Thank you Stefan. That's really interesting.
My guess is that Python is not much more complicated to program than R, and
that we can integrate some codes into R. If it can be 10 times faster,
that's great !
Best,
Jean
2009/11/21 Stefan Evert <stefan.ev...@uos.de>
> 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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