[Haskell-cafe] Memory Management and Lists

David Feuer david.feuer at gmail.com
Tue Jul 12 00:09:30 UTC 2016


The advantage of using a newtype is that it hides the structure from the
outside. Users of StateReader don't need to know that it's made of StateT
and Reader. If something else gets tossed onto the transformer stack,
existing users of StateReader won't need to change.
On Jul 11, 2016 6:08 PM, "Jake" <jake.waksbaum at gmail.com> wrote:

> Just out of curiosity, what is the advantage to using a newtype in this
> case when you will not be writing your own instances of typeclasses?
>
> On Mon, Jul 11, 2016, 15:56 David Feuer <david.feuer at gmail.com> wrote:
>
>> Please repost your code, giving a type signature for each top-level
>> binding. Without them, the code is very difficult to follow. I also
>> strongly recommend using a newtype for your custom monad. Something like
>> this:
>>
>> {-# LANGUAGE GeneralizedNewtypeDeriving, MultiParamTypeClasses,
>> StandaloneDeriving, ... #-}
>>
>> newtype StateReader s c a = SR {runSR :: StateT s (Reader c) a} deriving
>> (Functor, Applicative, Monad)
>>
>> deriving instance MonadReader c (StateReader s c)
>> deriving instance MonadState s (StateReader s c)
>> On Jul 11, 2016 11:07 AM, "Christopher Howard" <ch.howard at zoho.com>
>> wrote:
>>
>>> -- I'm a bit embarrassed of this code because I haven't yet optimized
>>> -- the 'stamp' algorithm for reduced number of matrix operations. But
>>> -- even in this state I should think the memory requirements shouldn't
>>> -- exceed 1MB while generating the nth Matrix, unless Matrix n-1, n-2,
>>> -- etc. are being preserved in memory unnecessarily.
>>>
>>> -- Monad Stack
>>>
>>> type StateReader s c a = StateT s (Reader c) a
>>>
>>> evalStateReader m s c = (runReader (evalStateT m s)) c
>>>
>>> -- Helper function
>>>
>>> type Point = (Float, Float)
>>> type Metric = Point -> Point -> Float
>>>
>>> euclidean :: Metric
>>> euclidean (x1, y1) (x2, y2) = sqrt ((x2 - x1)**2 + (y2 - y1)**2)
>>>
>>> -- monadic function. haven't had chance yet to optimize algorithm to
>>> -- reduce number of matrix operations
>>>
>>> stamp = do radius <- ask
>>>            (oMatrix, walk) <- get
>>>            (wX, wY) <- (return . head) walk
>>>            let nMatrix = matrix (nrows oMatrix) (ncols oMatrix)
>>>                  (\(x, y) -> let (x', y') = (fromIntegral x,
>>> fromIntegral y)
>>>                              in if euclidean (x', y') (wX, wY) > radius
>>>                                 then getElem x y oMatrix
>>>                                 else getElem x y oMatrix + 1)
>>>              in put (nMatrix, tail walk) >> get
>>>
>>>
>>>
>>> -- sequences and gathers results as list
>>>
>>> stampingStates initMx radius walk =
>>>   map fst $ evalStateReader (sequence (repeat stamp)) (initMx, walk)
>>> radius
>>>
>>>
>>> -- Some quick experimentation code. h is the list
>>>
>>> h = stampingStates initMx radius walk'
>>>   where initMx = zero 250 250
>>>         radius = 40
>>>         walk' = walk 40 (125, 125) (mkStdGen 31415)
>>>
>>> -- get 2001st Matrix and convert to Gloss Picture, employing
>>> -- some color interpretation code
>>>
>>> intensityG = let mx = head (drop 2000 h)
>>>              in toImage mx (lightnessInt 272 (minMax mx))
>>>
>>>
>>> On 07/10/2016 10:30 AM, Tom Ellis wrote:
>>> > On Sun, Jul 10, 2016 at 07:41:31AM -0800, Christopher Howard wrote:
>>> >> issue: a Matrix itself should only be, I'm guessing, somewhere around
>>> >> 100KB. But instead I'm maxing out the 3GB of RAM on my old T60 laptop.
>>> >> Maybe I'm generating list elements (Matrices) a lot faster than memory
>>> >> management is releasing them...?
>>> >
>>> > You have almost certainly got a space leak.  Can you post your code?
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