# Fortran and R – Speed Things Up

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If you are a newcomer to R then you are probably quite busy learning the semantics of the language as you experiment with the apply family of commands or come up to speed on the grouping and conditioning capabilities offered by lattice graphics. And, along the way, you might have heard that R has the ability to “link in” code written in other languages such as C, C++, Fortran, and Java. This is true but until you are presented with a compelling use case you will most likely ignore such capability since you’ve already got plenty to do. But that’s where this blog can help. I’ll talk about how to integrate Fortran code with R and, in a later post, discuss doing the same with C and C++.

**DISCLAIMER:** To accomplish this work requires the presence of a working Fortran compiler such as the GNU suite of compilers (g77, gfortran, gcc, etc). Relative to operating systems I use Linux and OSX. I rarely use Windows though do know that the GNU suite is available for that OS so you should be able to link Fortran code into R there. However, I’ve never tried it.

### Why Bother ?

Good question. Most people wind up wanting to access Fortran from R for a few reasons such as they have some really fast and efficient Fortran code that they want to exploit within R. Or maybe they have written some code in R that winds up being incredibly slow so they write a much faster version in Fortran and then want to call it from R. Perhaps they need to access subroutines from external Fortran libraries. Lastly, it might simply be because your boss or faculty advisor is making you do it ! Whatever your reason(s) we’ll break the process of linking in Fortran code down into three general steps: 1) prepare the subroutine for compilation and generate a “shared object”, 2) load the shared object into an active R session, and 3) provide R variables to the shared object via the “.Fortran” call, which is part of the “Foreign Function Interface” within R.

Foreign package:base R Documentation Foreign Function Interface Description: Functions to make calls to compiled code that has been loaded into R. Usage: .C(.NAME, ..., NAOK = FALSE, DUP = TRUE, PACKAGE, ENCODING) .Fortran(.NAME, ..., NAOK = FALSE, DUP = TRUE, PACKAGE, ENCODING)

## Prepare the Fortran Subroutine

Next, I present a very simple Fortran 77 subroutine that computes the factorial of a number “n” and stashes the result into a variable called “answer”. And speaking of subroutines it is important to know that to use the .Fortran interface one must make reference to Fortran subroutines only – not Fortran functions or full on programs. So if you have some code that you want to bring in then you will need to embed that code within a subroutine definition. We also need to pay attention to the variable types as declared within the subroutine so we can match those types accordingly when calling the subroutine from R. This winds up being one of the more critical steps in the process.

subroutine facto(n,answer) c c simple subroutine to compute factorial c integer n, answer, i answer = 1 do 100 i = 2,n answer = answer * i 100 continue end

You should make sure, of course, that this code does compile correctly. Our goal is to generate a shared object file ( a “.so” file) that can be linked in to R. Note also that our routine doesn’t print/write things to output. It simply uses its input variables and ultimately sets an output variable. Make your code lean.

$ ls facto.f $ gfortran -c facto.f $ ls facto.f facto.o $ gfortran -shared -o facto.so facto.o $ ls facto.f facto.o facto.so

So it looks like we are good to go here. However, instead of doing this compilation ourselves we could have allowed R to help us. In fact it is the preferred way to do this since this will insure the compilation is done “under the supervision” of the R tools. Let’s remove the .o and .so files and start over.

$ rm *.*o $ ls facto.f $ R CMD SHLIB facto.f$ ls facto.f facto.o facto.so

## Load it

So now what ? Let’s fire up R. We’ll use the dyn.load command which is part of the foreign interface capability. It’s purpose is to load/unload shared objects which are also known as DLLs, (dynamically loadable libraries).

system("ls") facto.f facto.o facto.so dyn.load("facto.so")

Okay not much happened there. What’s going on ? Well all we did was simply load the shared object. We have yet to use it. To do that we rely upon the “.Fortran” function. Keep in mind that the subroutine “facto” has two arguments both of which are integers. We’ll supply a value of 5 for “n” and we’ll pass a single integer as a value for the “answer” variable though that will be overwritten once the subroutine computes the “answer”.

