ./my_program --arg value

================================================================
                    POWERLOG ENERGY SUMMARY
================================================================
Command                 ./my_program --arg value
Runtime (s)             12.4180
CPU                     AMD EPYC 7532 32-Core Processor
GPU                     NVIDIA A100-PCIE-40GB
----------------------------------------------------------------
Domain            Energy (J)   Share (%)   Avg Power (W)
----------------------------------------------------------------
CPU                 962.4013       31.06         77.5013
GPU                2136.7742       68.94        172.0700
----------------------------------------------------------------
TOTAL              3099.1755
EDP (J*s)         38485.5262
----------------------------------------------------------------
Samples                 124
CPU source              CPU package (RAPL powercap sysfs), 2 package domain(s)
GPU source              NVIDIA GPU (nvidia-smi / NVML), 1 device(s)
================================================================

Powerlog runs unmodified binaries and samples CPU and GPU power in lockstep, so the measurement covers the whole application — I/O, transfers, kernel launches, synchronization — not just the kernels. It writes a summary CSV and a power trace, and exits with the program's own status.

Check what your machine exposes with powerlog --list-backends.

Documentation

Everything else is at powerlog.readthedocs.io:

Installation · Quick Start · CLI Reference · Example Applications · Backends · Methodology · Output Files · Python API

This repository additionally carries two things the wheel does not: example GPU programs in apps/, and datalog-engine-comparison/, a study comparing the energy behaviour of five GPU-accelerated Datalog engines.

Contributing

Bug reports and patches are welcome on the issue tracker. Open an issue first for anything substantial, then send a pull request against main. Release history is in Changelog.md.

License

MIT. See LICENSE.

Citation

@inproceedings{shovon2026heterogeneous,
  title={Heterogeneous Energy Characterization of GPU-Powered Datalog Engines},
  author={Shovon, Ahmedur Rahman and Sun, Yihao and Lan, Zhiling and Perarnau, Swann and Gilray, Thomas and Micinski, Kristopher and Papka, Michael E and Kumar, Sidharth},
  booktitle={2026 IEEE/ACM Workshop on Energy Efficiency with Sustainable Performance: Techniques, Tools, and Best Practices (EESP)},
  year={2026},
  organization={IEEE}
}