The Comonad.Reader

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]

This package provides speculative function application and speculative folds. Speculative STM transactions take the place of the transactional rollback machinery from the paper, but transactions are not always required in pure code. To get a feel for the shape of the library, here is an excerpt from the documentation for one of the combinators:


spec :: Eq a => a -> (a -> b) -> a -> b


spec g f a evaluates f g while forcing a, if g == a then f g is returned, otherwise f a is evaluated and returned. Furthermore, if the argument has already been evaluated, we skip the f g computation entirely. If a good guess at the value of a is available, this is one way to induce parallelism in an otherwise sequential task. However, if the guess isn't available more cheaply than the actual answer, then this saves no work and if the guess is wrong, you risk evaluating the function twice. Under high load, since f g is computed via the spark queue, the speculation will be skipped and you will obtain the same answer as f $! a.

ASCII art time-lines of how this can speed up evaluation are available in both the slides and the documentation linked to above, but assuming an otherwise serial problem, you effectively wager otherwise idle CPU time and the time to generate your guess on the quality of your guess.

Note that numSparks# feature request that was mentioned in the slides has already been implemented in GHC HEAD, and support shall be added to improve the performance of the speculative STM transactions under high load as mentioned in the slides.

I've uploaded a package named heaps to Hackage that provides Brodal-Okasaki bootstrapped skew-binomial heaps in Haskell.
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I've uploaded a package named rad to Hackage for handling reverse-mode automatic differentiation in Haskell.
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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.

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