Hi all,
The rcpp-devel list has been very helpful to me so far, so I hope you
don't mind another question! I'm trying to speed up my 2d convolution
function:
library(inline)
# 2d convolution -------------------------------------------------------------
convolve_2d <- cxxfunction(signature(sampleS = "numeric", kernelS =
"numeric"), plugin = "Rcpp", '
Rcpp::NumericMatrix sample(sampleS), kernel(kernelS);
int x_s = sample.nrow(), x_k = kernel.nrow();
int y_s = sample.ncol(), y_k = kernel.ncol();
Rcpp::NumericMatrix output(x_s + x_k - 1, y_s + y_k - 1);
for (int row = 0; row < x_s; row++) {
for (int col = 0; col < y_s; col++) {
for (int i = 0; i < x_k; i++) {
for (int j = 0; j < y_k; j++) {
output(row + i, col + j) += sample(row, col) * kernel(i, j);
}
}
}
}
return output;
')
x <- diag(1000)
k <- matrix(runif(20* 20), ncol = 20)
system.time(convolve_2d(x, k))
# user system elapsed
# 14.759 0.028 15.524
I have a vague idea that to get better performance I need to get
closer to bare pointers, and I need to be careful about the order of
my loops to ensure that I'm working over contiguous chunks of memory
as often as possible, but otherwise I've basically exhausted my
C++/Rcpp knowledge. Where should I start looking to improve the
performance of this function?
The example data basically matches the real problem - x is not usually
going to be much bigger than 1000 x 1000 and k typically will be much
smaller. (And hence, based on what I've read, this technique should
be faster than doing it via a discrete fft)
Hadley
--
Assistant Professor / Dobelman Family Junior Chair
Department of Statistics / Rice University
http://had.co.nz/
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