r - %dopar% or alternative method to speed up sequential stochastic calculation -

i have written stochastic process simulator speed since it's pretty slow.

the main part of simulator made of for loop re-write foreach `%dopar%.

i have tried doing simplified loop i'm running problems. suppose for loop looks

library(foreach)  r=0 t<-rep(0,500) for(n in 1:500){     s<-1/2+r     u<-runif(1, min = 0, max = 1)     if(u<s){         t[n]<-u         r<-r+0.001     }else{r<-r-0.001} } 

which means @ each iteration update value of r , s and, in 1 of 2 outcomes, populate vector t. have tried several different ways of re-writing foreach loop seems each iteration values don't updated , pretty strange results. have tried using return doesn't seem work!

this example of have come with.

rr=0 tt<-foreach(i=1:500, .combine=c) %dopar% {     ss<-1/2+rr     uu<-runif(1, min = 0, max = 1)     if(uu<=ss){         return(uu)         rr<-rr+0.001     }else{         return(0)         rr<-rr-0.001} } 

if impossible use foreach other way there me re-write loop able use cores , speed things?

since comments, turning c, encouraging , -mostly- prove isn't hard task (especially such operations) , it's worth looking into, here comparison of 2 sample functions accept number of iterations , perform steps of loop:

ffr = function(n)  {    r = 0    t = rep(0, n)    for(i in 1:n) {        s = 1/2 + r        u = runif(1)        if(u < s) {            t[i] = u            r = r + 0.001        } else r = r - 0.001    }     return(t) }   ffc = inline::cfunction(sig = c(r_n = "integer"), body = '     int n = integer(as_integer(r_n))[0];      sexp ans;     protect(ans = allocvector(realsxp, n));      double r = 0.0, s, u, *pans = real(ans);      getrngstate();      for(int = 0; < n; i++) {         s = 0.5 + r;         u = runif(0.0, 1.0);          if(u < s) {             pans[i] = u;             r += 0.001;         } else {             pans[i] = 0.0;             r -= 0.001;         }     }      putrngstate();      unprotect(1);     return(ans); ', includes = "#include <rmath.h>") 

a comparison of results:

set.seed(007); ffr(5) #[1] 0.00000000 0.39774545 0.11569778 0.06974868 0.24374939 set.seed(007); ffc(5) #[1] 0.00000000 0.39774545 0.11569778 0.06974868 0.24374939 

a comparison of speed:

microbenchmark::microbenchmark(ffr(1e5), ffc(1e5), times = 20) #unit: milliseconds #       expr        min         lq     median         uq        max neval # ffr(1e+05) 497.524808 519.692781 537.427332 668.875402 692.598785    20 # ffc(1e+05)   2.916289   3.019473   3.133967   3.445257   4.076541    20 

and sake of completeness:

set.seed(101); ans1 = ffr(1e5) set.seed(101); ans2 = ffc(1e5) all.equal(ans1, ans2) #[1] true 

hope of helpful in way.