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There was another question recently on StackOverflow that I had meant to discuss in a follow-up post here. User deltanovember asked about slow recursive functions and used the very classic Fibonacci number as an example. To recap, Fibonacci number are defined with two initial values F(0) = 0 and F(1) = 1; thereafter the Fibonacci number F(n) is defined as the sum of the two preceding numbers: F(n) = F(n-2) + F(n-1).

This leads to very straightforward implementations using recursion:

## R implementation of recursive Fibonacci sequence
fibR <- function(n) {
if (n == 0) return(0)
if (n == 1) return(1)
return (fibR(n - 1) + fibR(n - 2))
}


Unfortunately, this elegant implementation which remain close to the abtract formulation of the recurrence algorithm performs very poorly in R as there is noticeable overhead in function calls which becomes dominant in a recursion. This lead to the original question on StackOverflow, and the accepted answer uses a trick presented by Pat Burns in his lovely R Inferno: rewrite the solution using a computer science trick called memoization:

fibonacci <- local({
memo <- c(1, 1, rep(NA, 100))
f <- function(x) {
if(x == 0) return(0)
if(x < 0) return(NA)
if(x > length(memo))
stop("'x' too big for implementation")
if(!is.na(memo[x])) return(memo[x])
ans <- f(x-2) + f(x-1)
memo[x] <<- ans
ans
}
})


That is a fair answer, and even more was suggested with a link to a terrific analysis calling the Fibonacci recurrence the worst algorithm in the world. That is also fair, but all the basic research into better algorithms exploiting some structure of the problem to advance performance (and of course understanding) is overlooking one crucial part: algorithm analysis is essentially independent of the language. So whatever improvements we obtain by thinking really hard about a problem are then available for other implementations too.

So with a tip of the hat to the old Larry Wall quote about Lazyness, Impatience and Hubris, I would like to suggest what I consider a much simpler route to much better performance: recode it in C++ using both Rcpp (for the R/C++ integration) and inline for the on-the-fly compilation, linking and loading of C++ code into R.

## inline to compile, load and link the C++ code
require(inline)

## we need a pure C/C++ function as the generated function
## will have a random identifier at the C++ level preventing
## us from direct recursive calls
incltxt <- '
int fibonacci(const int x) {
if (x == 0) return(0);
if (x == 1) return(1);
return (fibonacci(x - 1)) + fibonacci(x - 2);
}'

## now use the snippet above as well as one argument conversion
## in as well as out to provide Fibonacci numbers via C++
fibRcpp <- cxxfunction(signature(xs="int"),
plugin="Rcpp",
incl=incltxt,
body
='
int x = Rcpp::as<int>(xs);
return Rcpp::wrap( fibonacci(x) );
')


This single R function call cxxfunction() takes the code embedded in the arguments to the body variable (for the core function) and the incltxt variable for the helper function we need to call. This helper function is needed for the recursion as cxxfunction() will use an randomized internal identifier for the function called from R preventing us from calling this (unknown) indentifier. But the rest of the algorithm is simple, and as beautiful as the initial recurrence. Three lines, three statements, and three cases for F(0), F(1) and the general case F(n) solved by recursive calls. This also illustrates how easy it is to get an integer from R to C++ and back: the as and wrap simply do the right thing converting to and from the SEXP types used internally by the C API of R.

A performance comparison of the basic R version fibR, its byte-compiled variant fibRC and and the C++ version fibRcpp shown above is very compelling. We have added a file fibonacci.r to the large and still growing set of examples included with Rcpp, and we can just execute that script with Rscript or (as here) r from the littler package:

[email protected]:~/svn/rcpp/pkg/Rcpp/inst/examples/Misc$r fibonacci.r Loading required package: inline Loading required package: methods Loading required package: compiler test replications elapsed relative user.self sys.self 3 fibRcpp(N) 1 0.092 1.0000 0.09 0.00 2 fibRC(N) 1 61.480 668.2609 61.47 0.00 1 fibR(N) 1 61.877 672.5761 61.83 0.02 [email protected]:~/svn/rcpp/pkg/Rcpp/inst/examples/Misc$

So the recursion for the original argument of N=35 takes just over a minute at about 61.5 and 61.9 seconds, respectively, for the R version and its byte-compiled variant (as per the column titled elapsed). So byte-compilation essentially offers no help for the bottleneck of slow function calls.

The C++ versions relying on Rcpp which created in a few lines of code and a single call to cxxfunction however takes just 92 milliseconds---or a relative gain of well over six-hundred times.

That provides another nice demonstration of what Rcpp can do. Improved algorithms for well-understood problems are surely one way to accelerate solutions. But there are (many ?) times when we do not have the luxury of being able to think through to a new and improved approach. Or worse, such an approach may even introduce new errors or inaccurracies if we get it wrong on a first try. With Rcpp, we are able to the express the problem as written in its original statement: a simple recursion. The gain relative to a slow R implementation is noteworthy---and could of course be improved further if we really needed to by relying on better algorithms like memoization. But for day to day tasks, I gladly take speedups of (up to) a few hundred times thanks to Rcpp without having to do hard algorithmic work.

Before closing, a quick reminder that I will be giving two classes on Rcpp in a few weeks. These will be in New York on September 24, and San Franciso on October 8, see this blog post as well as this page at Revolution Analytics (who are a co-organiser of the classes) for details and registration information.