Scheduling Effects

Posted on June 23, 2018
Part 4 of a 4-part series on Breadth-First Traversals
Tags: Haskell

After the last post, Noah Easterly pointed me to their tree-traversals library, and in particular the Phases applicative transformer. It allows you to batch applicative effects to be run together: for the breadth-first traversal, we can batch the effects from each level together, giving us a lovely short solution to the problem.

breadthFirst c = runPhasesForwards . go
    go (x:<xs) = liftA2 (:<) (now (c x)) (delay (traverse go xs))

In my efforts to speed this implementation up, I came across a wide and interesting literature on scheduling effects, which I’ll go through a little here.


The first thing that jumps to mind, for me, when I think of “scheduling” is coroutines. These are constructs that let you finely control the order of execution of effects. They’re well explored in Haskell by now, and most libraries will let you do something like the following:

oneThenTwo = do
  liftIO $ print 1
  delay $ liftIO $ print 2

We first print 1, then, after a delay, we print 2. The delay doesn’t make a difference if we just run the whole thing:

>>> retract oneThenTwo

But you can see its effect when we use the interleave combinator:

>>> retract $ interleave (replicate 3 oneThenTwo)

Hopefully you can see how useful this might be, and the similarity to the Phases construction.

The genealogy of most coroutine libraries in Haskell seems to trace back to Blažević (2011) or Kiselyov (2012): the implementation I have been using in these past few examples (IterT) comes from a slightly different place. Let’s take a quick detour to explore it a little.


In functional programming, there are several constructions for modeling error-like states: Maybe for your nulls, Either for your exceptions. What separates these approaches from the “unsafe” variants (null pointers, unchecked exceptions) is that we can prove, in the type system, that the error case is handled correctly.

Conspicuously absent from the usual toolbox for modeling partiality is a way to model nontermination. At first glance, it may seem strange to attempt to do so in Haskell. After all, if I have a function of type:

String -> Int

I can prove that I won’t throw any errors (with Either, that is), because the type Int doesn’t contain Left _. I’ve also proved, miraculously, that I won’t make any null dereferences, because Int also doesn’t contain Nothing. I haven’t proved, however, that I won’t loop infinitely, because (in Haskell), Int absolutely does contain \bot.

So we’re somewhat scuppered. On the other hand, While we can’t prove termination in Haskell, we can:

  1. Model it.
  2. Prove it in something else.

Which is exactly what Venanzio Capretta did in the fascinating (and quite accessible) talk “Partiality is an effect” (Capretta, Altenkirch, and Uustalu 2004)1.

The monad in question looks like this:

data Iter a
    = Now a
    | Later (Inf (Iter a))

We’re writing in Idris for the time being, so that we can prove termination and so on. The “recursive call” to Iter is guarded by the Inf type: this turns on a different kind of totality checking in the compiler. Usually, Idris will prevent you from constructing infinite values. But that’s exactly what we want to do here. Take the little-known function until:

until :: (a -> Bool) -> (a -> a) -> a -> a

It’s clearly not necessarily total, and the totality checker will complain as such when we try and implement it directly:

until : (a -> Bool) -> (a -> a) -> a -> a
until p f x = if p x then x else until p f (f x)

But we can use Iter to model that possible totality:

until : (a -> Bool) -> (a -> a) -> a -> Iter a
until p f x = if p x then Now x else Later (until p f (f x))

Of course, nothing’s for free: when we get the ability to construct infinite values, we lose the ability to consume them.

run : Iter a -> a
run (Now x) = x
run (Later x) = run x

We get an error on the run function. However, as you would expect, we can run guarded iteration: iteration up until some finite point.

runUntil : Nat -> Iter a -> Maybe a
runUntil Z _ = Nothing
runUntil (S n) (Now x) = Just x
runUntil (S n) (Later x) = runUntil n x

Making our way back to Haskell, we must first—as is the law—add a type parameter, and upgrade our humble monad to a monad transformer:

newtype IterT m a = IterT { runIterT :: m (Either a (IterT m a)) }

type Iter = IterT Identity

The semantic meaning of the extra m here is interesting: each layer adds not just a recursive step, or a single iteration, but a single effect. Interpreting things in this way gets us back to the original goal:


