Performance of Conway's Game of Life using the Store comonad

I've written a simple implementation of Conway's Game of Life using the Store comonad (see code below). My problem is that the grid generation is getting visibly slower from the fifth iteration onwards. Is my issue related to the fact that I'm using the Store comonad? Or am I making a glaring mistake? As far as I could tell, other implementations, which are based on the Zipper comonad, are efficient.

import Control.Comonad

data Store s a = Store (s -> a) s

instance Functor (Store s) where
    fmap f (Store g s) = Store (f . g) s

instance Comonad (Store s) where
    extract (Store f a) = f a
    duplicate (Store f s) = Store (Store f) s

type Pos = (Int, Int)

seed :: Store Pos Bool
seed = Store g (0, 0)
    where
        g ( 0,  1) = True
        g ( 1,  0) = True
        g (-1, -1) = True
        g (-1,  0) = True
        g (-1,  1) = True
        g _        = False

neighbours8 :: [Pos]
neighbours8 = [(x, y) | x <- [-1..1], y <- [-1..1], (x, y) /= (0, 0)]

move :: Store Pos a -> Pos -> Store Pos a
move (Store f (x, y)) (dx, dy) = Store f (x + dx, y + dy)

count :: [Bool] -> Int
count = length . filter id

getNrAliveNeighs :: Store Pos Bool -> Int
getNrAliveNeighs s = count $ fmap (extract . move s) neighbours8

rule :: Store Pos Bool -> Bool
rule s = let n = getNrAliveNeighs s
        in case (extract s) of
            True  -> 2 <= n && n <= 3
            False -> n == 3

blockToStr :: [[Bool]] -> String
blockToStr = unlines . fmap (fmap f)
    where
        f True  = '*'
        f False = '.'

getBlock :: Int -> Store Pos a -> [[a]]
getBlock n store@(Store _ (x, y)) =
    [[extract (move store (dx, dy)) | dy <- yrange] | dx <- xrange]
    where
        yrange = [(x - n)..(y + n)]
        xrange = reverse yrange

example :: IO ()
example = putStrLn
        $ unlines
        $ take 7
        $ fmap (blockToStr . getBlock 5)
        $ iterate (extend rule) seed

Solution

The store comonad per se doesn't really store anything (except in an abstract sense that a function is a “container”), but has to compute it from scratch. That clearly gets very inefficient over a couple of iterations.

You can alleviate this with no change to your code though, if you just back up the s -> a function with some memoisation:

import Data.MemoTrie

instance HasTrie s => Functor (Store s) where
  fmap f (Store g s) = Store (memo $ f . g) s

instance HasTrie s => Comonad (Store s) where
  extract (Store f a) = f a
  duplicate (Store f s) = Store (Store f) s

Haven't tested whether this really gives acceptable performance.

Incidentally, Edward Kmett had an explicitly-memoised version in an old version of the comonad-extras package, but it's gone now. I've recently looked if that still works (seems like it does, after adjusting dependencies).