Converting C code to C++ code: An example from plyr
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
The plyr package uses a couple of small C functions to optimise a number of particularly bad bottlenecks. Recently, two functions were converted to C++. This was mostly stimulated by a segmentation fault caused by some large inputs to the split_indices()
function: rather than figuring out exactly what was going wrong with the complicated C code, it was easier to rewrite with simple, correct C++ code.
The job of split_indices()
is simple: given a vector of integers, x
, it returns a list where the i
-th element of the list is an integer vector containing the positions of x
equal to i
. This is a useful building block for many of the functions in plyr.
It is fairly easy to see what is going on the in the C++ code:
#include <Rcpp.h> using namespace Rcpp; // [[Rcpp::exports]] std::vector<std::vector<int> > split_indices(IntegerVector x, int n = 0) { if (n < 0) stop("n must be a positive integer"); std::vector<std::vector<int> > ids(n); int nx = x.size(); for (int i = 0; i < nx; ++i) { if (x[i] > n) { ids.resize(x[i]); } ids[x[i] - 1].push_back(i + 1); } return ids; }
-
We create a
std::vector
of integers calledout
. This will grow efficiently as we add new values, and Rcpp will automatically convert to a list of integer vectors when returned to R. -
The loop iterates through each element of
x
, adding its index to the end ofout
. It also makes sure thatout
is long enough. (The plus and minus ones are needed because C++ uses 0 based indices and R uses 1 based indices.)
The code is simple, easy to understand (if one is a little familiar with the STL), and performant. Compare it to the original C code:
#include <R.h> #include <Rdefines.h> SEXP split_indices(SEXP group, SEXP n) { SEXP vec; int i, j, k; int nlevs = INTEGER(n)[0]; int nobs = LENGTH(group); int *pgroup = INTEGER(group); // Count number of cases in each group int counts[nlevs]; for (i = 0; i < nlevs; i++) counts[i] = 0; for (i = 0; i < nobs; i++) { j = pgroup[i]; if (j > nlevs) error("n smaller than largest index"); counts[j - 1]++; } // Allocate storage for results PROTECT(vec = allocVector(VECSXP, nlevs)); for (i = 0; i < nlevs; i++) { SET_VECTOR_ELT(vec, i, allocVector(INTSXP, counts[i])); } // Put indices in groups for (i = 0; i < nlevs; i++) { counts[i] = 0; } for (i = 0; i < nobs; i++) { j = pgroup[i] - 1; k = counts[j]; INTEGER(VECTOR_ELT(vec, j))[k] = i + 1; counts[j]++; } UNPROTECT(1); return vec; }
This function is almost three times as long, and has a bug in it. It is substantially more complicated because it:
-
has to take care of memory management with
PROTECT
andUNPROTECT
; Rcpp takes care of this for us -
needs an additional loop through the data to determine how long each vector should be; the
std::vector
grows efficiently and eliminates this problem
Conversion to C++ can make code shorter and easier to understand and maintain, while remaining just as performant.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.