Hi,

I have a program that computes a matrix of Floats of m rows by n columns. Computing each Float is relatively expensive. Each line is completely independent of the others, so I thought I'd try some simple SMP parallelism on this code:

myFun :: FilePath -> IO ()
myFun fp =
do fs <- readDataDir fp
     let process f = readFile' f >>= parse
         printLine = putStrLn . foldr (\a b -> show a ++ "\t" ++ b) ""
         runDiff l = [ [ diff x y | y <- l ]
                     | (x,i) <- zip l (map getId fs), myFilter i ]
     ps <- mapM process fs
     sequence_ [ printLine x | x <- runDiff ps `using` parList rdeepseq ]

So, I'm using parList to evaluate the rows in parallel, and fully evaluating each row. Here are the timings on a Dual Quad Core AMD 2378 @2.4 GHz, ghc-6.12.3, parallel-2.2.0.1:

-N      time (ms)
none    1m50
2       1m33
3       1m35
4       1m22
5       1m11
6       1m06
7       1m45

The increase at 7 is justified by the fact that there were two other processes running. I don't know how to justify the small increase at N3, though, but that doesn't matter too much. The problem is that I am not getting the gains I expected (halving at N2, a third at N3, etc.). Is this the best one can achieve with this implicit parallelism, or am I doing something wrong? In particular, is the way I'm printing the results at the end destroying potential parallel gains?

Any insights on this are appreciated.

Thanks,
Pedro