I noticed Doug only circulated the following on S-news, but it may be of
interest to R users who don't follow S-news. His findings are certainly
consistent with my own, where my problems often force me into the
element by element type of situation.
Paul Gilbert
_______
At the risk of beating this example to death, I went back and compared
the execution time for the element-by-element method with that of the
indexing method. At vector sizes of 50000 the indexing method is
close to 500 times faster.
S-PLUS : Copyright (c) 1988, 1996 MathSoft, Inc.
S : Copyright AT&T.
Version 3.4 Release 1 for Sun SPARC, SunOS 5.3 : 1996
Working data will be in .Data
> x <- rnorm(50000)
> y <- rnorm(50000)
> op <- sample(c("*","+","-","/"), 50000, repl = T)
> foo <-
+ function(x, y, op)
+ {
+ z <- x
+ for(i in seq(along = op))
+ z[i] <- do.call(op[i], list(x[i], y[i]))
+ z
+ }
> unix.time(res1 <-
cbind(x*y,x-y,x+y,x/y)[cbind(seq(along=x),match(op,c("*", "-", "+",
"/")))])
[1] 0.40000010 0.04999995 0.00000000 0.00000000 0.00000000
> length(res1)
[1] 50000
> unix.time(res2 <- foo(x, y, op))
[1] 196.23 0.29 199.00 0.00 0.00
> length(res2)
[1] 50000
> all.equal(res1, res2)
[1] T
It is interesting that the difference in execution speed when using R is
much less than when using S-PLUS. On this machine (Sun UltraSparc 1
running Solaris 2.5.1) R is somewhat slower than S-PLUS for the
indexing version but close to 10 times faster for the
element-by-element version
R> unix.time(res1 <-
cbind(x*y,x-y,x+y,x/y)[cbind(seq(along=x),match(op,c("*", "-", "+",
"/")))])
[1] 1.55 0.07 2.00 0.00 0.00
R> unix.time(res2 <- foo(x, y, op))
[1] 23.96 0.05 24.00 0.00 0.00
R> all(res1 == res2)
[1] TRUE
--
Douglas Bates bates@stat.wisc.edu
Statistics Department 608/262-2598
University of Wisconsin - Madison
http://www.stat.wisc.edu/~bates/
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R-alpha: loop speed
2 messages · Paul Gilbert, Thomas Lumley
On Mon, 10 Nov 1997, Paul Gilbert wrote:
I noticed Doug only circulated the following on S-news, but it may be of interest to R users who don't follow S-news. His findings are certainly consistent with my own, where my problems often force me into the element by element type of situation.
It is also interesting to look at the allegedly realistic if extreme
benchmark program provided with Matt Calder's partial S compiler.
LMS <- function(M, N)
{
### Pre-allocate result and filter. ###
R <- matrix(0,nrow=M, ncol=5)
W <- rep(0,5)
for (i in 1:M) {
### Simulate MA(1) ###
Z <- rnorm(N+1)
X <- Z[2:(N+1)] + 0.5 * Z[1:N]
### Perform LMS ###
for (j in 5:N) {
U <- X[j:(j-4)]
E <- Z[j+1] - sum(W * U)
W <- W + 0.1* E * U
}
### Save final result ###
R[i,] <- W
}
return(R)
}
I used this in S, R and compiled to C on an Ultra 1 (Solaris) and in R and
compiled to C on a Pentium Pro 150 under Linux
The results are quite interesting. These are CPU times in seconds for a
fixed value of N (100, I believe, but it was some time ago)
Ultra Intel
M R C S R C
100 7.24 2.08 11 6.38 0.79
200 14.19 4.06 25.8 12.36 1.56
300 21.59 6.3 56.8 18.89 2.31
400 28.76 8.37 89 24.97 3.09
800 57.62 16.42 295 50.33 6.16
The time taken by R scales roughly linearly with M, S time grows much
faster. The two are comparable for small M. It's also interesting that the
Intel machine was slightly faster, though this doesn't translate to disk
and memory intensive tasks.
Thomas Lumley
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