Avoid two for loops in R

Question

I have a R code that can do convolution of two functions...

convolveSlow <- function(x, y) {  
nx <- length(x); ny <- length(y)  
xy <- numeric(nx          


        

Answer 1

Since R is very fast at computing vector operations, the most important thing to keep in mind when programming for performance is to vectorise as many of your operations as possible.

This means thinking hard about replacing loops with vector operations. Here is my solution for fast convolution (50 times faster with input vectors of length 1000 each):

convolveFast <- function(x, y) {
    nx <- length(x)
    ny <- length(y)
    xy <- nx + ny - 1
    xy <- rep(0, xy)
    for(i in (1:nx)){
        j <- 1:ny
        ij <- i + j - 1
        xy[i+(1:ny)-1] <- xy[ij] + x[i] * y
    }
    xy
}

You will notice that the inner loop (for j in ...) has disappeared. Instead, I replaced it with a vector operation. j is now defined as a vector (j <- 1:ny). Notice also that I refer to the entire vector y, rather than subsetting it (i.e. y instead of y[j]).

j <- 1:ny
ij <- i + j - 1
xy[i+(1:ny)-1] <- xy[ij] + x[i] * y

I wrote a small function to measure peformance:

measure.time <- function(fun1, fun2, ...){
    ptm <- proc.time()
    x1 <- fun1(...)
    time1 <- proc.time() - ptm

    ptm <- proc.time()
    x2 <- fun2(...)
    time2 <- proc.time() - ptm

    ident <- all(x1==x2)

    cat("Function 1\n")
    cat(time1)
    cat("\n\nFunction 2\n")
    cat(time2)
    if(ident) cat("\n\nFunctions return identical results")

}

For two vectors of length 1000 each, I get a 98% performance improvement:

x <- runif(1000)
y <- runif(1000)
measure.time(convolveSlow, convolveFast, x, y)

Function 1
7.07 0 7.59 NA NA

Function 2
0.14 0 0.16 NA NA

Functions return identical results