## Posts Tagged ‘calling’

31/07/2011

### Introduction

This post follows on from the previous post on Gibbs sampling in various languages. In that post a simple Gibbs sampler was implemented in various languages, and speeds were compared. It was seen that R is very slow for iterative simulation algorithms characteristic of MCMC methods such as the Gibbs sampler. Statically typed languages such as C/C++ and Java were seen to be fastest for this type of algorithm. Since many statisticians like to use R for most of their work, there is natural interest in the possibility of extending R by calling simulation algorithms written in other languages. It turns out to be straightforward to call C, C++ and Java from within R, so this post will look at how this can be done, and exactly how fast the different options turn out to be. The post draws heavily on my previous posts on calling C from R and calling Java from R, as well as Dirk Eddelbuettel’s post on calling C++ from R, and it may be helpful to consult these posts for further details.

### Languages

#### R

We will start with the simple pure R version of the Gibbs sampler, and use this as our point of reference for understanding the benefits of re-coding in other languages. The background to the problem was given in the previous post and so won’t be repeated here. The code can be given as follows:

```gibbs<-function(N=50000,thin=1000)
{
mat=matrix(0,ncol=2,nrow=N)
x=0
y=0
for (i in 1:N) {
for (j in 1:thin) {
x=rgamma(1,3,y*y+4)
y=rnorm(1,1/(x+1),1/sqrt(2*x+2))
}
mat[i,]=c(x,y)
}
names(mat)=c("x","y")
mat
}
```

This code works perfectly, but is very slow. It takes 458.9 seconds on my very fast laptop (details given in previous post).

#### C

Let us now see how we can introduce a new function, gibbsC into R, which works in exactly the same way as gibbs, but actually calls on compiled C code to do all of the work. First we need the C code in a file called gibbs.c:

```#include <stdio.h>
#include <math.h>
#include <stdlib.h>
#include <R.h>
#include <Rmath.h>

void gibbs(int *Np,int *thinp,double *xvec,double *yvec)
{
int i,j;
int N=*Np,thin=*thinp;
GetRNGstate();
double x=0;
double y=0;
for (i=0;i<N;i++) {
for (j=0;j<thin;j++) {
x=rgamma(3.0,1.0/(y*y+4));
y=rnorm(1.0/(x+1),1.0/sqrt(2*x+2));
}
xvec[i]=x; yvec[i]=y;
}
PutRNGstate();
}
```

This can be compiled with R CMD SHLIB gibbs.c. We can load it into R and wrap it up so that it is easy to use with the following code:

```dyn.load(file.path(".",paste("gibbs",.Platform\$dynlib.ext,sep="")))
gibbsC<-function(n=50000,thin=1000)
{
tmp=.C("gibbs",as.integer(n),as.integer(thin),
x=as.double(1:n),y=as.double(1:n))
mat=cbind(tmp\$x,tmp\$y)
colnames(mat)=c("x","y")
mat
}
```

The new function gibbsC works just like gibbs, but takes just 12.1 seconds to run. This is roughly 40 times faster than the pure R version, which is a big deal.

Note that using the R inline package, it is possible to directly inline the C code into the R source code. We can do this with the following R code:

```require(inline)
code='
int i,j;
int N=*Np,thin=*thinp;
GetRNGstate();
double x=0;
double y=0;
for (i=0;i<N;i++) {
for (j=0;j<thin;j++) {
x=rgamma(3.0,1.0/(y*y+4));
y=rnorm(1.0/(x+1),1.0/sqrt(2*x+2));
}
xvec[i]=x; yvec[i]=y;
}
PutRNGstate();'
gibbsCin<-cfunction(sig=signature(Np="integer",thinp="integer",xvec="numeric",yvec="numeric"),body=code,includes="#include <Rmath.h>",language="C",convention=".C")
gibbsCinline<-function(n=50000,thin=1000)
{
tmp=gibbsCin(n,thin,rep(0,n),rep(0,n))
mat=cbind(tmp\$x,tmp\$y)
colnames(mat)=c("x","y")
mat
}
```

This runs at the same speed as the code compiled separately, and is arguably a bit cleaner in this case. Personally I’m not a big fan of inlining code unless it is something really very simple. If there is one thing that we have learned from the murky world of web development, it is that little good comes from mixing up different languages in the same source code file!

