Emil Axelsson and Koen Claessen wrote a functional pearl last year about Using Circular Programs for Higher-Order Syntax.

About 6 months ago I had an opportunity to play with this approach in earnest, and realized we can speed it up a great deal. This has kept coming up in conversation ever since, so I've decided to write up an article here.

In my bound library I exploit the fact that monads are about substitution to make a monad transformer that manages substitution for me.

Here I'm going to take a more coupled approach.

To have a type system with enough complexity to be worth examining, I'll adapt Dan Doel's UPTS, which is a pure type system with universe polymorphism. I won't finish the implementation here, but from where we get it should be obvious how to finish the job.


Lenses are a great way to deal with functional references, but there are two common issues that arise from their use.

  1. There is a long-standing folklore position that lenses do not support polymorphic updates. This has actually caused a fair bit of embarrassment for the folks who'd like to incorporate lenses in any Haskell record system improvement.
  2. Access control. It'd be nice to have read-only or write-only properties -- "one-way" or "mirrored" lenses, as it were. Moreover, lenses are commonly viewed as an all or nothing proposition, in that it is hard to mix them with arbitrary user functions.
  3. Finally there is a bit of a cult around trying to generalize lenses by smashing a monad in the middle of them somewhere, it would be nice to be able to get into a list and work with each individual element in it without worrying about someone mucking up our lens laws, and perhaps avoid the whole generalized lens issue entirely.

We'll take a whack at each of these concerns in turn today.

Andrej Bauer recently gave a really nice talk on how you can exploit side-effects to make a faster version of Martin Escardo's pseudo-paradoxical combinators.

A video of his talk is available over on his blog, and his presentation is remarkably clear, and would serve as a good preamble to the code I'm going to present below.

Andrej gave a related invited talk back at MSFP 2008 in Iceland, and afterwards over lunch I cornered him (with Dan Piponi) and explained how you could use parametricity to close over the side-effects of monads (or arrows, etc) but I think that trick was lost in the chaos of the weekend, so I've chosen to resurrect it here, and improve it to handle some of his more recent performance enhancements, and show that you don't need side-effects to speed up the search after all!


Last time, I showed that we can build a small parsec clone with packrat support.

This time I intend to implement packrat directly on top of Parsec 3.

One of the main topics of discussion when it comes to packrat parsing since Bryan Ford's initial release of Pappy has been the fact that in general you shouldn't use packrat to memoize every rule, and that instead you should apply Amdahl's law to look for the cases where the lookup time is paid back in terms of repetitive evaluation, computation time and the hit rate. This is great news for us, since, we only want to memoize a handful of expensive combinators.


You never heard of the Millenium Falcon? It's the ship that made the Kessel Run in 12 parsecs.

I've been working on a parser combinator library called trifecta, and so I decided I'd share some thoughts on parsing.

Packrat parsing (as provided by frisby, pappy, rats! and the Scala parsing combinators) and more traditional recursive descent parsers (like Parsec) are often held up as somehow different.

Today I'll show that you can add monadic parsing to a packrat parser, sacrificing asymptotic guarantees in exchange for the convenient context sensitivity, and conversely how you can easily add packrat parsing to a traditional monadic parser combinator library.


Today I hope to start a new series of posts exploring constructive abstract algebra in Haskell.

In particular, I want to talk about a novel encoding of linear functionals, polynomials and linear maps in Haskell, but first we're going to have to build up some common terminology.

Having obtained the blessing of Wolfgang Jeltsch, I replaced the algebra package on hackage with something... bigger, although still very much a work in progress.


Last time, I said that I was going to put our cheap new free monad to work, so let's give it a shot.


Last time, I started exploring whether or not Codensity was necessary to improve the asymptotic performance of free monads.

This time I'll show that the answer is no; we can get by with something smaller.


A couple of years back Janis Voigtländer wrote a nice paper on how one can use the codensity monad to improve the asymptotic complexity of algorithms using the free monads. He didn't use the name Codensity in the paper, but this is essentially the meaning of his type C.

I just returned from running a workshop on domain-specific languages at McMaster University with the more than able assistance of Wren Ng Thornton. Among the many topics covered, I spent a lot of time talking about how to use free monads to build up term languages for various DSLs with simple evaluators, and then made them efficient by using Codensity.

This has been shown to be a sufficient tool for this task, but is it necessary?


A couple of days ago, I gave a talk at Boston Haskell about a shiny new speculative evaluation library, speculation on hackage, that I have implemented in Haskell. The implementation is based on the material presented as "Safe Programmable Speculative Parallelism" by Prakash Prabhu, G Ramalingam, and Kapil Vaswani at last month's PLDI.

I've uploaded a copy of my slides here:

* Introducing Speculation [PowerPoint | PDF]


I've uploaded a package named heaps to Hackage that provides Brodal-Okasaki bootstrapped skew-binomial heaps in Haskell.

I've uploaded a package named rad to Hackage for handling reverse-mode automatic differentiation in Haskell.

I was asked to give two talks at the Boston Area Haskell User Group for this past Tuesday. The first was pitched at a more introductory level and the second was to go deeper into what I have been using monoids for lately.

The first talk covers an introduction to the mathematical notion of a monoid, introduces some of the features of my Haskell monoids library on hackage, and starts to motivate the use of monoidal parallel/incremental parsing, and the modification use of compression algorithms to recycle monoidal results.

The second talk covers a way to generate a locally-context sensitive parallel/incremental parser by modifying Iteratees to enable them to drive a Parsec 3 lexer, and then wrapping that in a monoid based on error productions in the grammar before recycling these techniques at a higher level to deal with parsing seemingly stateful structures, such as Haskell layout.

  1. Introduction To Monoids (PDF)
  2. Iteratees, Parsec and Monoids: A Parsing Trifecta (PDF)

Due to a late start, I was unable to give the second talk. However, I did give a quick run through to a few die-hards who stayed late and came to the Cambridge Brewing Company afterwards. As I promised some people that I would post the slides after the talk, here they are.

The current plan is to possibly give the second talk in full at either the September or October Boston Haskell User Group sessions, depending on scheduling and availability.

[ Iteratee.hs ]

This post is a bit of a departure from my recent norm. It contains no category theory whatsoever. None. I promise.

Now that I've bored away the math folks, I'll point out that this also isn't a guide to better horticulture. Great, there goes the rest of you.

Instead, I want to talk about Bloom filters, Bloom joins for distributed databases and some novel extensions to them that let you trade in resources that we have in abundance for ones that are scarce, which I've been using for the last few months and which I have never before seen before in print. Primarily because I guess they have little to do with the strengths of Bloom filters.