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It may be fair to say that if one is writing discrete event simulations in pure Python that performance is not their 'top' priority. Python's dynamic typing and garbage collection preclude being in the top tier of performance among DES tools.
But I think that performance still matters and tracking and benchmarking can put some observability on performance issues.
I've been looking into airspeed velocity. It supports running benchmarks across commits so you can see how things have improved or worsened. It is kind of like writing unit tests, except that they measure run times and memory usage.
I suggest trying this out with Ciw.
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