#### C++

We can also inline C++ code into R using the inline and Rcpp packages. The code below originates from Sanjog Misra, and was discussed in the post by Dirk Eddelbuettel mentioned at the start of this post.

```require(Rcpp)
require(inline)

gibbscode = '
int N = as<int>(n);
int thn = as<int>(thin);
int i,j;
RNGScope scope;
NumericVector xs(N),ys(N);
double x=0;
double y=0;
for (i=0;i<N;i++) {
for (j=0;j<thn;j++) {
x = ::Rf_rgamma(3.0,1.0/(y*y+4));
y= ::Rf_rnorm(1.0/(x+1),1.0/sqrt(2*x+2));
}
xs(i) = x;
ys(i) = y;
}
return Rcpp::DataFrame::create( Named("x")= xs, Named("y") = ys);
'

RcppGibbsFn <- cxxfunction( signature(n="int", thin = "int"),
gibbscode, plugin="Rcpp")

RcppGibbs <- function(N=50000,thin=1000)
{
RcppGibbsFn(N,thin)
}
```

This version of the sampler runs in 12.4 seconds, just a little bit slower than the C version.

#### Java

It is also quite straightforward to call Java code from within R using the rJava package. The following code

```import java.util.*;
import cern.jet.random.tdouble.*;
import cern.jet.random.tdouble.engine.*;

class GibbsR
{

public static double[][] gibbs(int N,int thin,int seed)
{
DoubleRandomEngine rngEngine=new DoubleMersenneTwister(seed);
Normal rngN=new Normal(0.0,1.0,rngEngine);
Gamma rngG=new Gamma(1.0,1.0,rngEngine);
double x=0,y=0;
double[][] mat=new double[2][N];
for (int i=0;i<N;i++) {
for (int j=0;j<thin;j++) {
x=rngG.nextDouble(3.0,y*y+4);
y=rngN.nextDouble(1.0/(x+1),1.0/Math.sqrt(2*x+2));
}
mat[0][i]=x; mat[1][i]=y;
}
return mat;
}

}
```

can be compiled with javac GibbsR.java (assuming that Parallel COLT is in the classpath), and wrapped up from within an R session with

```library(rJava)
.jinit()
obj=.jnew("GibbsR")

gibbsJ<-function(N=50000,thin=1000,seed=trunc(runif(1)*1e6))
{
result=.jcall(obj,"[[D","gibbs",as.integer(N),as.integer(thin),as.integer(seed))
mat=sapply(result,.jevalArray)
colnames(mat)=c("x","y")
mat
}
```

This code runs in 10.7 seconds. Yes, that's correct. Yes, the Java code is faster than both the C and C++ code! This really goes to show that Java is now an excellent option for numerically intensive work such as this. However, before any C/C++ enthusiasts go apoplectic, I should explain why Java turns out to be faster here, as the comparison is not quite fair... In the C and C++ code, use was made of the internal R random number generation routines, which are relatively slow compared to many modern numerical library implementations. In the Java code, I used Parallel COLT for random number generation, as it isn't straightforward to call the R generators from Java code. It turns out that the COLT generators are faster than the R generators, and that is why Java turns out to be faster here...

