RFC: naming for FrontTransversal and Transversal ranges

Don nospam at nospam.com
Sat May 2 01:17:29 PDT 2009

Bill Baxter wrote:
> On Fri, May 1, 2009 at 5:36 PM, bearophile <bearophileHUGS at 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

I confess to being mighty skeptical about the whole multi-threaded, 
multi-core thing. I think we're going to find that there's only two 
practical uses of multi-core:
(1) embarressingly-parallel operations; and
(2) process-level concurrency.
I just don't believe that apps have as much opportunity for parallelism 
as people seem to think. There's just too many dependencies.
Sure, you can (say) with a game, split your AI into a seperate core from 
your graphics stuff, but that's only applicable for 2-4 cores. It 
doesn't work for 100+ cores.

(Which is why I think that broadening the opportunity for case (1) is 
the most promising avenue for actually using a host of cores).

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