Theory

Transactional Memory with data invariants (draft sequel to the STM-Haskell paper)

Transactional memory with data invariants
From the abstract:
This paper introduces a mechanism for asserting invariants that are maintained by a program that uses atomic memory transactions.
The idea is simple: the programmer write always E where E is an expression that should be preserved by every atomic update for the remainder of the program's execution. We have extended STM Haskell to dynamically check always statements atomically with the user's updates: the result is that we can identify precisely which update is the first one to break an invariant.
This seems connected to Typed Contracts for Functional Programming by Ralf Hinze, Johan Jeuring, and Andres Löh (noticed on the blog of Dominic Fox).
Maybe this year design-by-contract is the hot subject?

I haven't gotten far enough into either of these papers to have much opinion, but the motivational paragraph at the beginning of the Typed Functional Contracts paper grabbed my attention instantly, and I know I want more STM in my applications, so I look forward to a few enjoyable hours.

Towards Applicative Relational Programming

This 10-page 1992 article, Towards Applicative Relational Programming by Ibrahim & van Enden, has just appeared on ArXiv, which asked the question of how to combine functional and relational programming.


This is fairly well-trodden ground now, with approaches such as Miller's lambda-Prolog and Saraswat's constraint-lambda calculus being well establsihed, but this paper offers a rather different approach, based on the Henkin-Monk-Tarski approach of cylindric algebras, which were devised as a means of formalising predicate logic in equational logic - I'm familiar with them in the context of modal logic, since they offer a means for handling quantifiers by means of modalities, where [] is used to express forall and <> is used to express exists.


The treatment is nice, and is recommended to LtUers interested in having an arsenal of techniques for bridging the declarative divide.

A Tail-Recursive Machine with Stack Inspection

A Tail-Recursive Machine with Stack Inspection. John Clements and Matthias Felleisen, TOPLAS 2004.

Security folklore holds that a security mechanism based on stack inspection is incompatible with a global tail call optimization policy... In this article, we prove this widely held belief wrong. ... Our machine is surprisingly simple and suggests that tail calls are as easy to implement in a security setting as they are in a conventional one.

I don't believe we've discussed this paper before, although it was mentioned in this thread. Tail calls have been a topic of discussion here several times. [1][2][3]

Rho calculus

The Rho-calculus is a calculus of pattern matching that embeds the lambda-calculus in a very simple manner, and also naturally accomodates a number of extensions of the lambda-calculus. It encodes the results of pattern matching in a manner that ensures confluence of the whole calculus.

It was proposed by Horatiu Cirstea and Claude Kirchner in 1998, and Matching Power, a 2001 RTA paper, is maybe the nicest introduction to the calculus. Kirchner's research group maintains a list of papers.

Postscript There was an LtU classic story on the rho-calculus as well...

Semantic Distance: NLP Not a Resource Sink

Following the story on Mind Mappers and other RDF comments of late, I thought this NLP slide show (PDF) should get a story. Dr. Adrian Walker offers an interesting perspective in a friendly crayon-colored format, including a critique of RDF. Source site Internet Business Logic has other offerings including an online demo.

Module Mania: A Type-Safe, Separately Compiled, Extensible Interpreter

Module Mania: A Type-Safe, Separately Compiled, Extensible Interpreter

To illustrate the utility of a powerful modules language, this paper presents the embedded interpreter Lua-ML. The interpreter combines extensibility and separate compilation without compromising type safety. Its types are extended by applying a sum constructor to built-in types and to extensions, then tying a recursive knot using a two-level type; the sum constructor is written using an ML functor. The initial basis is extended by composing initialization functions from individual extensions, also using ML functors.

This is an excellent example of how the ML module language doesn't merely provide encapsulation but also strictly adds expressive power. It also demonstrates how a dynamic language (Lua) can be embedded in the statically-typed context of ML. Finally, it demonstrates that none of this need come at the expense of separate compilation or extensibility. Norman Ramsey's work is always highly recommended.

ClassicJava in PLT Redex

Classic Java

This collection is an implementation of (most of) ClassicJava, as defined
in "A Programmer's Reduction Semantics for Classes and Mixins," by Matthew
Flatt, Shriram Krishnamurthi, and Matthias Felleisen; in _Formal Syntax and
Semantics of Java_, Springer-Verlag LNCS 1523, pp. 241-269, 1999. A
tech-report version of the paper is also available at
<http://www.ccs.neu.edu/scheme/pubs/#tr97-293. The implementation is
written in PLT Redex, also available through PLaneT. Please consult that
package's documentation for further details.

This might be interesting to folks curious about how to formalize a real language, or about how PLT Redex works in practice.

What good is Strong Normalization in Programming Languages?

There's a neat thread about strong normalization happening on The Types Forum.

If you've ever wondered Why is it useful to have {type systems,reductions,operations,...} that always terminate? this may Illuminate you.
Here are some snippets to give you a feel for the discussion:

I think it is clearer to split Gérard's original question in two:

  1. Is strong normalization useful for programming languages ?
  2. Is the proof of strong normalization via type systems applicable for programming languages?

-- François-Régis Sinot

Termination is good:

If a program is a solution to a problem then knowing that it terminates
for any input is an important aspect of it being a solution. Often the
best way to see that it is terminating is expressing it in a language
where all programs terminate. The programming language Epigram is an
example of an experimental language which is intended to be terminating
(even though not all conditions are enforced in the current version), see
http://www.e-pig.org/ for more information.

-- Thorsten Altenkirch

Termination is good!

I think the moral sense of strong normalization is that a program
in a strictly-typed language can only diverge as a result of some
programming construct, which _explicitly_ permits looping, like
iteration, recursion etc. My favourite example here is that the
"big Omega" can be written in Algol 60, because procedure types
in this language are not fully specified.

-- Pawel Urzyczyn

Termination is good and with fixpoints is turing complete?

Another way to put this is that data structures should be definable in a
strongly-normalising language so that data access, etc. is guaranteed to
terminate. Then add fixpoints or loops to describe arbitrary computations.

-- Barry Jay

Terminating reductions allows exhaustive applications of optimizations:

In a compiler, if a set of reductions is strongly normalizing, then the compiler can apply them exhaustively to an intermediate-language term without fear of looping. (We rely on this in the MLj and SML.NET compilers' "simplify" compilation phases, which apply simple reductions and directed commuting conversions until a normal form is reached. Though it has to be said that it's not the classical SN results that are relevant here, but something more specific to our intermediate language and the simplifying reductions that we employ).

-- Andrew Kenney

Rene Vestergaard also gave a link to a 2004 discussion of strong normalization on the rewriting list.

Mechanizing Language Definitions

The problem: Most languages don't have formal specifications.

The solution (maybe): Make specification easier by using mechanized tools (for example, use Twelf).

The presentation: here (Robert Harper, ICFP'05).

The conclusion: You decide.

XML feed