#### C+GSL

Of course we do not have to use the R random number generators within our C code. For example, we could instead call on the GSL generators, using the following code:

```#include <stdio.h>
#include <math.h>
#include <stdlib.h>
#include <gsl/gsl_rng.h>
#include <gsl/gsl_randist.h>
#include <R.h>

void gibbsGSL(int *Np,int *thinp,int *seedp,double *xvec,double *yvec)
{
int i,j;
int N=*Np,thin=*thinp,seed=*seedp;
gsl_rng *r = gsl_rng_alloc(gsl_rng_mt19937);
gsl_rng_set(r,seed);
double x=0;
double y=0;
for (i=0;i<N;i++) {
for (j=0;j<thin;j++) {
x=gsl_ran_gamma(r,3.0,1.0/(y*y+4));
y=1.0/(x+1)+gsl_ran_gaussian(r,1.0/sqrt(2*x+2));
}
xvec[i]=x; yvec[i]=y;
}
}
```

It can be compiled with R CMD SHLIB -lgsl -lgslcblas gibbsGSL.c, and then called as for the regular C version. This runs in 8.0 seconds, which is noticeably faster than the Java code, but probably not “enough” faster to make it an important factor to consider in language choice.

### Summary

In this post I’ve shown that it is relatively straightforward to call code written in C, C++ or Java from within R, and that this can give very significant performance gains relative to pure R code. All of the options give fairly similar performance gains. I showed that in the case of this particular example, the “obvious” Java code is actually slightly faster than the “obvious” C or C++ code, and explained why, and how to make the C version slightly faster by using the GSL. The post by Dirk shows how to call the GSL generators from the C++ version, which I haven’t replicated here.

01/01/2011

### Introduction

In the previous post I looked at some simple methods for calling C code from R using a simple Gibbs sampler as the motivating example. In this post we will look again at the same Gibbs sampler, but now implemented in Java, and look at a couple of options for calling that code from an R session.

### Stand-alone Java code

Below is some Java code for implementing the bivariate Gibbs sampler discussed previously. It relies on Parallel COLT, which must be installed and in the Java CLASSPATH in order to follow the examples.

```import java.util.*;
import cern.jet.random.tdouble.*;
import cern.jet.random.tdouble.engine.*;

class Gibbs
{

public static void main(String[] arg)
{
if (arg.length != 3) {
System.err.println("Usage: java Gibbs <Iters> <Thin> <Seed>");
System.exit(1);
}
int N=Integer.parseInt(arg[0]);
int thin=Integer.parseInt(arg[1]);
int seed=Integer.parseInt(arg[2]);
DoubleRandomEngine rngEngine=new DoubleMersenneTwister(seed);
Normal rngN=new Normal(0.0,1.0,rngEngine);
Gamma rngG=new Gamma(1.0,1.0,rngEngine);
double x=0,y=0;
System.out.println("Iter x y");
for (int i=0;i<N;i++) {
for (int j=0;j<thin;j++) {
x=rngG.nextDouble(3.0,y*y+4);
y=rngN.nextDouble(1.0/(x+1),1.0/Math.sqrt(x+1));
}
System.out.println(i+" "+x+" "+y);
}
}

}
```

It can be compiled and run stand-alone from an OS shell with the following commands:

```javac Gibbs.java
java Gibbs 10 1000 1
```

As discussed in the previous post, it is possible to call any command-line program from inside an R session using the system() command. A small wrapper function for conveniently running this code from within R can be written as follows.

```gibbs<-function(N=10000,thin=500,
seed=trunc(runif(1)*1e6),
exec="Gibbs",
tmpfile=tempfile())
{
command=paste("java",exec,N,thin,seed,">",tmpfile)
system(command)
}
```

This can then be run from within an R session with a simple call to gibbs(). Note that a random seed is being generated within R to be passed to the Java code to be used to seed the COLT random number generator used within the Java code. As previously discussed, for many long running codes, this approach can be quite effective, and is clearly very simple. However, there is an overhead associated with the system() call, and also with writing output to disk and then reading it back again.

### Using rJava

It is possible to avoid the overheads associated with the above approach by directly calling the Java code from R and having the return values returned directly into the R session from memory. There isn’t really direct support for this within the core R language, but there are couple of different solutions provided by R packages. The simplest and most popular approach seems to be the rJava package. This package can be installed with a simple

```install.packages("rJava")
```

This should “just work” on some OSs (eg. Windows), but may fail on other OSs if R is not aware of the local Java environment. If the installation fails, check the error message carefully for advice/instructions. On most Linux systems, the problem can be fixed by quitting R, then running the following command from the shell

```sudo R CMD javareconf
```

before re-starting R and re-attempting the installation. rJava provides a mechanism for starting a JVM within the running R session, creating objects, calling methods and having method return values returned to R. It is actually much more flexible than the .C() function for C code discussed in the previous post.

