CodeGreen: Towards Improving Precision and Portability in Software Energy Measurement
This tool addresses energy optimization for software developers by providing a portable and accurate measurement solution, though it is incremental in improving existing profilers.
The paper tackles the trade-off between accuracy and overhead in software energy measurement by introducing CodeGreen, a modular platform that achieves high precision with R^2 = 0.9934 correlation to ground truth and enables cross-language portability.
Accurate software energy measurement is critical for optimizing energy, yet existing profilers force a trade-off between measurement accuracy and overhead due to tight coupling with supported specific hardware or languages. We present CodeGreen, a modular energy measurement platform that decouples instrumentation from measurement via an asynchronous producer-consumer architecture. We implement a Native Energy Measurement Backend (NEMB) that polls hardware sensors (Intel RAPL, NVIDIA NVML, AMD ROCm) independently, while lightweight timestamp markers enable tunable granularity. CodeGreen leverages Tree-sitter AST queries for automated instrumentation across Python, C++, C, and Java, with straightforward extension to any Tree-sitter-supported grammar, enabling developers to target specific scopes (loops, methods, classes) without manual intervention. Validation against "Computer Language Benchmarks Game" demonstrates $R^2 = 0.9934$ correlation with RAPL ground truth and $R^2 = 0.9997$ energy-workload linearity. By bridging fine-grained measurement precision with cross-platform portability, CodeGreen enables practical algorithmic energy optimization across heterogeneous environments. Source code, video demonstration, and documentation for the tool are publicly available at: https://smart-dal.github.io/codegreen/.