codegreen 0.3.16

Know the true energy cost of your code -- hardware-level measurement via RAPL, NVML, IOReport, EMI

CodeGreen - Garage for Energy Measurement and Optimization

CodeGreen is a comprehensive tool for fine-grained energy profiling and optimization of code. It provides real-time energy measurement during code execution, identifies energy hotspots, and offers optimization suggestions to reduce energy consumption.

Installation

From PyPI (recommended for users)

pip install codegreen

Pre-built wheels available for Linux x86_64 and macOS ARM64 (Apple Silicon). Includes the native NEMB energy measurement backend.

From source (recommended for development)

git clone https://github.com/SMART-Dal/codegreen.git
cd codegreen
./install.sh

# Linux: RAPL sensor access (one-time, requires sudo):
sudo ./install.sh   # or: sudo codegreen init-sensors

# macOS: no setup needed, energy measurement works out of the box

Platform support

Platform	pip install	Energy measurement	Backend
Linux x86_64 (Intel)	Pre-built wheel	Full (PKG, core, iGPU, DRAM)	RAPL via NEMB
Linux x86_64 (AMD)	Pre-built wheel	Full (PKG, no DRAM counter)	RAPL via NEMB
macOS ARM64 (Apple Silicon)	Pre-built wheel	Full (CPU, GPU, ANE, DRAM)	IOReport + kpc via NEMB
macOS Intel	From source	Full	IOReport via NEMB
Windows 11 (Intel)	From source	Full (PKG, core, iGPU, DRAM)	EMI via intelpep.sys
Windows 11 (AMD)	From source	Unverified	EMI may not expose AMD RAPL
NVIDIA GPU (any OS)	Automatic	Full (cumulative mJ)	NVML
Other	From source	Time-only	Fallback

Requirements

• Python 3.9+
• Linux: kernel 5.0+, Intel/AMD CPU with RAPL support
• macOS: Apple Silicon (M1-M5) or Intel, no setup needed
• Windows 11: Intel/AMD CPU (EMI via inbox intelpep.sys driver, zero install)
• Source builds: CMake 3.16+, C++17 compiler

Usage

Measure energy of any command (like hyperfine, but for energy)

codegreen run -- python script.py
codegreen run --repeat 20 --warmup 3 -- ./my_binary arg1 arg2
codegreen run --budget 10.0 --json -- python train.py   # CI/CD gate

Per-function energy profiling

# Coarse mode: total program energy
codegreen measure python script.py

# Fine mode: per-function energy breakdown
codegreen measure python script.py -g fine --json

# With energy timeline plot
codegreen measure python script.py -g fine --export-plot energy.html

Static analysis (no execution)

codegreen analyze python script.py --save-instrumented

Benchmark validation (against perf RAPL ground truth)

codegreen benchmark -p nbody spectralnorm -l python -r 5 --profilers codegreen perf

Output formats

JSON (default for --json), CSV, Markdown table, and text summary. The JSON output is a comprehensive single source of truth containing system state, per-function energy, instrumentation points with AST-stable identifiers, and statistical analysis.

Language support

Adding a new language requires only a JSON config file in codegreen/instrumentation/configs/ plus the tree-sitter grammar. No Python code changes needed.

Currently supported: Python, C, C++, Java. JavaScript config exists but is not yet exposed via CLI.

Benchmarking

# Compare CodeGreen accuracy against perf RAPL ground truth
codegreen benchmark --suite benchmarksgame -l python --reps 5

# Run PerfOpt Java benchmark suite (JMH, original vs patched comparison)
codegreen benchmark --suite perfopt --dataset-dir /path/to/PerfOpt --jars-dir /path/to/jars

# Generate comparison artifacts for documentation
bash scripts/generate_comparison_artifacts.sh docs/benchmarks/

Energy Flow Graph (EFG)

CodeGreen includes an Energy Flow Graph module (codegreen/analysis/cfg/) that builds energy-annotated control flow graphs from source code:

from codegreen.analysis.cfg.builder import build_per_method_cfgs
from codegreen.analysis.cfg.energy_flow import build_efg, efg_to_dot, efg_to_text

cfgs = build_per_method_cfgs(java_source_code)
efg = build_efg(cfg_nodes, cfg_edges, "ClassName.method", "File.java", codegreen_data)
print(efg_to_text(efg))  # compact format for LLM prompts

Features: Ball & Larus branch heuristics, SCC-based hot path computation, three-level accuracy annotations (MEASURED/ESTIMATED/INFERRED), configurable via EFGConfig.

Architecture

• C++ NEMB backend: platform-aware energy measurement (RAPL on Linux, IOReport on macOS, EMI on Windows), sub-microsecond timestamping, background polling with lock-free ring buffers
• Python instrumentation: tree-sitter AST analysis, config-driven checkpoint insertion
• Energy Flow Graph: CFG + energy annotation for path-dependent analysis
• Benchmark harness: multi-suite support (benchmarksgame, PerfOpt), statistical analysis with t-distribution CI, IQR outlier detection, profiler comparison

CI/CD integration

# Fail pipeline if energy exceeds budget
codegreen run --budget 10.0 --json -- python tests/benchmark.py
# Exit code 1 if mean energy > 10 Joules

Upgrading

pip install --upgrade codegreen   # PyPI
# or
cd codegreen && git pull && ./install.sh --upgrade   # source

Citation

@article{rajput2026codegreen,
  title={CodeGreen: Towards Improving Precision and Portability in Software Energy Measurement},
  author={Rajput, Saurabhsingh and Sharma, Tushar},
  journal={arXiv preprint arXiv:2603.17924},
  year={2026}
}

License

MPL-2.0 License - see LICENSE file.

Docs: smart-dal.github.io/codegreen