In order to use this package for our example, we must first re-factor the code slightly in the following way.

```import java.util.*;
import cern.jet.random.tdouble.*;
import cern.jet.random.tdouble.engine.*;

class GibbsR
{

public static void main(String[] arg)
{
if (arg.length != 3) {
System.err.println("Usage: java GibbsR <Iters> <Thin> <Seed>");
System.exit(1);
}
int N=Integer.parseInt(arg[0]);
int thin=Integer.parseInt(arg[1]);
int seed=Integer.parseInt(arg[2]);
double[][] mat=gibbs(N,thin,seed);
System.out.println("Iter x y");
for (int i=0;i<N;i++) {
System.out.println(""+i+" "+mat[0][i]+" "+mat[1][i]);
}
}

public static double[][] gibbs(int N,int thin,int seed)
{
DoubleRandomEngine rngEngine=new DoubleMersenneTwister(seed);
Normal rngN=new Normal(0.0,1.0,rngEngine);
Gamma rngG=new Gamma(1.0,1.0,rngEngine);
double x=0,y=0;
double[][] mat=new double[2][N];
for (int i=0;i<N;i++) {
for (int j=0;j<thin;j++) {
x=rngG.nextDouble(3.0,y*y+4);
y=rngN.nextDouble(1.0/(x+1),1.0/Math.sqrt(x+1));
}
mat[0][i]=x; mat[1][i]=y;
}
return mat;
}

}
```

This code can be compiled and run from the command-line just as the previous code could.

```javac GibbsR.java
java GibbsR 10 1000 1
```

However, we have now separated out the code we want to be able to call from R into a static method called gibbs, which runs the Gibbs sampler and stores the result in a 2-dimensional array which is its return value. We can now see how to call this code from within a running R session. We first need to set up the R environment ready to call the code.

```library(rJava)
.jinit()
obj=.jnew("GibbsR")
```

Line 1 loads the package, line 2 starts up the JVM, and line 3 creates a link to the the GibbsR class (in general this is used to create a new Java object of the given type, but here we are using static methods). Java methods are called on Java objects using .jcall(). We can write a simple R function to conveniently call the method as follows.

```jgibbs<-function(N=10000,thin=500,seed=trunc(runif(1)*1e6))
{
result=.jcall(obj,"[[D","gibbs",as.integer(N),as.integer(thin),as.integer(seed))
mat=sapply(result,.jevalArray)
mat=cbind(1:N,mat)
colnames(mat)=c("Iter","x","y")
mat
}
```

This can now be called with a simple jgibbs(). The first line of the function body carries out the actual method call. The return type of the method must be explicitly declared – “[[D” means a 2-dimensional array of doubles, using JNI notation. Care must also be taken to coerce the method parameters into the correct type that the Java method expects to receive. .jcall() is generally quite good at unpacking basic Java types into corresponding R types. However, the two dimensional array is here returned as an R list consisting of one-dimensional Java array objects. The unpacking is completed using the subsequent call to jevalArray() using sapply(), before the resulting matrix is tidied up and returned to the R session.

We have looked at a couple of very simple methods for calling Java code from an R session. The rJava package is a very flexible mechanism for integrating Java code into R.

I haven’t found a lot of tutorial-level material on the web for the rJava package. However, the package itself has very good documentation associated with it. Start with the information on the rJava home page. From an R session with the rJava package loaded, help(package="rJava") lists the available functions, all of which have associated documentation. ?.jinit, ?.jnew, ?.jcall and ?.jevalArray provide further background and information on the example covered here.

After that, the source code of R packages which use rJava are a useful source of further inspiration – look at the reverse-depends list for rJava in CRAN. In particular, the helloJavaWorld package is a tutorial for how to include Java code in an R package (read the associated vignette).

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