Teams make code faster and call it greener, but speed and energy do not always move together. In GreenCode research, the GPU energy of a widely used AI inference engine fell by 4.7% while its speed rose by only 3%.
The fix you can use today
Measure energy before you change anything.
Measure again after every change, on the same machines under the same load.
Keep a change only if it saves energy and nothing else got worse.
GreenCode researchers measure each change before and after, and keep only the ones that save energy.
Tools that help
green-languages (GreenCode researchers): a command-line tool that measures the energy of whole programs or marked sections of code, across languages
GreenCode Constitution profiler (profile.sh) (GreenCode researchers): a script from the Constitution tooling that measures the energy of a command from processor counters, GPU sensors and an optional smart plug, and writes the result as JSON
Scaphandre: measures the power drawn by each process on a Linux server and exports it to Prometheus and other monitoring stacks
CodeCarbon: a Python package that estimates the energy and CO2 of a run from hardware power and regional grid intensity
perf (RAPL counters): Linux perf reads the processor's RAPL energy counters, e.g. `perf stat -e power/energy-pkg/`
How GreenCode helps
GreenCode helps tackle this by measuring energy before any change and after every optimisation pass, on the same infrastructure under the same load. A saving only counts if quality, security and error rate did not get worse. More on this.