The Later constructor above can be translated to a delay function on the transformer:

delay = IterT . pure . Right

And using this again, we can write the following incredibly short definition for unfoldTreeM_BF:

unfoldTreeM_BF :: Monad m => (b -> m (a, [b])) -> b -> m (Tree a)
unfoldTreeM_BF f = retract . go
    go b = do
      (x,xs) <- lift (f b)
      fmap (Node x) (interleave (map (delay . go) xs))


It would be nice to bring this back to traversals, but alas, IterT is pretty monad-centric. What’s more, if it’s analogous to Phases it certainly doesn’t look like it:

data Phases f a where
  Lift :: f a -> Phases f a
  (:<*>) :: f (a -> b) -> Phases f a -> Phases f b

However, in the documentation for IterT, there’s the following little note:

IterT ~ FreeT Identity

Where FreeT is the free monad transformer. This seems to strongly hint that we could get the same thing for applicatives with ApT. Let’s try it:

newtype Phases f a = Phases
    { runPhases :: ApT Identity f a
    } deriving Functor

The Applicative instance is a little hairy, but it seems correct:

Applicative Instance

(on a side note: thank goodness for liftA2 finally getting into Applicative)

And we get all the normal combinators:

delay :: Applicative f => Phases f a -> Phases f a
delay = Phases . ApT . pure . Ap (pure ()) . fmap const . runPhases

lift :: Functor f => f a -> Phases f a
lift = Phases . liftApO

The issue comes with running the thing at the end: Monad creeps back in.

retract :: Monad f => Phases f a -> f a
retract = fmap (runIdentity . retractAp) . joinApT . runPhases

Because the effects are all layered on top of each other, you need to flatten them out at the end, which requires join. Mind you, it does work: it’s just not as general as it could be.

All’s not lost, though. Turns out, we never needed the transformer in the first place: we could just define the different applicative instance straight off.

newtype Phases f a = Phases
    { runPhases :: Ap f a
    } deriving Functor

instance Applicative f =>
         Applicative (Phases f) where
    pure = Phases . Pure
    liftA2 f' (Phases xs') (Phases ys') = Phases (go f' xs' ys')
        go ::  a b c.
              (a -> b -> c)
           -> Ap f a
           -> Ap f b
           -> Ap f c
        go f (Pure x) ys = fmap (f x) ys
        go f xs (Pure y) = fmap (`f` y) xs
        go f (Ap x xs) (Ap y ys) =
                (liftA2 (,) x y)
                (go (\xx yy -> uncurry f . (xx *** yy)) xs ys)

delay :: Applicative f => Phases f a -> Phases f a
delay = Phases . Ap (pure ()) . fmap const . runPhases

retract :: Applicative f => Phases f a -> f a
retract = retractAp . runPhases

lift :: f a -> Phases f a
lift = Phases . liftAp

More Coroutines

In the wonderful article Coroutine Pipelines (Blažević 2011), several different threads on coroutine-like constructions are unified. What I’ve demonstrated above isn’t yet as powerful as what you might see in a full coroutine library: ideally, you’d want generators and sinks. As it turns out, when we look back at the note from IterT:

IterT ~ FreeT Identity

We can get both of those other constructs by swapping out Identity2:

Generator a = FreeT ((,) a)
Sink a = FreeT ((->) a)

(Sink is usually called an Iteratee)

This is the fundamental abstraction that underlies things like the pipes library (Gonzalez 2018).


The only missing part from the first coroutine example by now is interleave. In the free library, it has the following signature:

interleave :: Monad m => [IterT m a] -> IterT m [a]

But we should be able to spot that, really, it’s a traversal. And, as a traversal, it should rely on some underlying Applicative instance. Let’s try and come up with one:

newtype Parallel m f a = Parallel
    { runParallel :: FreeT m f a

instance (Functor f, Functor m) =>
         Functor (Parallel m f) where
    fmap f = Parallel . FreeT . fmap go . runFreeT . runParallel
        go = bimap f (FreeT . fmap go . runFreeT)

instance (Applicative f, Applicative m) =>
         Applicative (Parallel m f) where
    pure = Parallel . FreeT . pure . Pure
    Parallel fs' <*> Parallel xs' = Parallel (unw fs' xs')
        unw (FreeT fs) (FreeT xs) = FreeT (liftA2 go fs xs)
        go (Pure f) = bimap f (runParallel . fmap f . Parallel)
        go (Free fs) = Free . \case
            Pure x -> fmap (runParallel . fmap ($x) . Parallel) fs
            Free xs -> liftA2 unw fs xs

Now, interleave is just sequenceA!

