The so called “cogen approach” to program specialisation, writing a compiler generator instead of a specialiser, has been used with considerable success in partial evaluation of both functional and imperative languages. This paper demonstrates that this approach is also applicable to partial evaluation of logic programming languages, also called partial deduction. Self-application has not been as much in focus in logic programming as for functional and imperative languages, and the attempts to self-apply partial deduction systems have, of yet, not been altogether that successful. So, especially for partial deduction, the cogen approach should prove to have a considerable importance when it comes to practical applications. This paper first develops a generic offline partial deduction technique for pure logic programs, notably supporting partially instantiated datastructures via binding types. From this a very efficient cogen is derived, which generates very efficient generating extensions (executing up to several orders of magnitude faster than current online systems) which in turn perform very good and non-trivial specialisation, even rivalling existing online systems. All this is supported by extensive benchmarks. Finally, it is shown how the cogen can be extended to directly support a large part of Prolog’s declarative and non-declarative features and how semi-online specialisation can be efficiently integrated.

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. Recently, partial deduction of logic programs has been extended to conceptually embed folding. To this end, partial deductions are no longer computed of single atoms, but rather of entire conjunctions; Hence the term "conjunctive partial deduction". Conjunctive partial deduction aims at achieving unfold/fold-like program transformations such as tupling and deforestation within fully automated partial deduction. However, its merits greatly surpass that limited context: Also other major efficiency improvements are obtained through considerably improved side-ways information propagation. In this extended abstract, we investigate conjunctive partial deduction in practice. We describe the concrete options used in the implementation(s), look at abstraction in a practical Prolog context, include and discuss an extensive set of benchmark results. From these, we can conclude that conjunctive partial deduction indeed pays off in practice, thoroughly beating its conventional precursor on a wide...

The relation between partial deduction and the unfold/fold approach has been a matter of intense discussion. In this paper we consolidate the advantages of the two approaches and provide an extended partial deduction framework in which most of the tupling and deforestation transformations of the fold/unfold approach, as well the current partial deduction transformations, can be achieved. Moreover, most of the advantages of partial deduction, e.g. lower complexity and a more detailed understanding of control issues, are preserved. We build on well-defined concepts in partial deduction and present a conceptual embedding of folding into partial deduction, called conjunctive partial deduction. Two minimal extensions to partial deduction are proposed: using conjunctions of atoms instead of atoms as the principle specialisation entity and also renaming conjunctions of atoms instead of individual atoms. Correctness results for the extended framework (with respect to computed answer semantics and finite failure semantics) are given. Experiments with a prototype implementation are presented, showing that, somewhat to our surprise, conjunctive partial deduction not only handles the removal of unnecessary variables, but also leads to substantial improvements in specialisation for standard partial deduction examples. 1

. A partial deduction strategy for logic programs usually uses an abstraction operation to guarantee the finiteness of the set of atoms for which partial deductions are produced. Finding an abstraction operation which guarantees finiteness and does not loose relevant information is a difficult problem. In earlier work Gallagher and Bruynooghe proposed to base the abstraction operation on characteristic paths and trees. A characteristic tree captures the relevant structure of the generated partial SLDNF-tree for a given goal. Unfortunately the abstraction operations proposed in the earlier work do not always produce more general atoms and do not always preserve the characteristic trees. This problem has been solved for purely determinate unfolding rules and definite programs in [12, 13] by using constraints inside the partial deduction process. In this paper we propose an alternate solution which achieves the preservation of characteristic trees for any unfolding rule, normal logic prog...

Functional logic languages combine the operational principles of the most important declarative programming paradigms, namely functional and logic programming. Inductively sequential programs admit the definition of optimal computation strategies and are the basis of several recent (lazy) functional logic languages. In this paper, we define a partial evaluator for inductively sequential functional logic programs. We prove strong correctness of this partial evaluator and show that the nice properties of inductively sequential programs carry over to the specialization process and the specialized programs. In particular, the structure of the programs is preserved by the specialization process. This is in contrast to other partial evaluation methods for functional logic programs which can destroy the original program structure. Finally, we present some experiments which highlight the practical advantages of our approach. 1 Introduction Functional logic languages combine the operational p...

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...

We have recently defined a framework for Narrowing-driven Partial Evaluation (NPE) of functional logic programs. This method is as powerful as partial deduction of logic programs and positive supercompilation of functional programs. Although it is possible to treat complex terms containing primitive functions (e.g. conjunctions or equations) in the NPE framework, its basic control mechanisms do not allow for effective polygenetic specialization of these complex expressions. We introduce a sophisticated unfolding rule endowed with a dynamic narrowing strategy which permits flexible scheduling of the elements (in conjunctions) which are reduced during specialization. We also present a novel abstraction operator which carefully considers primitive functions and is the key to achieving accurate polygenetic specialization. The abstraction operator extends some recent partitioning techniques defined in the framework of conjunctive partial deduction. We provide experimental results obtained from an implementation using the INDY system which demonstrate that the control refinements produce better specializations.

Functional logic languages with a complete operational semantics are based on narrowing, a unification-based goal-solving mechanism which subsumes the reduction principle of functional languages and the resolution principle of logic languages. Needed narrowing is an optimal narrowing strategy and the basis of several recent functional logic languages. In this paper, we define a partial evaluator for functional logic programs based on needed narrowing. We prove strong correctness of this partial evaluator and show that the nice properties of needed narrowing carry over to the specialization process and the specialized programs. In particular, the structure of the specialized programs provides for the application of optimal evaluation strategies. This is in contrast to other partial evaluation methods for functional logic programs which can change the original program structure in a negative way. Finally, we present some experiments which highlight the practical advantages of our approach.

Indy Indy Indy 1 Introduction innermost lazy M. Alpuente M. Falaschi G. Vidal March 4, 1998 Experiments with the Call-by-Value Partial Evaluator polyvariant polygenetic local global closedness unfolding abstraction This work has been partially supported by CICYT under grant TIC 95-0433-C03-03 and by HCM project CONSOLE. DSIC, U. P. Valencia, Camino de Vera s/n, 46022 Valencia, Spain ( alpuente,gvidal @dsic.upv.es). Dip. Matematica e Informatica, U. Udine, