In an oddly-titled post earlier today I’d had too much coffee, and we looked at how compiled Haskell code smokes out interpreted code (for various reasons, mostly to do with being compiled and not interpreted). However, the real point of the article (other than to burn things with my flame thrower) was to start to explore the new parallelism annotations in Haskell.

So let’s continue that, with some more explorations of how far we can get with parallel annotations, and whether Haskell can compete for C, given enough cores. (And I’d just like to note that Spencer Janssen (of the xmonads) contributed most of the code and ideas for this article :).

We’ll stick to the the naive fibonacci implementation, but switch to a 4 core machine, and see how far we can scale up the Haskell code as we add cores, without resorting to manual parallel programming.

So, the naive Haskell, but we’ll compute to a reasonable size of N:

fib :: Int -> Int fib 0 = 0 fib 1 = 1 fib n = fib (n-1) + fib (n-2) main = forM_ [0..45] $ \i -> printf "n=%d => %d\n" i (fib i)

All very pretty. Now, if we run it on a 4 core, 3Ghz linux machine, will it use those resources?

$ ghc-6.8.1 -O2 Naive.hs -o naive --make $ time ./naive n=0 => 0 n=1 => 1 n=2 => 1 ... n=44 => 701408733 n=45 => 1134903170 ./naive 39.03s user 0.00s system 99% cpu 39.108 total

99%. So the answer is no: GHC doesn’t magically parallelise the naive code (nor memoise it, guys…). Remember that number: 39s. That’s our goal to beat by using the extra cores.

However, we can give the compiler some hints about how best to parallelise the code, using the lovely `par` annotation (from Control.Parallel) (originally from this paper). From the manual:

The expression (x `par` y) sparks the evaluation of x (to weak head normal form) and returns y. Sparks are queued for execution in FIFO order, but are not executed immediately. If the runtime detects that there is an idle CPU, then it may convert a spark into a real thread, and run the new thread on the idle CPU. In this way the available parallelism is spread amongst the real CPUs.

So let’s naively annotate this. Just split the tree into two parts, and run the first branch in parallel with the other, hoping that it finishes about the same time as the second, so there’s no waiting:

import Control.Parallel import Control.Monad import Text.Printf fib :: Int -> Int fib 0 = 0 fib 1 = 1 fib n = r `par` (l `pseq` l+r) where l = fib (n-1) r = fib (n-2) main = forM_ [0 .. 45] $ \i -> printf "n=%d => %d\n" i (fib i)

Let’s compile this with some reasonable flags:

$ ghc-6.8.1 NaivePar.hs -O2 -o np --make -threaded

And we can toss two cores at it to start with:

./np +RTS -N2 138.69s user 1.18s system 190% cpu 72.48 total

Ok, how about 3, or 4 cores?

./np +RTS -N3 160.39s user 1.98s system 261% cpu 62.15 total ./np +RTS -N4 167.61s user 2.26s system 311% cpu 54.53 total

Hmm, interesting! While the cpus are getting utilised, we’re not making much progress towards our naive single core goal. What is going on?

The problem, of course, is that we’re wasting time registering thread sparks for very small expressions (anything under about N=35 or so). We should really not use `par` for those little jobs, since the cost of registering a thread spark outweighs the cost of just evaluating it here and now.

So what we can do is use the `par` version when N is larger, and drop back to straight line code for smaller jobs. That should do the trick.

import Control.Parallel import Control.Monad import Text.Printf cutoff = 35 fib' :: Int -> Integer fib' 0 = 0 fib' 1 = 1 fib' n = fib' (n-1) + fib' (n-2) fib :: Int -> Integer fib n | n < cutoff = fib' n | otherwise = r `par` (l `pseq` l + r) where l = fib (n-1) r = fib (n-2) main = forM_ [0..45] $ \i -> printf "n=%d => %d\n" i (fib i)

So that’s the same algorithm, just split into two loops, once of which creates thread sparks. Now, we can try this with a couple of cores:

$ ghc-6.8.1 Par.hs -O2 -o real-par --make -threaded $ time ./real-par +RTS -N2 n=0 => 0 n=1 => 1 n=2 => 1 ... n=44 => 701408733 n=45 => 1134903170 ./real-par +RTS -N2 71.98s user 0.49s system 191% cpu 37.866 total

Ok. Cool, with two cores, and the `par` overhead, we’re actually beating one core now. How about 3?

./real-par +RTS -N3 75.03s user 0.82s system 262% cpu 28.854 total

Excellent. And how about the lot?

./real-par +RTS -N4 76.81s user 0.75s system 351% cpu 22.059 total

Haskell FTW! So that’s scaling up enough for now, and, considering the effort involved to parallelise it, I’m more than happy with that result.

This is, as far as I’m aware, the lightest weight parallelism mechanism in any mainstream language. And the magical thing is that we parallelised our code without ever worrying about synchronisation, communication, race conditions, dead locks, live locks. semaphores, mutexes…

The other interesting thing to think about: at what point do we beat the same algorithm in C, and how hard would it be to parallelise the algorithm in C with pthreads… I’m not going to attempt the latter, but we can check the former:

#include #include long long fib(long long n) { if (n < 2) { return 1; } return fib(n - 2) + fib(n - 1); } int main(int argc, char ** argv) { long long n = 0; for (n = 0; n <= 45; n++) { printf("Fib(%lld): %lld\n", n, fib(n)); } return 0; }

Compiled with:

$ gcc -O3 par.c -o par-c

And running it:

$ time ./par-c Fib(0): 1 Fib(1): 1 ... Fib(43): 701408733 Fib(44): 1134903170 ./par-c 32.91s user 0.00s system 99% cpu 32.960 total

32s. Wow! So with an off-the-shelf Linux box, you can write simple (but parallel) Haskell will outperform gcc’s best efforts by a good margin — today! Multicore programming just got a lot easier.