On Apr 3, 2017 8:59 AM, "Pierre Haessig" <pierre.haes...@crans.org> wrote:
Le 03/04/2017 à 15:44, Jaime Fernández del Río a écrit : This <https://github.com/JuliaLang/julia/blob/7fb758a275a0b4cf0e3f4cbf482c065cb32f0011/doc/src/stdlib/numbers.md#L116> says that Julia uses this library <http://www.math.sci.hiroshima-u.ac.jp/%7Em-mat/MT/SFMT/#dSFMT>, which is different from the home brewed version of the Mersenne twister in NumPy. The second link I posted claims their speed comes from generating double precision numbers directly, rather than generating random bytes that have to be converted to doubles, as is the case of NumPy through this magical incantation <https://github.com/numpy/numpy/blob/master/numpy/random/mtrand/randomkit.c#L514>. They also throw the SIMD acronym around, which likely means their random number generation is parallelized. My guess is that most of the speed-up comes from the SIMD parallelization: the Mersenne algorithm does a lot of work <https://github.com/numpy/numpy/blob/master/numpy/random/mtrand/randomkit.c#L221> to produce 32 random bits, so that likely dominates over a couple of arithmetic operations, even if divisions are involved. Thanks for the feedback. I'm not good in enough in reading Julia to be 100% sure, but I feel like that the random.jl (https://github.com/JuliaLang/ julia/blob/master/base/random.jl) contains a Julia implementation of Mersenne Twister... but I have no idea whether it is the "fancy" SIMD version or the "old" 32bits version. That code contains many references to "dSFMT", which is the name of the "fancy" algorithm. IIUC dSFMT is related to the mersenne twister but is actually a different generator altogether -- advertising that Julia uses the mersenne twister is somewhat misleading IMHO. Of course this is really the fault of the algorithm's designers for creating multiple algorithms that have "mersenne twister" as part of their names... -n
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