R Language Speeding up tough-to-vectorize code Speeding tough-to-vectorize for loops with Rcpp
Example
Consider the following tough-to-vectorize for loop, which creates a vector of length len where the first element is specified (first) and each element x_i is equal to cos(x_{i-1} + 1):
repeatedCosPlusOne <- function(first, len) {
x <- numeric(len)
x[1] <- first
for (i in 2:len) {
x[i] <- cos(x[i-1] + 1)
}
return(x)
}
This code involves a for loop with a fast operation (cos(x[i-1]+1)), which often benefit from vectorization. However, it is not trivial to vectorize this operation with base R, since R does not have a "cumulative cosine of x+1" function.
One possible approach to speeding this function would be to implement it in C++, using the Rcpp package:
library(Rcpp)
cppFunction("NumericVector repeatedCosPlusOneRcpp(double first, int len) {
NumericVector x(len);
x[0] = first;
for (int i=1; i < len; ++i) {
x[i] = cos(x[i-1]+1);
}
return x;
}")
This often provides significant speedups for large computations while yielding the exact same results:
all.equal(repeatedCosPlusOne(1, 1e6), repeatedCosPlusOneRcpp(1, 1e6))
# [1] TRUE
system.time(repeatedCosPlusOne(1, 1e6))
# user system elapsed
# 1.274 0.015 1.310
system.time(repeatedCosPlusOneRcpp(1, 1e6))
# user system elapsed
# 0.028 0.001 0.030
In this case, the Rcpp code generates a vector of length 1 million in 0.03 seconds instead of 1.31 seconds with the base R approach.
