M.J. Plasmeijer

Term graph rewriting

Graph rewriting (also called reduction) as defined in Wadsworth [1971] was introduced in order to be able to give a more efficient implementation of functional programming languages in the form of lambda calculus or term rewrite systems: identical subterms are shared using pointers.

CLEAN: A language for functional graph rewriting

Clean is an experimental language for specifying functional computations in terms of graph rewriting. It is based on an extension of Term Rewriting Systems (TRS) in which the terms are replaced by graphs. Such a Graph Rewriting System (GRS) consists of a, possibly cyclic, directed graph, called the data graph and graph rewrite rules which specify how this data graph may be rewritten. Clean is designed to provide a firm base for functional programming. In particular, Clean is suitable as an intermediate language between functional languages and (parallel) target machine architectures. A sequential implementation of Clean on a conventional machine is described and its performance is compared with other systems. The results show that Clean can be efficiently implemented.

A Generic Programming Extension for Clean

Generic programming enables the programmer to define functions by induction on the structure of types. Defined once, such a generic function can be used to generate a specialized function for any user defined data type. Several ways to support generic programming in functional languages have been proposed, each with its own pros and cons. In this paper we describe a combination of two existing approaches, which has the advantages of both of them. In our approach overloaded functions with class variables of an arbitrary kind can be defined generically. A single generic definition defines a kind-indexed family of overloaded functions, one for each kind. For instance, the generic mapping function generates an overloaded mapping function for each kind. Additionally, we propose a separate extension that allows to specify a customized instance of a generic function for a type in terms of the generated instance for that type.

Theorem Proving for Functional Programmers

SPARKLE is a new theorem prover written in and specialized for the functional programming language CLEAN. It is mainly intended to be used by programmers for proving properties of parts of programs, combining programming and reasoning into one process. It can also be used by logicians interested in proving properties of larger programs.Two features of SPARKLE are in particular helpful for programmers. Firstly, SPARKLE is integrated in CLEAN and has a semantics based on lazy graph-rewriting. This allows reasoning to take place on the program itself, rather than on a translation that uses different concepts. Secondly, Sparkle supports automated reasoning. Trivial goals will automatically be discarded and suggestions will be given on more difficult goals.This paper presents a small example proof built in SPARKLE. It will be shown that building this proof is easy and requires little effort.

Towards an intermediate language based on graph rewriting

Lean is an experimental language for specifying computations in terms of graph rewriting. It is based on an alternative to Term Rewriting Systems (TRS) in which the terms are replaced by graphs. Such a Graph Rewriting System (GRS) consists of a set of graph rewrite rules which specify how a graph may be rewritten. Besides supporting functional programming, Lean also describes imperative constructs and allows the manipulation of cyclic graphs. Programs may exhibit non-determinism as well as parallelism. In particular, Lean can serve as an intermediate language between declarative languages and machine architectures, both sequential and parallel.

Input–Output Tools: A Language Facility for Interactive and Real-Time Systems

A conceptual model is discussed which allows the hierarchic definition of high-level input driven objects, called input-output tools, from any set of basic input primitives. An input-output tool is defined as a named object. Its most important elements are the input rule, output rule, internal tool definitions, and a tool body consisting of executable statements. The input rule contains an expression with tool designators as operands and with operators allowing for sequencing, selection, interleaving, and repetition. Input rules are similar in appearance to production rules in grammars. The input expression specifies one or more input sequences, or input patterns, in terms of tool designators. An input parser tries, at run-time, to match (physical) input tokens against active input sequences. If a match between an input token and a tool designator is found, the corresponding tool body is executed, and the output is generated according to specifications in the tool body. The control structures in the input expression allow a variety of input patterns from any number of sources. Tool definitions may occur in-line or be stored in a library. All tools are ultimately encompassed in one tool representing the program.

Efficient Distributed Memory Implementation of a Data Parallel Functional Language

We discuss why existing implementations of functional languages on MIMD-machines with distributed memory are slow. This is done by comparing the behavior of a functional program with a corresponding Occam program. The main reason is that functional languages give insufficient means to control parallelism and communication. Our approach is to support data parallelism by providing a set of primitives on arrays which allow the user to control the parallelism and communication on a high level, disabling problems like deadlocks. Only one unique version of an array may be referenced at a time. This restriction allows arrays to be updated in place and enables the user to control the space requirements of the program. The uniqueness of arrays is checked by the compiler. Experimental results demonstrate the efficiency of our data parallel functional language.

Efficient Combinator Parsers

Parser combinators enable the construction of recursive descent parsers in a very clear and simple way. Unfortunately, the resulting parsers have a polynomial complexity and are far too slow for realistic inputs. We show how the speed of these parsers can be improved by one order of magnitude using continuations. These continuations prevents the creation of intermediate data structures. Furthermore, by using an exclusive or-combinator instead of the ordinary or-combinator the complexity for deterministic parsers can be reduced from polynomial to linear. The combination of both improvements turn parser combinators from a beautiful toy to a practically applicable tool which can be used for real world applications. The improved parser combinators remain very easy to use and are still able to handle ambiguous grammars.

Interactive Functional Objects in Clean

The functional programming language Clean has a high level I/O system (version 0.8) in which complex yet efficient interactive programs can be created. In this paper we present its successor (version 1.0), the object I/O system. We consider some of the design considerations that have influenced the design of the new I/O system greatly. Key issues are compositionality, orthogonality, and extensibility. Apart from design, the object I/O system improves on its predecessor by two major contributions: programmers can introduce polymorphic local state at every (collection of) user interface component(s) and programmers can create interactive processes in a flexible way. All interface components can communicate with each other by sharing state but also using powerful message passing primitives in both synchronous, asynchronous, and uni- or bi-directional way. Using the message passing mechanism remote procedure calling can be added easily. The result is an object oriented I/O system. As in the previous system the uniqueness type system of Clean, offering the possibility to do destructive updates in a pure functional framework, plays a crucial role. Furthermore, the object I/O system makes extensive use of new type system facilities, namely type constructor classes and existential types.

The Dutch parallel reduction machine project

In November 1984 three research groups at the universities of Amsterdam, Nijmegen and Utrecht started a cooperative project sponsored by the Dutch Ministry of Science and Education (Science Council). The first phase lasting until the end of 1987 is a pilot study and has as aim to answer the following question. Is it possible and realistic to construct an efficient parallel reduction machine? The present paper gives an outline of the problems concerning parallel reduction machines and of our research towards their solutions.