[R] Speeding up "accumulation" code in large matrix calc?

[robertfeldt]

Wow! Thanks to both Petr and Berend for this extensive help. 

I learned a lot not only about this specific case but about R in general
from studying your answers. 

The compiled version t4 seems to give the most consistently quickest results
and for my case (about 6000 rows and 500 columns with a probability of the
sought for outcome 0.04) I see speedups from my original of 30-40 times. See
below for details.

Excellent help thank you!

# t1-t4 as above in thread and then compiled to t1.c-t4.c ...
random_matrix <- function(nrows, ncols, probabilityOfOne) {
	matrix((runif(nrows*ncols)<probabilityOfOne)+0, nrow=nrows);
}

library(benchmark)
compare.exec.times <- function(A) {
	benchmark(t1(A,outcome=1), 
	            t2(A,outcome=1), 
	            t3(A,outcome=1), 
		    t4(A,outcome=1), 
	            t1.c(A,outcome=1), 
	            t2.c(A,outcome=1), 
	            t3.c(A,outcome=1), 
	            t4.c(A,outcome=1), 
	            columns=c("test", "user.self", "relative"), 
	            replications=3)
}

compare.exec.times(random_matrix(100, 1000, 0.10)) # t4.c quickest, 47 times
speedup
compare.exec.times(random_matrix(1000, 100, 0.10)) # t4.c quickest, 25 times
speedup
compare.exec.times(random_matrix(1000, 1000, 0.10)) # t4.c quickest, 37
times speedup
# Most realistic for my data:
compare.exec.times(random_matrix(6000, 400, 0.04)) # t4.c quickest,  30
times speedup
                  test user.self  relative
1   t1(A, outcome = 1)    35.372 30.145038
5 t1.c(A, outcome = 1)     8.591  7.329092
2   t2(A, outcome = 1)    14.598 12.761662
6 t2.c(A, outcome = 1)    14.413 12.587786
3   t3(A, outcome = 1)     1.579  1.743851
7 t3.c(A, outcome = 1)     1.608  1.818490
4   t4(A, outcome = 1)     1.092  1.120441
8 t4.c(A, outcome = 1)     0.894  1.000000

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