Program specialisation aims at improving the overall performance of programs by performing source to source transformations. A common approach within functional and logic programming, known respectively as partial evaluation and partial deduction, is to exploit partial knowledge about the input. It is achieved through a well-automated application of parts of the Burstall-Darlington unfold/fold transformation framework. The main challenge in developing systems is to design automatic control that ensures correctness, efficiency, and termination. This survey and tutorial presents the main developments in controlling partial deduction over the past 10 years and analyses their respective merits and shortcomings. It ends with an assessment of current achievements and sketches some remaining research challenges.

Partial deduction strategies for logic programs often use an abstraction operator to guarantee the finiteness of the set of goals for which partial deductions are produced. Finding an abstraction operator which guarantees finiteness and does not lose relevant information is a difficult problem. In earlier work Gallagher and Bruynooghe proposed to base the abstraction operator on characteristic paths and trees, which capture the structure of the generated incomplete SLDNF-tree for a given goal. In this paper we exhibit the advantages of characteristic trees over purely syntactical measures: if characteristic trees can be preserved upon generalisation, then we obtain an almost perfect abstraction operator, providing just enough polyvariance to avoid any loss of local specialisation. Unfortunately, the abstraction operators proposed in earlier work do not always preserve the characteristic trees upon generalisation. We show that this can lead to important specialisation losses as well as to non-termination of the partial deduction algorithm. Furthermore, this problem cannot be adequately solved in the ordinary partial deduction setting. We therefore extend the expressivity and precision of the Lloyd and Shepherdson partial deduction framework by integrating constraints. We provide formal correctness results for the so obtained generic framework of constrained partial deduction. Within this new framework we are, among others, able to overcome the above mentioned problems by introducing an alternative abstraction operator, based on so called pruning constraints. We thus present a terminating partial deduction strategy which, for purely determinate unfolding rules, induces no loss of local specialisation due to the abstraction while ensuring correctness o...

In this paper we present a partial evaluation scheme for a "real life" subset of Prolog. This subset contains first-order built-in's, simple side-effects and the operational predicate if-then-else. We outline a denotational semantics for this subset of Prolog and show how partial deduction can be extended to specialise programs of this kind. We point out some of the problems not occurring in partial deduction and show how they can be solved in our setting. Finally we provide some results based on an implementation of the above.

Given a program and some input data, partial deduction computes a specialized program handling any remaining input more efficiently. However, controlling the process well is a rather difficult problem. In this article, we elaborate global control for partial deduction: for which atoms, among possibly infinitely many, should specialized relations be produced, meanwhile guaranteeing correctness as well as termination? Our work is based on two ingredients. First, we use the concept of a characteristic tree, encapsulating specialization behavior rather than syntactic structure, to guide generalization and polyvariance, and we show how this can be done in a correct and elegant way. Second, we structure combinations of atoms and associated characteristic trees in global trees registering “causal ” relationships among such pairs. This allows us to spot looming nontermination and perform proper generalization in order to avert the danger, without having to impose a depth bound on characteristic trees. The practical relevance and benefits of the work are illustrated through extensive experiments. Finally, a similar approach may improve upon current (on-line) control strategies for program transformation in general such as (positive) supercompilation of functional programs. It also seems valuable in the context of abstract interpretation to handle infinite domains of infinite height with more precision.

. Intensional query answering aims at providing a response to a query addressed to a knowledge base by making use of the intensional knowledge as opposed to extensional. Such a response is an abstract description of the conventional answer that can be of interest in many situations, for example it may increase the cooperativeness of the system, or it may replace the conventional answer in case access to the extensional part of the knowledge base is costly as for Mobile Systems. In this paper we present a general framework to generate intensional answers in knowledge bases adhering to the logic programming paradigm. Such a framework is based on a program transformation technique, namely Partial Evaluation, and allows for generating complete and procedurally complete (wrt SLDNF-resolution) sets of intensional answers, treating both recursion and negation conveniently. Keywords: Knowledge bases, intensional query answering, logic programs, partial evaluation 1. Introduction Intensional an...

We present the fully automatic partial deduction system ecce, which can be used to specialise and optimise logic programs. We describe the underlying principles of ecce and illustrate some of the potential application areas. Interesting possibilites of crossfertilisation with other fields such as reachability analysis of concurrent systems and inductive theorem proving are highlighted and substantiated. 1 Introduction Program specialisation, also called partial evaluation or partial deduction, is an automatic technique for program optimisation. The central idea is to specialise a given source program for a particular application domain. Program specialisation encompasses traditional compiler optimisation techniques, such as constant folding and in-lining, but uses more aggressive transformations, yielding both the possibility of obtaining (much) greater speedups and more difficulty in controlling the transformation process. In addition to achieving important speedups, program special...

Abstract Partial evaluation is a symbolic manipulation technique used to produce efficient algorithms when part of the input to the algorithm is known. Other applications of partial evaluators such as universal compilation and compiler generation are also known to be possible. A partial evaluator receives as input a program and partially known input to that program, and outputs a residual program which should run at least as efficient as the input program with restricted input. In this paper we study the case where both the input and residual programs are logic programs, being the partial evaluator itself a logic program. Up to now, partial evaluators have failed to process large "nontoy" examples. Here we present extensions to partial evaluators which will allow us to produce more efficient residual programs using less computing resources during partial evaluation. First, the introduced extensions allow the processing of large examples, which is not possible with the previous techniques. This is now possible since the extensions use less CPU time and memory consumption during the partial evaluation process. Second, the extended partial evaluator produces smaller residual programs, producing important CPU time optimizing effects. With the standard techniques, a partial evaluator will most probably act as a pessimizer, not as an optimizer. Examples are given.

Abstract not found