My hunch is that Python and R run at about the same speed, and both
use C libraries for speedups (Python primarily via the numpy package).
That's not necessarily true. There can be enormous differences
between interpreted languages, and R appears to be a particularly slow
one (which doesn't usually matter, as well-written code will mostly
perform matrix operations).
I did run some simple benchmarks with "naive" loops such as this one
for (x in 1:N) {
sum <- sum + x
}
as well as function calls. I haven't tested Python yet, but in
generally it is considered to be roughly on par with Perl.
Here are results for the loop above:
R/simple_count.R 0.82 Mops/s (2000000 ops in 2.43 s)
perl/simple_count.perl 8.32 Mops/s (10000000 ops in 1.20 s)
(where Mops = million operations per second treats one loop iteration
as a single operation here). As you can see, Perl is about 10 times
as fast as R. The point is, however, that this difference may not be
worth the effort you spend re-implementing your algorithms in Python
or Perl and getting the Python/Perl interface for R up and running
(I've just about given up on RSPerl, since I simply can't get it to
install on my Mac in the way I need it).
The difference between R and Perl appears much less important if you
compare it to compiled C code:
C/simple_count.exe 820.86 Mops/s (500000000 ops in 0.61
s)
If you really need speed from an interpreted language, you could try
Lua:
lua/simple_count.lua 65.78 Mops/s (100000000 ops in 1.52
s)
(though you're going to lose much of this advantage as soon as you
include function calls, which have a lot of overhead in every
interpreted language.
Hope this helps,
Stefan
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