I found a solution via Rcpp::Function and then using the R function set.seed. But I do not know what happens, when iterations run in parallel....I fear, that then this solution shows undefined behavior. On Feb 10, 2013, at 10:33 AM, Simon Zehnder <szehn...@uni-bonn.de> wrote:
> Actually I arrived at another fundamental question: > > I read '6.3 Random Number Generation' in the R Extensions Guide. It seems > that it is not possible to set out of C++ the random seed (the wrapper > RNGScope calls GetRNGstate() and PutRNGState() so it should have no extended > functionality), which is very important when trying to produce reproducible > results. So, I need a Mersenne-Twister, but I must be able to set the seed in > every iteration. I think this brings me to the GSL library or - as long only > normal random variates are needed - to the C++ std::random header? > > > 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 >> > > _______________________________________________ > Rcpp-devel mailing list > Rcpp-devel@lists.r-forge.r-project.org > https://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/rcpp-devel
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