Working on optimizations is a task more complex than expected on the firstlook. Any optimization must be measured to make sure that, in practice, itspeeds up the application task. Problem: it is very hard to obtain stablebenchmark results. The stability of a benchmark (performance measurement) is essential to be ableto compare two versions of the code and compute the difference (faster orslower?). An unstable benchmark is useless, and is a risk of giving a falseresult when comparing performance which could lead to bad decisions. I'm gonna show you the Python project "perf" which helps to launch benchmarks,but also to analyze them: compute the mean and the standard deviation onmultiple runs, render an histogram to visualize the probability curve, comparebetween multiple results, run again a benchmark to collect more samples, etc. The use case is to measure small isolated optimizations on CPython and makesure that they don't introduce performance regression in term of performance.