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Comment by isilofi

For many kinds of Monte Carlo algorithms, CSPRNGs are stupidly slow. The author compares two handpicked examples of a fast CSPRNG and a very slow PRNG, arriving at a factor of 4. In practice, e.g. comparing to very simple stuff like multiply-add RNGs, it is more like a factor of 4000. Only to then claim that "But that would only be true if generating random bits was the hot spot, the bottleneck of your program. It never is in practice."

E.g. for the usual example of Monte Carlo integration, you pick a point randomly, usually by running your RNG for both coordinates. Then you evaluate your characteristic function with that point as an input. Very often, the function will have a runtime that is in the range of a few hundred FPU instructions or less. Comparable to the evaluation of your CSPRNG. So in the end, you usually get a 40 to 50% faster runtime by just using a slow PRNG instead of a CSPRNG. Not to mention the few extra percent you will gain with a fast PRNG.

And it doesn't stop there. CSPRNG libraries are often optimized to be side-channel free and cryptographically safe. Even if you were to use a CSPRNG, you are leaving performance on the table by using stuff with security properties you will never ever need, that can easily be optimized out for a 30% gain in the CSPRNG parts.

And yes, for simulations a few percent points are relevant. Thoses "few" percents are maybe days in runtime, thousands of currency units in power and hardware cost and tons of CO2 in pollution.

parent isilofi 2 years ago

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