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Existential Pontification and Generalized Abstract Digressions

Category theory for loop optimizations

Christopher de Sa and I have been working on a category theoretic approach to optimizing MapReduce-like pipelines. Actually, we didn’t start with any category theory—we were originally trying to impose some structure on some of the existing loop optimizations that the Delite compiler performed, and along the way, we rediscovered the rich relationship between category theory and loop optimization.

On the one hand, I think the approach is pretty cool; but on the other hand, there’s a lot of prior work in the area, and it’s tough to figure out where one stands on the research landscape. As John Mitchell remarked to me when I was discussing the idea with him, “Loop optimization, can’t you just solve it using a table lookup?” We draw a lot of inspiration from existing work, especially the program calculation literature pioneered by Bird, Meertens, Malcom, Meijer and others in the early 90s. The purpose of this blog post is to air out some of the ideas we’ve worked out and get some feedback from you, gentle reader.

There are a few ways to think about what we are trying to do:

  • We would like to implement a calculational-based optimizer, targeting a real project (Delite) where the application of loop optimizations can have drastic impacts on the performance of a task (other systems which have had similar goals include Yicho, HYLO).
  • We want to venture where theorists do not normally tread. For example, there are many “boring” functors (e.g. arrays) which have important performance properties. While they may be isomorphic to an appropriately defined algebraic data type, we argue that in a calculational optimizer, we want to distinguish between these different representations. Similarly, many functions which are not natural transformations per se can be made to be natural transformations by way of partial application. For example, filter p xs is a natural transformation when map p xs is incorporated as part of the definition of the function (the resulting function can be applied on any list, not just the original xs). The resulting natural transformation is ugly but useful.
  • For stock optimizers (e.g. Haskell), some calculational optimizations can be supported by the use of rewrite rules. While rewrite rules are a very powerful mechanism, they can only describe “always on” optimizations; e.g. for deforestation, one always wants to eliminate as many intermediate data structures as possible. In many of the applications we want to optimize, the best performance can only be achieved by adding intermediate data structures: now we have a space of possible programs and rewrite rules are woefully inadequate for specifying which program is the best. What we’d like to do is use category theory to give an account for rewrite rules with structure, and use domain specific knowledge to pick the best programs.

I’d like to illustrate some of these ideas by way of an example. Here is some sample code, written in Delite, which calculates an iteration of (1-dimensional) k-means clustering:

(0 :: numClusters, *) { j =>
  val weightedPoints = sumRowsIf(0,m){i => c(i) == j}{i => x(i)};
  val points = c.count(_ == j);
  val d = if (points == 0) 1 else points
  weightedPoints / d

You can read it as follows: we are computing a result array containing the position of each cluster, and the outermost block is looping over the clusters by index variable j. To compute the position of a cluster, we have to get all of the points in x which were assigned to cluster j (that’s the c(i) == j condition) and sum them together, finally dividing by the sum by the number of points in the cluster to get the true location.

The big problem with this code is that it iterates over the entire dataset numClusters times, when we’d like to only ever do one iteration. The optimized version which does just that looks like this:

val allWP = hashreduce(0,m)(i => c(i), i => x(i), _ + _)
val allP = hashreduce(0,m)(i => c(i), i => 1, _ + _)
(0::numClusters, *) { j =>
    val weightedPoints = allWP(j);
    val points = allP(j);
    return weightedpoints / d

That is to say, we have to precompute the weighted points and the point count (note the two hashreduces can and should be fused together) before generating the new coordinates for each of the clusters: generating more intermediate data structures is a win, in this case.

Let us now calculate our way to the optimized version of the program. First, however, we have to define some functors:

  • D_i[X] is an array of X of a size specified by i (concretely, we’ll use D_i for arrays of size numPoints and D_j for arrays of size numClusters). This family of functors is also known as the diagonal functor, generalized for arbitrary size products. We also will rely on the fact that D is representable, that is to say D_i[X] = Loc_D_i -> X for some type Loc_D_i (in this case, it is the index set {0 .. i}.
  • List[X] is a standard list of X. It is the initial algebra for the functor F[R] = 1 + X * R. Any D_i can be embedded in List; we will do such conversions implicitly (note that the reverse is not true.)

There are a number of functions, which we will describe below:

  • tabulate witnesses one direction of the isomorphism between Loc_D_i -> X and D_i[X], since D_i is representable. The other direction is index, which takes D_i[X] and a Loc_D_i and returns an X.
  • fold is the unique function determined by the initial algebra on List. Additionally, suppose that we have a function * which combines two algebras by taking their cartesian product,
  • bucket is a natural transformation which takes a D_i[X] and buckets it into D_j[List[X]] based on some function which assigns elements in D_i to slots in D_j. This is an example of a natural transformation that is not a natural transformation until it is partially applied: if we compute D_i[Loc_D_j], then we can create a natural transformation that doesn’t ever look at X; it simply “knows” where each slot of D_i needs to go in the resulting structure.

Let us now rewrite the loop in more functional terms:

tabulate (\j ->
  let weightedPoints = fold plus . filter (\i -> c[i] == j) $ x
      points = fold inc . filter (\i -> c[i] == j) $ x
  in divide (weightedPoints, points)

(Where divide is just a function which divides its arguments but checks that the divisor is not zero.) Eliminating some common sub-expressions and fusing the two folds together, we get:

tabulate (\j -> divide . fold (plus * inc) . filter (\i -> c[i] == j) $ x)

At this point, it is still not at all clear that there are any rewrites we can carry out: the filter is causing problems for us. However, because filter is testing on equality, we can rewrite it in a different way:

tabulate (\j -> divide . fold (plus * inc) . index j . bucket c $ x)

What is happening here? Rather than directly filtering for just items in cluster j, we can instead view this as bucketing x on c and then indexing out the single bucket we care about. This shift in perspective is key to the whole optimization.

Now we can apply the fundamental rule of natural transformations. Let phi = index j and f = divide . fold (plus * inc), then we can push f to the other side of phi:

tabulate (\j -> index j . fmap (divide . fold (plus * inc)) . bucket c $ x)

Now we can eliminate tabulate and index:


Finally, because we know how to efficiently implement fmap (fold f) . bucket c (as a hashreduce), we split up the fmap and join the fold and bucket:

fmap divide . hashreduce (plus * inc) c $ x

And we have achieved our fully optimized program.

All of this is research in progress, and there are lots of open questions which we have not resolved. Still, I hope this post has given you a flavor of the approach we are advocating. I am quite curious in your comments, from “That’s cool!” to “This was all done 20 years ago by X system.” Have at it!

4 Responses to “Category theory for loop optimizations”

  1. Derek Elkins says:

    I don’t have any specific comments. Some not directly related references, which you are probably aware of are Halide (, Ferry (, and don’t underestimate the traditional query optimization literature (for which, unfortunately, I don’t have any poignant references.)

  2. Anonymous says:

    I think the first sentence is somewhat superfluous, given that this is a Haskell blog.

  3. Derek: Good pointers. I also had pointed out to me privately that Flume is a dynamic approach to this sort of optimization problem.

  4. Paolo G. Giarrusso says:

    I don’t know if this was already pointed to you, but among the works of Torsten Grust, I think you might want to at least skim his PhD thesis. It’s not a collection of papers; it’s instead a book on database optimizations adapted to usage in programs (without the restrictions of SQL and the relational model) analysed using category theory, and with pointers to database literature.

    What you’re doing seems cool and I’m really interested, since I’ve done related work myself (without using explicitly a category-theoretic formalization, though).

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