On Fri, May 1, 2009 at 5:36 PM, bearophile <bearophileh...@lycos.com> 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