Stefan Evert wrote:
Sure, badly written R code does not perform as well as well written
python code or C code. On the other hand badly written python code
does not perform as well as well written R code.
What happens when you try one of these :
sum <- sum( 1:N )
R runs out of memory and crashes. :-) I didn't tell you how big N is,
did I?
Really?
N <- 1e30
sum( 1:N )
Error in 1:N : result would be too long a vector
-Peter Ehlers
>
But this is exactly the point I was trying to make (but perhaps not
prominently enough). In many cases, you can vectorize at least parts of
your code or find a more efficient algorithm, which may be faster in R
than a brute-force solution in C. But sometimes, you just cannot avoid
loops (let's not forget that all the forms of apply() are just loops and
don't give much of a speed benefit over a for-loop), function calls,
etc.; in this case, performance differences between interpreted
languages can matter.
Personally, I'd never switch from R to Perl just for speed, though.
BTW, I also tried a vectorised algorithm in R, which calculates the sum
above in a small number of chunks:
N1 <- 50
N2 <- 1000000
N <- N1 * N2
sum <- 0
for (i in 1:N1) {
x <- as.numeric(i-1) * N2 + 1:N2
sum <- sum + sum(x)
}
which gives
R/simple_count_vec.R 31.30 Mops/s (50000000 ops in 1.60 s)
So an interpreted loop in Lua is still faster than this partially
vectorized code in R:
lua/simple_count.lua 65.78 Mops/s (100000000 ops in 1.52 s)
As people on the SQLite mailing list always say: there's no general
answer as to which language/implementation/query/... is faster and
better. You just have to test the different options for your specific
application setting, and be prepared for one or two surprises.
Just in case this isn't obvious: If I rewrote matrix multiplication in C
and linked this code into R, it would run much slower than if I just
typed "A %*% B".
All the best,
Stefan
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