Dear Yan, dear Dirk, thank you for these very precise answers! I think at the beginning I'll fall back to the R RNG. I will test if it is faster to create an Armadillo vector/matrix and fill it via R::rnorm, or if it is faster to use Rcpp sugar and change the Rcpp::NumericVector to an arma::vec via arma::conv_to.
Btw Dirk, this is a nice overview given at the Rcpp gallery for timing random number generators. Your HPC library Random123 looks pretty interesting to me. I am very into HPC and use openMP and OpenMPI a lot. In this case however, I have to use specifically a Mersenne Twister RNG, as I want to compare results from my older simulation package, using Scythe Statistical Library, and my newer one using RcppArmadillo. As Scythe uses MT, I want to use the same RNG and same seed to have results comparable to each other. Later on, when I know all results are plausible, I will concentrate on speed. Best Simon On Feb 9, 2013, at 5:42 PM, Yan Zhou <zhou...@me.com> wrote: >> >> This really is a BIG topic and worth a few more comments. Note that I wrote a >> few related posts on RNGs at the Rcpp Gallery, see for example >> >> http://gallery.rcpp.org/articles/timing-normal-rngs/ >> >> which compares the RNGs from R, C++11 and Boost. Simon just added Armadillo >> to >> the list, we can add even more RNGs fromn other packages. > > If it is of interest to anyone, I once timed Boost, C++11 and Random123 (A > high performance parallel RNG, > http://www.thesalmons.org/john/random123/releases/latest/docs/, It come with > a C++11 compatible RNG engine, can be used just like std::mt19937) once for > different compilers on Linux. I just uploaded them to > https://github.com/zhouyan/vSMC/wiki/RNG-performance-comparison > > There are two benchmark, one for the performance of URNG (mt19937 etc). These > include those in C++11 <random> and Boost.Random, which are almost identical > in functionality (C++11 <random> is based on Boost.Random after all). Also > they include two URNG from Random123, threefry and philox (both come with > four basic configurations) > > Another benchmark is the performance of generating distribution random > numbers (such as normal). The Random123 threefry2x64 was used for all > distribution and compilers, since it is the one with least performance > difference between compilers. > > Compilers include, > clang SVN with libstdc++ 4.7 > clang SVN with libc++ SVN > gcc 4.7 > intel icpc 13 > > clang and gcc version also come with results when using AMD libm instead of > glibc. However, the benchmark are not Rcpp specific. They are compiled to > standalone C++ programs. But all these URNGs can be used Rcpp. As > demonstrated in Dirk's example. > > Best, > > Yan Zhou >
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