system("ls") facto.f facto.o facto.so dyn.load("facto.so") .Fortran("facto",n=as.integer(5),answer=as.integer(1)) $n [1] 5 $answer [1] 120 # Or more directly .Fortran("facto",n=as.integer(5),answer=as.integer(1))$answer [1] 120

If you are wondering what types are supported or shared between R and Fortran here is a list from the .Fortran man page. It is worth some time to peruse the man page as it provides some caveats and deeper explanations on how to interact with Fortran.

R Fortran integer integer numeric double precision - or - real complex double complex logical integer

## Wrap it Up!

Okay that was cool but this is sort of an awkward way to call the Fortran subroutine. We should probably write our own wrapper function in R to do the loading of the shared object and the referencing to the .Fortran function. Take a look at this approach which winds up being very “R like”.

myfacto <- function(num) { dyn.load("facto.so") retvals <- .Fortran("facto",n = as.integer(num), answer = as.integer(1)) return(retvals$answer) } myfacto(5) [1] 120 sapply(1:10,myfacto) [1] 1 2 6 24 120 720 5040 40320 362880 3628800

So with the wrapper approach we can use the Fortran subroutine just as we would any other R function since the call to .Fortran is “buried” in the wrapper function. We could make this a bit more robust by putting in some logic to see if the shared object is already loaded.

myfacto <- function(num) { if (!is.loaded('facto')) { dyn.load("facto.so") } retvals <- .Fortran("facto",n = as.integer(num), answer = as.integer(1)) return(retvals$answer) } myfacto(5) [1] 120

## It’s all Convoluted

Well that was okay but let’s look at a more involved example. Let’s consider the idea of doing discrete convolution between two vectors. (Note: This is discussed in the “Writing R Extensions” manual). Why did I pick such an example ? Well first it’s commonly referenced in R literature and , second, it is a good motivating case for using an external language to speed up the processing. The algorithm itself isn’t hard to code up either in R or Fortran. However, the performance in R isn’t so good once the vectors get larger. Check it out:

conr <- function(x, y) { lx <- length(x) ly <- length(y) cxy <- numeric(lx + ly - 1) for(i in 1:lx) { xi <- x[i] for(j in 1:ly) { ij <- i+j-1 cxy[ij] <- cxy[ij] + xi * y[j] } } return(cxy) } # Let's check the timings for vectors of different sizes v1 = rnorm(100); v2 = rnorm(100) system.time(conr(v1,v2)) user system elapsed 0.034 0.000 0.035 v1 = rnorm(2000); v2 = rnorm(2000) system.time(conr(v1,v2)) user system elapsed 13.195 0.020 13.215 v1 = rnorm(4000); v2 = rnorm(4000) system.time(conr(v1,v2)) user system elapsed 57.757 0.130 58.008

The timings grow significantly longer as the sizes of the vectors grow. So passing vectors of size 10,000 could take a very long time. While this blog isn’t specifically on performance let’s do a little bit more coding to get an idea about how poorly performing the convolution written in R is. We’ll use this for later comparison with the performance numbers resulting from the Fortran subroutine. Let’s write a wrapper function to the conr function. This will call conr with a variable x that represents the size of the vectors we wish to convolute. If you don’t understand exactly what is going on here don’t worry – just think of at as more exposure to the apply family of commands.

timewrap <- function(x) { times <- system.time(conr(rnorm(x),rnorm(x)))[3] return(c(size=x,times)) } # time the convolution for vectors of size 100,1000,2000, and 4000 (convtimes <- sapply(c(100,1000,2000,4000),timewrap)) [,1] [,2] [,3] [,4] size 100.000 1000.000 2000.000 4000.00 elapsed 0.033 3.552 13.811 62.15 # Let's plot this library(lattice) xyplot(convtimes[2,]~convtimes[1,],type=c("p","l","g"), xlab = "vector size", ylab = "elapsed time in seconds", main = "Execution times for Convolution in R", pch = 19)

