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I am working on a code where I represent a MonadPlus using a Tree data structure and then explore it, summing over all of the leaves (whose type must be an instance of Monoid); I need to use a Tree representation to do this (rather than using the List representation) because as I explore the tree I want to manually keep track of things like my position in the Tree for checkpointing and workload balancing purposes.

My problem is that using a Tree data structure is slower than using the List data structure by a factor of ~ 4 for small depths (~ 1-10) and ~ 2.5 for higher depths (~15), and I am having trouble understanding exactly why this is; in particular I am wondering if there are tricks that I can employ to make my code faster.

The remainder of this question will be some code that I wrote to benchmark Tree versus List. I will present my code in sections. First, the prelude:

{-# LANGUAGE FlexibleInstances #-}
{-# LANGUAGE GeneralizedNewtypeDeriving #-}

import Control.DeepSeq
import Control.Monad
import Control.Monad.Operational

import Criterion.Main

import Data.Functor.Identity
import Data.Monoid

My Tree type is defined using functionality in the operational package by specifying the type of instructions and then use operational to obtain a Program monad from this.

data TreeTInstruction m α where
    Choice :: TreeT m α -> TreeT m α -> TreeTInstruction m α
    Null :: TreeTInstruction m α

newtype TreeT m α = TreeT { unwrapTreeT :: ProgramT (TreeTInstruction m) m α } deriving (Monad)

type Tree = TreeT Identity

instance Monad m => MonadPlus (TreeT m) where
    mzero = TreeT . singleton $ Null
    left `mplus` right = TreeT . singleton $ Choice left right

The following two functions respectively build a perfect binary tree with Sum Int at all the leaves (which can be any type that is an instance MonadPlus) and explore a given Tree.

makeTree :: MonadPlus m => Int -> m (Sum Int)
makeTree 0 = return (Sum 1)
makeTree d = makeTree (d-1) `mplus` makeTree (d-1)

exploreTree :: Tree (Sum Int) -> Sum Int
exploreTree v =
    case view (unwrapTreeT v) of
        Return x -> x
        Choice left right :>>= k ->
            let x = exploreTree $ left >>= TreeT . k
                y = exploreTree $ right >>= TreeT . k
                xy = mappend x y
            in xy

As a technicality we need to write an instance of NFData for Sum Int for the benchmarking code.

instance NFData (Sum Int) where
    rnf s@(Sum x) = x `seq` s `seq` ()

Finally, we have the benchmarking code. First, it benchmarks using makeTree to construct a tree using the List monad and then using mconcat to sum over all the results, and second, it benchmarks using makeTree using the Tree monad and sums over all the leaves using the exploreTree function.

main = defaultMain
    [bench "list" $ nf (mconcat . makeTree) depth
    ,bench "tree" $ nf (exploreTree . makeTree) depth
    depth = 1 -- this is a knob that controls the tree size

For benchmarks, the list and tree times were as follows on my machine:

       List    Tree  Tree/List
 1:   140ns   460ns  3.3x
 2:   330ns  1300ns  4.0x
 4:  1600ns  6300ns  3.9x
 8:    32us   130us  4.0x
10:   150us   600us  4.0x
12:   990us  2900us  2.9x
14:   4.6ms    12ms  2.6x
16:    24ms    55ms  2.3x
17:    50ms   110ms  2.2x

(17 is the largest depth because after that exploring the list causes a stack overflow.)

If anyone has advice on exactly what the cause is of this slow down and if anything can be done about it then I would greatly appreciate it.

share|improve this question
Could you please describe in more detail what task you'd like to solve? This would help to understand the program and offer alternative solutions. –  Petr Pudlák Feb 1 at 14:34
My project involves automatically parallelizing searches through trees; to do this I need an efficient representation for non-deterministic computations as a Monad that I can interpret as a tree, as the branches in the tree give me workloads that I can send to other workers to explore in parallel. For more details, project page is here. –  Gregory Crosswhite Feb 5 at 23:45
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