Prof Brian Ripley wrote:
On Thu, 27 Jan 2005, Paul Gilbert wrote:
A few weeks ago I noticed
z <- matrix(rnorm(20000),10000,2)
system.time(for (i in 1:1000) apply(z,2,sum))
[1] 13.44 0.48 14.08 0.00 0.00
system.time(for (i in 1:1000) rep(1,10000) %*% z)
[1] 6.46 0.11 6.84 0.00 0.00
So both run in a few milliseconds for rather large problems.
which seemed completely contrary to all my childhood teachings. Now
system.time(for (i in 1:1000) crossprod(rep(1,10000), z))
[1] 1.90 0.12 2.24 0.00 0.00
makes sense because it is suppose to be faster than %*% , but why is apply so slow?
`so slow' sic: what are you going to do in the 7ms you saved?
Yes, I think I've spent more time checking this than I will ever save.
(And should I go back and change apply in my code everywhere or is this likely to reverse again?)
It's not likely. apply is an R-level loop, and %*% is a C-level one.
However, %*% is not supposed to be much slower than crossprod, and the devil is in the details of how the BLAS is implemented: the code is very similar.
That %*% was faster than apply has been true in all my (17 years) of S/R experience. Your childhood may predate S3, of course.
I didn't say the teachings were correct. I clearly should have questioned some things sooner.
I still think one should use row/colSums for clarity with acceptable
efficiency. It must be very unusual for these operations to be a dominant part of a calculation, so let's not lose proportion here.
Agreed. I think I like the clarity reason best.
Thanks for the explanation.
Paul
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