If you didn't measure it twice, you didn't save it.
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%.
GreenCode researchers measure each change before and after, and keep only the ones that save energy.
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.
If you didn't measure it twice, you didn't save it. Teams often make code faster, and call it greener. But faster is not always greener. In GreenCode research, the energy of a widely used AI program fell nearly five percent. Its speed rose by only three percent. Speed and energy do not always move together. Here is 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. Many processors and graphics cards report their own energy use. So you don't have to guess. Read them using tools like GreenCode's green-languages, perf on Linux, and Scaphandre. Two numbers, before and after, are something your whole team can trust. GreenCode researchers measure each change before and after. They keep only the ones that save energy. GreenCode helps tackle this by measuring your system under the same load, before and after. A saving only counts if quality, security and error rate did not get worse. That was GreenCode Tech Tip number three. Follow for the next tip, and find more on greencode.ai/tech-tips.
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