Applicatives, Again

So we can see that there’s a “parallel” applicative for both the free monad and the free applicative. To try and understand this type a little better, we can leverage our intuition about a much simpler, more familiar setting: lists. There’s an interesting similarity between lists and the free monad: FreeT ((,) a)) looks a lot like “ListT done right” (so much so, in fact, that most coroutine libraries provide their own version of it). More concretely, list also has a famous “parallel” applicative: ZipList!

newtype ZipList a
    = ZipList 
    { getZipList :: [a]
    } deriving Functor

instance Applicative ZipList where
  pure = ZipList . repeat
  liftA2 f (ZipList xs) (ZipList ys) = ZipList (zipWith f xs ys)

We’ll use some of our knowledge about ZipList to help us in the next section.


We’ve seen that efforts to model both coroutines and partiality end up in the same neighborhood: there’s yet another way to get there, which seems (at first) almost the opposite of the second. It starts with a blog post from Conor McBride (2009) called “Time flies like an applicative functor”. Curiously, here too breadth-first labeling is the focus. Remember first the lovely circular solution from Jones and Gibbons (1993):

data Tree a = Leaf | Node a (Tree a) (Tree a)

relabel :: Tree x -> [[a]] -> (Tree a, [[a]])
relabel Leaf xss = (Leaf,xss)
relabel (Node _ l r) ((x:xs):xss0) =
  let (l',xss1) = relabel l xss0
      (r',xss2) = relabel r xss1
  in (Node x l' r',xs:xss2)
bflabel :: Tree x -> [a] -> Tree a
bflabel tr xs = u
    (u,xss) = relabel tr (xs:xss)

As lovely as it is, spare a thought for the poor totality checker: it’s hard to imagine how it would even start to show that something so lazy and circular would terminate. IterT won’t help us here, either: it can help us express programs that might diverge, not weird-looking ones that definitely won’t.

The solution presented is a type (De) which has a limited set of combinators: a fixpoint (fix :: (De x -> x) -> x), and an applicative instance. As long as all problematic recursive calls are instead expressed using those combinators, the termination checker should be satisfied.

De can be thought of as a “delay” wrapper. Values of type De a are one step in the future, De (De a) are two, and so on. This idea was later expanded upon in Atkey (2011) and Atkey and McBride (2013) to clock variables. Instead of types with a delay, types are tagged with how much more time they have (something like “fuel” in the Idris sense, maybe). So a value of type a𝖪a^\mathsf{K} is tagged with time 𝖪\mathsf{K}, effectively meaning “I have 𝖪\mathsf{K} productive steps left before I diverge”. “Productive steps” will mean something different for every data type: for lists, it could mean that it can produce up until the 𝖪\mathsf{K}th cons-cell. In the paper (Atkey and McBride 2013) this is fleshed out a little more, with fixpoint combinators and so on. As a concrete example, take the type of the cons operator on streams:

Cons:aStream a𝖪Stream a𝖪+1\begin{equation} \text{Cons} : \text{a} \rightarrow \text{Stream a}^\mathsf{K} \rightarrow \text{Stream a}^{\mathsf{K}+1} \end{equation}

It increments the clock on the type, saying that it has one more productive step than it did before. This is kind of the opposite of a “delay”: previously, the scheduling types have meant “this is available 𝖪\mathsf{K} number of steps in the future” rather than “this is available for another 𝖪\mathsf{K} steps”. We can still describe delays in this system, though, using the 𝖪\rhd^\mathsf{K} notation:

Cons:a𝖪Stream aStream a\begin{equation} \text{Cons} : \text{a} \rightarrow \rhd^\mathsf{K}\text{Stream a} \rightarrow \text{Stream a} \end{equation}

Let’s first try express some of this in the free monad:

data K = Z | S K

data Delay :: K -> (Type -> Type) -> (Type -> Type) -> Type -> Type where
  Now   :: a -> Delay n f m a
  Later :: f (DelayT n f m a) -> Delay (S n) f m a

instance (Functor f, Functor m) => Functor (Delay n f m) where
  fmap f (Now x) = Now (f x)
  fmap f (Later xs) = Later (fmap (fmap f) xs)

newtype DelayT n f m a = DelayT { runDelayT :: m (Delay n f m a) }

instance (Functor f, Functor m) =>
         Functor (DelayT n f m) where
    fmap f = DelayT . fmap (fmap f) . runDelayT

We can straight away express one of the combinators from the paper, force:

force :: Functor m => ( k. DelayT k f m a) -> m a
force (DelayT xs) = fmap f xs
    f :: Delay Z f m a -> a
    f (Now x) = x

Similar trick to runST here: if the type is delayed however long we want it to be, then it mustn’t really be delayed at all.

