On Fri, 01 May 2009 21:14:54 -0400, Bill Baxter <[email protected]> wrote:
On Fri, May 1, 2009 at 5:36 PM, bearophile <[email protected]>
wrote:
Bill Baxter:
Much more often the discussion on the numpy list takes the form of
"how do I make this loop faster" becuase loops are slow in Python so
you have to come up with clever transformations to turn your loop into
array ops. This is thankfully a problem that D array libs do not
have. If you think of it as a loop, go ahead and implement it as a
loop.
Sigh! Already today, and even more tomorrow, this is often false for D
too. In my computer I have a cheap GPU that is sleeping while my D code
runs. Even my other core sleeps. And I am using one core at 32 bits
only.
You will need ways to data-parallelize and other forms of parallel
processing. So maybe nornmal loops will not cuti it.
Yeh. If you want to use multiple cores you've got a whole 'nother can
o worms. But at least I find that today most apps seem get by just
fine using a single core. Strange though, aren't you the guy always
telling us how being able to express your algorithm clearly is often
more important than raw performance?
--bb
Well, since I do GP-GPU work, the GPU algorithm is much algorithmically
cleaner than the CPU algorithm. :) But I do know that this is very
algorithm dependent.
