I have a very simple piece of code in Haskell and Scala. This code is intended to run in a very tight loop so performance matters. The problem is that Haskell is about 10x slower than Scala. Here it is Haskell code.
{-# LANGUAGE BangPatterns #-}
import qualified Data.Vector.Unboxed as VU
newtype AffineTransform = AffineTransform {get :: (VU.Vector Double)} deriving (Show)
{-# INLINE runAffineTransform #-}
runAffineTransform :: AffineTransform -> (Double, Double) -> (Double, Double)
runAffineTransform affTr (!x, !y) = (get affTr `VU.unsafeIndex` 0 * x + get affTr `VU.unsafeIndex` 1 * y + get affTr `VU.unsafeIndex` 2,
get affTr `VU.unsafeIndex` 3 * x + get affTr `VU.unsafeIndex` 4 * y + get affTr `VU.unsafeIndex` 5)
testAffineTransformSpeed :: AffineTransform -> Int -> (Double, Double)
testAffineTransformSpeed affTr count = go count (0.5, 0.5)
where go :: Int -> (Double, Double) -> (Double, Double)
go 0 res = res
go !n !res = go (n-1) (runAffineTransform affTr res)
What more can be done to improve this code?
I defined the following strict/unboxed pair type:
import System.Random.MWC -- for later
import Control.DeepSeq
data SP = SP {
one :: {-# UNPACK #-} !Double
, two :: {-# UNPACK #-} !Double
} deriving Show
instance NFData SP where
rnf p = rnf (one p) `seq` rnf (two p) `seq` ()
and replaced it in the runAffineTransform
function:
runAffineTransform2 :: AffineTransform -> SP -> SP
runAffineTransform2 affTr !(SP x y) =
SP ( get affTr `U.unsafeIndex` 0 * x
+ get affTr `U.unsafeIndex` 1 * y
+ get affTr `U.unsafeIndex` 2 )
( get affTr `U.unsafeIndex` 3 * x
+ get affTr `U.unsafeIndex` 4 * y
+ get affTr `U.unsafeIndex` 5 )
{-# INLINE runAffineTransform2 #-}
then ran this benchmark suite:
main :: IO ()
main = do
g <- create
zs <- fmap (AffineTransform . U.fromList)
(replicateM 100000 (uniformR (0 :: Double, 1) g))
let myConfig = defaultConfig { cfgPerformGC = ljust True }
defaultMainWith myConfig (return ()) [
bench "yours" $ nf (testAffineTransformSpeed zs) 10
, bench "mine" $ nf (testAffineTransformSpeed2 zs) 10
]
Compiled with -O2
and ran, and observed some (~4x) speedup:
benchmarking yours
mean: 257.4559 ns, lb 256.2492 ns, ub 258.9761 ns, ci 0.950
std dev: 6.889905 ns, lb 5.688330 ns, ub 8.839753 ns, ci 0.950
found 5 outliers among 100 samples (5.0%)
3 (3.0%) high mild
2 (2.0%) high severe
variance introduced by outliers: 20.944%
variance is moderately inflated by outliers
benchmarking mine
mean: 69.56408 ns, lb 69.29910 ns, ub 69.86838 ns, ci 0.950
std dev: 1.448874 ns, lb 1.261444 ns, ub 1.718074 ns, ci 0.950
found 4 outliers among 100 samples (4.0%)
4 (4.0%) high mild
variance introduced by outliers: 14.190%
variance is moderately inflated by outliers
Full code is in a gist here.
EDIT
I also posted criterion's output report here.
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