Performance and Feature Case Studies in Ecstasy

In my (copious) spare time, I’ve been working on an RTS game written in Haskell. It’s using my entity-component system library ecstasy in anger, for what is likely the library’s first time. As a result, I’m learning a lot about doing performance haggling with the compiler, as well as getting opportunities for even more type-trickery. I thought both might make for interesting topics of discussion.

Overview of ecstasy’s internals🔗

Before diving into what I’ve been changing recently, it’s probably a good idea to quickly talk inside baseball about how ecstasy works. The basic idea is this, you define a “world” higher-kinded data (HKD) corresponding to the components you care about. The library instantiates your HKD world in different ways to form a structure-of-arrays corresponding to the high-efficiency storage of the ECS, and to form just a structure corresponding to an actual entity.

This machinery is built via the Component type family:

type family Component (s :: StorageType)
                      (c :: ComponentType)
                      (a :: *) :: *

Using DataKinds, Component is parameterized by three types. s :: StorageType describes how the library wants to use this component – possibly in the “structure-of-arrays” format consumed by the library, or as an entity structure, to be used by the application programmer. s is left polymorphic when defining the HKD.

The c :: ComponentType parameter is used to indicate the semantics of the field; some options include “each entity may or may not have this field” or “at most one entity may have this field.” The former might be used for something like position, while the latter could be focusedOnByTheCamera.

Finally, a :: * is the actual type you want the field to have.

Having data is a great first step, but it’s currently just an opaque blob to the library. This is where GHC.Generics comes in – given an (auto-derivable) Generic instance for our world, we can use GHC.Generics to automatically further derive more specialized machinery for ourselves.

As an example, assume our world looked like this (absent the Component trickery):

data World f = World
  { position :: f (V2 Double)
  , graphics :: f Graphics

we can use GHC.Generics to automatically generate the equivalent to a function:

getEntity :: Int -> World Data.IntMap.IntMap -> World Maybe
getEntity ent storage =
  World (Data.IntMap.lookup ent $ position storage)
        (Data.IntMap.lookup ent $ graphics storage)

which converts from a structure-of-arrays representation to a structure-of-maybes. The actual technique behind implementing these generic functions is out of scope for today’s topic, but I’ve written on it previously.

For its part, ecstasy exposes the SystemT monad, which at its heart is just a glorified Control.Monad.Trans.State.StateT (Int, World 'Storage). The Int keeps track of the next ID to give out for a newly created entity.

To a rough approximation, this is all of the interesting stuff inside of ecstasy. So armed with this knowledge, we’re ready to tackle some of the problems that have been unearthed recently.

Stupid space leaks🔗

My original test for ecstasy was a small platformer – a genre not known for the sheer number of entities all interacting at once. As a result, ecstasy performed terribly, but I didn’t notice because I hadn’t benchmarked it or actually stress-tested it whatsoever. But that’s OK, I wrote it to scratch an itch while hanging out in a Thai airport; I’ve never claimed to write titanium-grade software :)

But in my RTS, the library was obvious struggling after allocating only 100 dudes. The thing was leaking memory like crazy, which was because I used lazy state and containers. Oopsie daisies! Replacing Control.Monad.Trans.State and Data.IntMap with their strict versions cleared it up.

Honestly I’m not sure why the lazy versions are the default, but I guess that’s the world we live in. SANDY’S HOT PRO TIPS: don’t use lazy maps or state unless you’ve really thought about it.

Virtual components🔗

While working on my RTS, I realized that I was going to need fast spacial queries to answer questions like “is there anyone around that I should attack?” The result was some sort of Frankenstein bastard child of a quadtree and a reverse index to answer both “where am I?” and “who’s nearby?”

This worked well to answer the queries I asked of it, but posed a problem; in order to maintain its indices, my datastructure needed to be the source of truth on who was where. Having a position component wasn’t going to cut it anymore, since the ECS was no longer responsible for this data. I briefly considered trying to write a shim to keep the two datasources in sync, but it felt simultaneously like an ad-hoc hack and a maintenance nightmare, so I gave up and removed the component.

Unfortunately, all was not well. I added some monadic getters and setters to help shuffle the position information around, but oh god this became a garbage fire. Things that were before atomic updates now had extra calls to get and set the bastard, and everything was miserable.

I realized what I really wanted was the capability for ecstasy to be aware of components without necessarily being the owner of them. Which is to say, components whose reads and writes invisibly dispatched out to some other monadic system.

OK, great, I knew what I wanted. Unfortunately, the implementation was not so straightforward. The problem was the functions I wanted:

vget :: Ent -> m (Maybe a)
vset :: Ent -> Update a -> m ()

had this troublesome m parameter, and there was no clear place to put it. The monad to dispatch virtual calls to is a property of the interpretation of the data (actually running the sucker), not the data itself.

As a result, it wasn’t clear where to actually keep the m type parameter. For example, assuming we want position to be virtual in our world:

data World s = World
  { position :: Component s 'Virtual (V2 Double)

Somehow, after unifying s ~ 'Storage, we want this to come out as:

data World 'Storage = World
  { position :: ( Ent -> m (Maybe (V2 Double)        -- vget
                , Ent -> Update (V2 Double) -> m ()  -- vset

But where do we get the m from? There’s no obvious place.

