The other thing here is that he's using unboxed, nested arrays in C, while using naive lists in Haskell. To actually compare them, you'd need to use nested STUArrays. Hopefully we'll have a library for these soon (as a result of the ndp lib). Otherwise, use on one of the matrix libraries (hmatrix/ gslhaskell) For non-nested arrays, we can do rather well with: import Data.Array.Vector n :: Int n = 4000 main = print (sumU (zipWithU (*) a b)) where a = replicateU n (2::Double) b = mapU realToFrac $ enumFromToU 0 (n-1) Which compiles to some nicely fused unboxed loops. The trick is to get this working with nested arrays. The ndp library looks like our best bet here: darcs.haskell.org/packages/ndp -- Don tim:

On Thu, 24 Apr 2008 04:01:50 Tillmann Vogt wrote:

...

Hi,

I am currently experimenting with parallelizing C-programs. I have therefore written a matrix vector multiplication example that needs 13 seconds to run (5 seconds with OpenMP). Because I like Haskell I did the same in this language, but it takes about 134 seconds. Why is it so slow? Does someone have an idea?

module Main where

main = do putStrLn (show (stupid_mul 100)) putStrLn "100 multiplications done"

stupid_mul 0 = [] stupid_mul it = (s_mul it) : stupid_mul (it-1) -- without "it" after s_mul only one multiplication is executed

s_mul it = mul (replicate 4000 [0..3999]) (replicate 4000 2)

mul :: [[Double]] -> [Double] -> [Double] mul [] _ = [] mul (b:bs) c | sp==0 = sp : (mul bs c) -- always false, force evaluation

| otherwise = (mul bs c)

where sp = (scalar b c)

scalar :: [Double] -> [Double] -> Double scalar _ [] = 0 scalar [] _ = 0 scalar (v:vs) (w:ws) = (v*w) + (skalar vs ws)

and here the C-program

#include <stdio.h> #include <stdlib.h> #include <time.h>

#define M 4000 #define N 4000 #define IT 100

double a[M], b[M][N], c[N];

int main(int argc, char *argv[]) { double d; int i, j, l; time_t start,end;

printf("Initializing matrix B and vector C\n"); for(j=0; j<N; j++) c[j] = 2.0; for(i=0; i<M; i++) for(j=0; j<N; j++) b[i][j] = j;

printf("Executing %d matrix mult. for M = %d N = %d\n",IT,M,N); time (&start);

for(l=0; l<IT; l++)

#pragma omp parallel for default(none) \ shared(a,b,c) private(i,j,l)

for(i=0; i<M; i++) { a[i] = 0.0; for (j=0; j<N; j++) a[i] += b[i][j]*c[j]; } time (&end);

d = difftime (end,start); printf ("calculation time: %.2lf seconds\n", d ); return 0; } _______________________________________________ Haskell mailing list Haskell@haskell.org http://www.haskell.org/mailman/listinfo/haskell

You haven't even told us which compilers you're using so it's pretty difficult to do. I can't even get your code to compile - there are typos in it so you've obviously altered it since compiling yourself.

While this program may be wrong, I've dashed off this attempt that takes about 1.3 seconds on my not-too-powerful machine:

module Main where

import Control.Monad

rows = 4000 cols = 4000 iterations = 100

main = do let vector :: [Double] vector = replicate cols 2.0 matrix :: [[Double]] matrix = replicate rows (map fromIntegral [0..cols-1]) a = map (sum . (zipWith (*) vector)) matrix replicateM_ iterations (putStrLn (show a))

Those who understand how Haskell programs are executed will now be screaming "cheating!". This program when optimised by GHC (which I use) will only actually do the calculation once and print it 100 times. That is, after all, the same output you asked for. It may even be taking more shortcuts using identities around map and replicate but I'm not sure.

When mapping imperative languages to functional ones a little understanding of how it is executed goes a long way. Performance of your programs will benefit immensely if you know how your program will be run. I'm new to Haskell but have already realised that performance can be altered by orders of magnitude by making possible optimisations more visible to the compiler with how things are set out.

P.S. I would really recommend increasing use of higher level functions such as map. They make code much more readable and the most common also receive special optimisations from many compilers.

Cheers,

Tim _______________________________________________ Haskell mailing list Haskell@haskell.org http://www.haskell.org/mailman/listinfo/haskell