Straight, curly, or compiled?

Christian Robert, whose blog I commented-on here once before, had followed up on a recent set of posts by Radford Neal which had appeared both on Radford’s blog and on the r-devel mailing list.

Now, let me prefix this by saying that I really enjoyed Radford’s posts. He obviously put a lot of time into finding a number of (all somewhat small in isolation) inefficiencies in R which, when taken together, can make a difference in performance. I already spotted one commit by Duncan in the SVN logs for R so this is being looked at.

Yet Christian, on the other hand, goes a little overboard in bemoaning performance differences somewhere between ten and fifteen percent — the difference between curly and straight braces (as noticed in Radford’s first post). Maybe he spent too much time waiting for his MCMC runs to finish to realize the obvious: compiled code is evidently much faster.

And before everybody goes and moans and groans that that is hard, allow me to just interject and note that it is not. It really doesn’t have to be. Here is a quick cleaned up version of Christian’s example code, with proper assigment operators and a second variable x. We then get to the meat and potatoes and load our Rcpp package as well as inline to define the same little test function in C++. Throw in rbenchmark which I am becoming increasingly fond of for these little timing tests, et voila, we have ourselves a horserace:

# Xian's code, using <- for assignments and passing x down
f <- function(n, x=1) for (i in 1:n) x=1/(1+x)
g <- function(n, x=1) for (i in 1:n) x=(1/(1+x))
h <- function(n, x=1) for (i in 1:n) x=(1+x)^(-1)
j <- function(n, x=1) for (i in 1:n) x={1/{1+x}}
k <- function(n, x=1) for (i in 1:n) x=1/{1+x}

# now load some tools
library(Rcpp)
library(inline)

# and define our version in C++
l <- cxxfunction(signature(ns="integer", xs="numeric"),
                 'int n = as<int>(ns); double x=as<double>(xs);
                  for (int i=0; i<n; i++) x=1/(1+x);
                  return wrap(x); ',
                 plugin="Rcpp")

# more tools
library(rbenchmark)

# now run the benchmark
N <- 1e6
benchmark(f(N, 1), g(N, 1), h(N, 1), j(N, 1), k(N, 1), l(N, 1),
          columns=c("test", "replications", "elapsed", "relative"),
          order="relative", replications=10)

And how does it do? Well, glad you asked. On my i7, which the other three cores standing around and watching, we get an eighty-fold increase relative to the best interpreted version:

/tmp$ Rscript xian.R
Loading required package: methods
     test replications elapsed relative
6 l(N, 1)           10   0.122    1.000
5 k(N, 1)           10   9.880   80.984
1 f(N, 1)           10   9.978   81.787
4 j(N, 1)           10  11.293   92.566
2 g(N, 1)           10  12.027   98.582
3 h(N, 1)           10  15.372  126.000
/tmp$ 

So do we really want to spend time arguing about the ten and fifteen percent differences? Moore’s law gets you those gains in a couple of weeks anyway. I’d much rather have a conversation about how we can get people speed increases that are orders of magnitude, not fractions. Rcpp is one such tool. Let’s get more of them.