Iteration of a randomized algorithm in fixed space and linear time

Question

I used to ask a similar question once. Now I\'ll be more specific. The purpose is to learn a Haskell idiom to write iterative algorithms with monadic results. In particular,

Answer 1

Some things to consider:

• Use the mersenne-random generator, it is often >100x faster than StdGen

For raw all-out performance, write a custom State monad, like so:

import System.Random.Mersenne.Pure64

data R a = R !a {-# UNPACK #-}!PureMT

-- | The RMonad is just a specific instance of the State monad where the
--   state is just the PureMT PRNG state.
--
-- * Specialized to a known state type
--
newtype RMonad a = S { runState :: PureMT -> R a }

instance Monad RMonad where
    {-# INLINE return #-}
    return a = S $ \s -> R a s

    {-# INLINE (>>=) #-}
    m >>= k  = S $ \s -> case runState m s of
                                R a s' -> runState (k a) s'

    {-# INLINE (>>) #-}
    m >>  k  = S $ \s -> case runState m s of
                                R _ s' -> runState k s'

-- | Run function for the Rmonad.
runRmonad :: RMonad a -> PureMT -> R a
runRmonad (S m) s = m s

evalRmonad :: RMonad a -> PureMT -> a
evalRmonad r s = case runRmonad r s of R x _ -> x

-- An example of random iteration step: one-dimensional random walk.
randStep :: (Num a) => a -> RMonad a
randStep x = S $ \s -> case randomInt s of
                    (n, s') | n < 0     -> R (x+1) s'
                            | otherwise -> R (x-1) s'

Like so: http://hpaste.org/fastcgi/hpaste.fcgi/view?id=27414#a27414

Which runs in constant space (modulo the [Double] you build up), and is some 8x faster than your original.

The use of a specialized state monad with local defintion outperforms the Control.Monad.Strict significantly as well.

Here's what the heap looks like, with the same paramters as you:

Note that it is about 10x faster, and uses 1/5th the space. The big red thing is your list of doubles being allocated.

---

Inspired by your question, I captured the PureMT pattern in a new package: monad-mersenne-random, and now your program becomes this:

• Using monad-mersenne-random

The other change I made was to worker/wrapper transform iterateM, enabling it to be inlined:

 {-# INLINE iterateM #-}
 iterateM n f x = go n x
     where
         go 0 !x = return x
         go n !x = f x >>= go (n-1)

Overall, this brings your code from, with K=500, N=30k

• Original: 62.0s
• New: 0.28s

So that is, 220x faster.

The heap is a bit better too, now that iterateM unboxes.

Answer 2

Importing Control.Monad.State.Strict instead of Control.Monad.State yields a significant performance improvement. Not sure what you're looking for in terms of asymptotics, but this might get you there.

Additionally, you get a performance increase by swapping the iterateM and the mapM so that you don't keep traversing the list, you don't have to hold on to the head of the list, and you don't need to deepseq through the list, but just force the individual results. I.e.:

let end = flip evalState rnd $ mapM (iterateM iters randStep) start

If you do so, then you can change iterateM to be much more idiomatic as well:

iterateM 0 _ x = return x
iterateM n f !x = f x >>= iterateM (n-1) f

This of course requires the bang patterns language extension.

Answer 3

This is probably a small point compared to the other answers, but is your ($!!) function correct?

You define

($!!) :: (NFData a) => (a -> b) -> a -> b
f $!! x = x `deepseq` f x

This will fully evaluate the argument, however the function result won't necessarily be evaluated at all. If you want the $!! operator to apply the function and fully evaluate the result, I think it should be:

($!!) :: (NFData b) => (a -> b) -> a -> b
f $!! x = let y = f x in y `deepseq` y