Next, remember that we have types for streams (generators) from the IterT monad:

type Stream n a = DelayT n ((,) a)

And cons does indeed have the right type:

cons :: Applicative m => a -> Stream n a m b -> Stream (S n) a m b
cons x xs = DelayT (pure (Later (x,xs)))

We also get an applicative:

instance (Applicative f, Applicative m) =>
         Applicative (DelayT n f m) where
    pure = DelayT . pure . Now
    DelayT fs' <*> DelayT xs' = DelayT (liftA2 go fs' xs')
        go ::  k a b. Delay k f m (a -> b) -> Delay k f m a -> Delay k f m b
        go (Now f) = fmap f
        go (Later fs) = Later . \case
            Now x -> fmap (fmap ($x)) fs
            Later xs -> liftA2 (<*>) fs xs

Now, I’m not sure how much this stuff actually corresponds to the paper, but what caught my eye is the statement that De is a classic “applicative-not-monad”: just like ZipList. However, under the analogy that the free monad is listy, and the parallel construction is ziplist-y, what we have in the DelayT is the equivalent of a length-indexed list. These have an applicative instance similar to ziplists: but they also have a monad. Can we apply the same trick here?

Future Posts

There’s a lot of fascinating stuff out there—about clock variables, especially—that I hope to get a chance to learn about once I get a chance. What I’m particularly interested to follow up on includes:

  1. Comonads and their relationship to these constructions. Streams are naturally expressed as comonads, could they be used as a basis on which to build a similar “delay” mechanism?
  2. I’d love to explore more efficient implementations like the ones in Spivey (2017).
  3. I’m interested to see the relationship between these types, power series, and algebras for combinatorial search (Spivey 2009).


Atkey, Robert. 2011. “How to be a Productive Programmer - by putting things off until tomorrow.” Heriot-Watt University.

Atkey, Robert, and Conor McBride. 2013. “Productive coprogramming with guarded recursion.” In, 197. ACM Press. doi:10.1145/2500365.2500597.

Blažević, Mario. 2011. “Coroutine Pipelines.” The Monad.Reader 19 (19) (August): 29–50.

Capretta, Venanzio, Thorsten Altenkirch, and Tarmo Uustalu. 2004. “Partiality is an effect.” In Dependently Typed Programming, 04381:20. Dagstuhl seminar proceedings. Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany.

———. 2005. “Partiality is an effect.” In Slides for a talk given by Uustalu at the 22nd meeting of IFIP Working Group. Vol. 2.

Gonzalez, Gabriel. 2018. “Pipes: Compositional pipelines.”

Jones, Geraint, and Jeremy Gibbons. 1993. Linear-time Breadth-first Tree Algorithms: An Exercise in the Arithmetic of Folds and Zips. Dept of Computer Science, University of Auckland.

Kiselyov, Oleg. 2012. “Iteratees.” In Proceedings of the 11th International Conference on Functional and Logic Programming, 166–181. Lecture notes in computer science. Berlin, Heidelberg: Springer, Berlin, Heidelberg. doi:10.1007/978-3-642-29822-6_15.

McBride, Conor. 2009. “Time flies like an applicative functor.” Epilogue for Epigram.\%3Fp=186.html.

Spivey, J. Michael. 2009. “Algebras for combinatorial search.” Journal of Functional Programming 19 (3-4) (July): 469–487. doi:10.1017/S0956796809007321.

Spivey, Michael. 2017. “Faster coroutine pipelines.” Proceedings of the ACM on Programming Languages 1 (ICFP) (August): 1–23. doi:10.1145/3110249.

  1. There is a later, seemingly more formal version of the talk available (Capretta, Altenkirch, and Uustalu 2005), but the one from 2004 was a little easier for me to understand, and had a lot more Haskell code.

  2. Small note: (,) a and (->) a are adjunct. I wonder if there is any implication from this? Certainly, producers and consumers seem adjunct, but there’s no instance I can find for it in adjunctions.