(Forwarding to haskell-cafe)
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
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