You guys might also want to take a look at the Cilk programming language, and how it managed threads. If you know C, learning Cilk is about 2 hours of work, as it's C with half a dozen extra keywords and a few new concepts. I'd love to see Cilk - C + Haskell as a programming language.
The key idea of Cilk is that it's easier to deparallelize than it is to parallelize, especially automatically. So the idea is that the program is written incredibly parallel, with huge numbers of microthreads, which are (on average) very cheap to spawn. The runtime then deparallelizes the microthreads, running multiple microthreads sequentially within a single real thread (a worker thread). Microthreads that live their entire life within a single real thread are cheap to spawn (as in "not much more expensive than a normal function call" cheap).
The problem that Cilk runs into is that it's, well, C. It doesn't deal with contention at well at all- a single microthread blocking blocks the whole worker thread- meaning, among other things, that you can have "false deadlocks", where one microthread blocks on another microthread in the same real thread, and thus acts like it's deadlocked even though it really isn't. You have greatly increased the likelyhood of raceconditions as well (mutable data and multithreading just don't mix). Plus you have all the normal fun you have with C bugs- wild pointers, buffer over runs, etc.
All of which you avoid if you replace the C aspects of Cilk with Haskell. With STM you avoid the deadlock problem entirely, and with immutable data (except for carefully coded monads) you avoid the whole race condition problem. Plus all the normal advantages Haskell has over C.
Just my $0.02. Brian On Sat, 11 Aug 2007, Neil Bartlett wrote:
Hugh, 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 failure". 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 ;-) Regards, Neil On 8/11/07, Hugh Perkins <[EMAIL PROTECTED]> wrote:On 8/11/07, Thomas Conway <[EMAIL PROTECTED]> 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 chunksof 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 done! 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 ;-) _______________________________________________ Haskell-Cafe mailing list Haskell-Cafe@haskell.org http://www.haskell.org/mailman/listinfo/haskell-cafe_______________________________________________ Haskell-Cafe mailing list Haskell-Cafe@haskell.org http://www.haskell.org/mailman/listinfo/haskell-cafe
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