Generalized algebraic data types (GADTs), sometimes known as “guarded recursive data types ” or “first-class phantom types”, are a simple but powerful generalization of the data types of Haskell and ML. Recent works have given compelling examples of the utility of GADTs, although type inference is known to be difficult. Our contribution is to show how to exploit programmer-supplied type annotations to make the type inference task almost embarrassingly easy. Our main technical innovation is wobbly types, which express in a declarative way the uncertainty caused by the incremental nature of typical type-inference algorithms.

We describe the design and current status of our effort to implement the programming model of nested data parallelism into the Glasgow Haskell Compiler. We extended the original programmingmodel and its implementation, both of which were first popularised by the NESL language, in terms of expressiveness as well as efficiency. Our current aim is to provide a convenient programming environment for SMP parallelism, and especially multicore architectures. Preliminary benchmarks show that we are, at least for some programs, able to achieve good absolute performance and excellent speedups.

The programming language Standard ML is an amalgam of two, largely orthogonal, languages. The Core language expresses details of algorithms and data structures. The Modules language expresses the modular architecture of a software system. Both languages are statically typed, with their static and dynamic semantics specified by a formal definition.

We report on an extension of Haskell with open type-level functions and equality constraints that unifies earlier work on GADTs, functional dependencies, and associated types. The contribution of the paper is that we identify and characterise the key technical challenge of entailment checking; and we give a novel, decidable, sound, and complete algorithm to solve it, together with some practically-important variants. Our system is implemented in GHC, and is already in active use.

ABSTRACT. If you want to program a parallel computer, a purely functional language like Haskell is a promising starting point. Since the language is pure, it is by-default safe for parallel evaluation, whereas imperative languages are by-default unsafe. But that doesn’t make it easy! Indeed it has proved quite difficult to get robust, scalable performance increases through parallel functional programming, especially as the number of processors increases. A particularly promising and well-studied approach to employing large numbers of processors is data parallelism. Blelloch’s pioneering work on NESL showed that it was possible to combine a rather flexible programming model (nested data parallelism) with a fast, scalable execution model (flat data parallelism). In this paper we describe Data Parallel Haskell, which embodies nested data parallelism in a modern, general-purpose language, implemented in a state-of-the-art compiler, GHC. We focus particularly on the vectorisation transformation, which transforms nested to flat data parallelism. 1

Severely resource-constrained devices present a confounding challenge to the functional programmer: we are used to having powerful abstraction facilities at our fingertips, but how can we make use of these tools on a device with an 8- or 16-bit CPU and at most tens of kilobytes of RAM? Motivated by this challenge, we have developed Flask, a domain specific language embedded in Haskell that brings the power of functional programming to sensor networks, collections of highly resource-constrained devices. Flask consists of a staging mechanism that cleanly separates node-level code from the meta-language used to generate node-level code fragments; syntactic support for embedding standard sensor network code; a restricted subset of Haskell that runs on sensor networks and constrains program space and time consumption; a higher-level “data stream ” combinator library for quickly constructing sensor network programs; and an extensible runtime that provides commonly-used services. We demonstrate Flask through several small code examples as well as a compiler that generates node-level code to execute a network-wide query specified in a SQL-like language. We show how using Flask ensures constraints on space and time behavior. Through microbenchmarks and measurements on physical hardware, we demonstrate that Flask produces programs that are efficient in terms of CPU and memory usage and that can run effectively on existing sensor network hardware.

There has been a lot of interest of late for programming languages that incorporate features from dependent type systems and proof assistants, in order to capture important invariants of the program in the types. This allows type-based program verification and is a promising compromise between plain old types and full blown Hoare logic proofs. The introduction of GADTs in GHC (and more recently type families) made such dependent typing available in an industry-quality implementation, making it possible to consider its use in large scale programs. We have undertaken the construction of a complete compiler for System F, whose main property is that the GHC type checker verifies mechanically that each phase of the compiler properly preserves types. Our particular focus is on “types rather than proofs”: reasonably few annotations that do not overwhelm the actual code. We believe it should be possible to write such a type-preserving compiler with an amount of extra code comparable to what is necessary for typical typed intermediate languages, but with the advantage of static checking. We will show in this paper the remaining hurdles to reach this goal.

We describe the design, implementation, and use of a machinecertified framework for correct compilation and execution of programs in garbage-collected languages. Our framework extends Leroy’s Coq-certified Compcert compiler and Cminor intermediate language. We add: (i) a new intermediate language, GCminor, that includes primitives for allocating memory in a garbage-collected heap and for specifying GC roots; (ii) a precise, low-level specification for a Cminor library for garbage collection; and (iii) a proven semantics-preserving translation from GCminor to Cminor plus the GC library. GCminor neatly encapsulates the interface between mutator and collector code, while remaining simple and flexible enough to be used with a wide variety of source languages and collector styles. Front ends targeting GCminor can be implemented using any compiler technology and any desired degree of verification, including full semantics preservation, type preservation, or informal trust. As an example application of our framework, we describe a compiler for Haskell that translates the Glasgow Haskell Compiler’s Core intermediate language to GCminor. To support a simple but useful memory safety argument for this compiler, the front end uses a novel combination of type preservation and runtime checks, which is of independent interest.

Several programming languages are beginning to integrate static and dynamic typing, including Racket (formerly PLT Scheme), Perl

Supercompilation is a technique due to Turchin [1] which allows for the construction of program optimisers that are both simple and extremely powerful. Supercompilation is capable of achieving transformations such as deforestation [2], function specialisation and constructor specialisation [3]. Inspired by Mitchell’s promising results [4], we show how the call-by-need supercompilation algorithm can be recast to be based explicitly on an evaluator, and in the process extend it to deal with recursive let expressions.