I certainly think it would be wrong to declare that NDP is doomed to
failure... not because you would be making an enemy of SPJ (I'm pretty
sure you wouldn't!) but because it actually aims to solves a less
ambitious problem: the problem of parallelising the SAME task applied
to different data, rather than a collection of arbitrary tasks.
Because the task does not change, we know that e.g. taking half the
data cuts the amount of work in half. Therefore an up-front scheduling
approach can work.
If you fast forward to about 42 minutes into the London HUG video, you
see that Simon talks about the problem of parallelizing (f x) + (g y),
and says that he spent "quite a lot of time in the eighties trying to
make this game go fast [..] and the result was pretty much abject
You're absolutely right that a dynamic/adaptive approach is the only
one that will work when the tasks are of unknown size. Whether this
approach is as easy as you think is open for you to prove. I look
forward to testing your VM implementation, or at the very least
reading your paper on the subject ;-)
On 8/11/07, Hugh Perkins <hughperkins@xxxxxxxxx> wrote:
> On 8/11/07, Thomas Conway <drtomc@xxxxxxxxx> wrote:
> > There are many papers about this in the Parallel Logic Programming
> > area. It is commonly called "Embarrassing Parallelism".
> Ah, I wasnt very precise ;-) I didnt mean I dont understand the
> problem; I meant I dont understand why people think it is difficult to
> solve ;-) (And then I tried to explain by examples why it is easy,
> but it is true that my communication sucks ;-) )
> > you'll only get a benefit if you can break xs into chunks
> of e.g. 10^3 elements or more, and more like 10^5 or more for more
> usual 'f's.
> Actually, the number of elements is irrelevant. The only measure that
> is important is how long the function is taking to execute, and
> whether it is parellizable.
> Example, the following only has 3 elements but will take a while to run:
> strPrtLn $ sum $ map getNumberPrimes [10240000, 20480000, 40960000 ]
> The following has 10 million elements but runs quickly:
> strPrtLn $ sum $ map (+1) [1..10000000 ]
> In the second, we start the function running, in a single thread, and
> after a second, the function has already finished, so great! Were
> In the first, we start the function running, in a single thread.
> After a second the function is still running, so we look at what is
> taking the time and whether it is parallelizable.
> Turns out the vm has been chugging away on the map for the last
> second, and that that maps are parallelizeable, so we split the map
> into Math.Min( <numberelements>, <number cores>) pieces, which on a
> 1024-core machine, given we have 3 elements, is Math.Min( 1024, 3 ),
> which is 3.
> So, we assign each of the 3 elements of the map to a thread. So, now
> we're using 3 of our 64 cores.
> A second later, each thread is still chugging away at its element, so
> we think, ok, maybe we can parallelize more, because we still have 61
> threads/cores available, so now we look at the getNumberOfPrimes
> function itself, and continue the above type of analysis.
> This continues until all 64 cores have been assigned some work to do.
> Whenever a thread finishes, we look around for some work for it to do.
> If there is some, we assign it. If there isnt, we look around for an
> existing thread that is doing something parallelizeable, and split it
> into two pieces, giving one to the old thread, and one to the
> available thread.
> Not sure why this is perceived as difficult (added the word
> "perceived" this time ;-) ). I think the main barrier to
> understanding why it is easy is understanding that this needs to be
> done from a VM, at runtime. It is not possible to do it statically at
> compilation, but I really need to finish watching SPJ's video before
> declaring that SPJ's proposal is doomed to fail ;-) Not least,
> probably not good to make an enemy of SPJ ;-)
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