How do we address this problem ? Well there are opportunities for improvement within the R code by using vectorization techniques. A good start would be to somehow avoid the second for loop and there are ways to do that. In fact there is a way to avoid both loops altogether and maybe we’ll explore such an approach in another post. But for now let’s see if writing the code in Fortran and then linking it in could help improve things. So here is the rewritten convolution algorithm, which we will save into a file called convolvef77.f

subroutine convolvef77 (x, lx, y, ly, xy) c c A basic implementation of convolution algorithm for two vectors c Here we assume that they are the same length just to simplify things c I use zero-based arrays here. c integer lx, ly, i, j double precision x(0:lx-1), y(0:ly-1), xy(0:lx+ly-2) do 20 i = 0, (lx-1) do 15 j = 0, (ly-1) xy(i+j) = xy(i+j) + x(i) * y(j) 15 continue 20 continue end # Save the above to a file called convolvef77.f # Now compile it to a shared library $ R CMD SHLIB convolvef77.f

Next we’ll write a function in R to call the convolvef77 function. Start up R.

convolvef77 <- function(x,y) { dyn.load('convolvef77.so') lx = length(x) ly = length(y) retdata <- .Fortran("convolvef77", x = as.double(x), lx = as.integer(lx), y = as.double(y), ly = as.integer(ly), xy = double(lx+ly-1))$xy return(retdata) } # Now let's throw some large vectors at it. Look at how much better the times are v1 = rnorm(4000); v2 = rnorm(4000) system.time(convolvef77(v1,v2)) user system elapsed 0.012 0.000 0.012 v1 = rnorm(8000); v2 = rnorm(8000) system.time(convolvef77(v1,v2)) user system elapsed 0.047 0.001 0.083

So the speed looks really good. So now let’s repeat the timing exercise we applied to the convolutions done in R.

timewrap <- function(x) { times <- system.time(convolvef77(rnorm(x),rnorm(x)))[3] return(c(size=x,times)) } (convtimes <- sapply(c(100,1000,2000,4000),timewrap)) [,1] [,2] [,3] [,4] size 100.000 1e+03 2e+03 4.0e+03 elapsed 0.045 2e-03 4e-03 1.3e-02 # Wow. This is FAST !!!!! Let's throw some bigger vectors at it. (convtimes <- sapply(c(100,1000,2000,4000,10000,20000,50000),timewrap)) [,1] [,2] [,3] [,4] [,5] [,6] [,7] size 1e+02 1e+03 2e+03 4.0e+03 1.0e+04 2.00e+04 50000.000 elapsed 1e-03 2e-03 4e-03 1.2e-02 7.2e-02 3.22e-01 2.074 # Plot the times xyplot(convtimes[2,]~convtimes[1,],type=c("p","l","g"), xlab = "vector size", ylab = "elapsed time in seconds", main = "Execution times for Convolution in Fortran", pch = 19)

So using the Fortran subroutine took 2.0 seconds to convolute vectors of size 50,000 whereas using native R code to do the convolution took 3.5 seconds for vectors of size 1,000 ! Thus using the Fortran code definitely helped speed things up. However, speed might not be the only reason you choose to link in Fortran code. For example I know of people who have written the bulk of their thesis analysis work using Fortran and now seek to leverage that effort within R. Sure, they could recode their stuff into R but that would probably result in lower performance results. Any time you have a significant body of work in one language you would like to avoid having to recode it in another. Lastly, there are other ways to bring in Fortran that I haven’t discussed here. The “inline” package allows one to compile fortran code inline within a given R program, which might be more appealing to some. Hope this has been helpful.

Filed under: Fortran, Performance, R programming apply lapply tapply, Uncategorized

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