We could add it as a mandatory parameter on World, but that forces an implementation detail on people who don’t need any virtual fields.

We could existentialize it, and then unsafeCoerce it back, but… well, I stopped following that line of thought pretty quickly.

My first solution to this problem was to add a Symbol to the Virtual component-type token, indicating the “name” of this component, and then using a typeclass instance to actually connect the two:

data World s = World
  { position :: Component s ('Virtual "position") (V2 Double)

-- we put the monad here: `m`
instance VirtualAccess "position" m (V2 Double) where
  vget = ...
  vset = ...

While it worked, this was obviously a hack and my inner muse of library design was so offended that I spent another few days looking for a better solution. Thankfully, I came up with one.

The solution is one I had already skirted around, but failed to notice. This monad is a property only of the interpretation of the data, which is to say it really only matters when we’re building the world storage. Which means we can do some janky dependency-injection stuff and hide it inside of the storage-type token.

Which is to say, that given a world of the form:

data World s = World
  { position :: Component s 'Virtual (V2 Double)

we could just pass in the appropriate monad when instantiating the world for its storage. Pseudocode:

data World (Storage m) = World
  { position :: Component (Storage m) 'Virtual (V2 Double)

All of a sudden, the Component type family now has access to m, and so it can expand into the vget/vset pair in a type-safe way. And the best part is that this is completely invisible to the user who never needs to care about our clever implementation details.

Spectacular! I updated all of the code generated via GHC.Generics to run in m so it could take advantage of this virtual dispatch, and shipped a new version of ecstasy.

Polymorphic performance woes🔗

While all of this virtual stuff worked, it didn’t work particularly quickly. I noticed some significant regressions in performance in my RTS upon upgrading to the new version. What was up? I dug in with the profiler and saw that my GHC.Generics-derived code was no longer being inlined. HKD was performing more terribly than I thought!

All of my INLINE pragmas were still intact, so I wasn’t super sure what was going on. I canvassed #ghc on freenode, and the ever-helpful glguy had this to say:

generics can’t optimize away when that optimization relies on GHC applying Monad laws to do it

Oh. Lame. That’s why my performance had gone to shit!

I’m not sure if this is true, but my understanding is that the problem is that my monad was polymorphic, and thus the inliner wasn’t getting a chance to fire. glguy pointed me towards the aptly-named confusing lens combinator, whose documentation reads:

Fuse a Traversal by reassociating all of the <*> operations to the left and fusing all of the fmap calls into one. This is particularly useful when constructing a Traversal using operations from GHC.Generics

confusing exploits the Yoneda lemma to merge their separate uses of fmap into a single fmap and it further exploits an interesting property of the right Kan lift (or Curried) to left associate all of the uses of <*> to make it possible to fuse together more fmaps.

This is particularly effective when the choice of functor f is unknown at compile time or when the Traversal in the above description is recursive or complex enough to prevent inlining.

That sounds exactly like the problem I was having, doesn’t it? The actual confusing combinator itself was no help in this situation, so I dug in and looked at its implementation. It essentially lifts your m-specific actions into Curried (Yoneda m) (Yoneda m) (don’t ask me!), and then lowers it at the very end. My (shaky) understanding is this:

Yoneda f is a functor even when f itself is not, which means we have a free functor instance, which itself means that fmap on Yoneda f can’t just lift fmap from f. This is cool if fmaping over f is expensive – Yoneda just fuses all fmaps into a single one that gets performed when you lower yourself out of it. Essentially it’s an encoding that reduces an O(n)O(n) cost of doing nn fmaps down to O(1)O(1).

Curried f f similarly has a free Applicative instance, which, he says waving his hands furiously, is where the <*> improvements come from.

So I did a small amount of work to run all of my GHC.Generics code in Curried (Yoneda m) (Yoneda m) rather than in m directly, and looked at my perf graphs. While I was successful in optimizing away my GHC.Generics code, I was also successful in merely pushing all of the time and allocations out of it and into Yoneda.fmap. Curiously, this function isn’t marked as INLINE which I suspect is why the inliner is giving up (the isomorphic Functor instance for Codensity is marked as INLINE, so I am very hesitantly rallying the hubris to suggest this is a bug in an Ed Kmett library.)

Despite the fact that I’ve been saying “we want to run virtual monadic actions,” throughout this post, I’ve really meant “we want to run virtual applicative actions.” Which is why I thought I could get away with using Curried (Yoneda m) (Yoneda m) to solve my optimization problems for me.

So instead I turned to Codensity, which legend tells can significantly improve the performance of free monads by way of the same mystical category-theoretical encodings. Lo and behold, moving all of my monadic actions into Codensity m was in fact enough to get the inliner running again, and as a result, getting our HKD once more to be less terrible.

If you’re curious in how Codensity and friends work their magic, glguy pointed me to a tutorial he wrote explaining the technique. Go give it a read if you’re feeling plucky and adventurous.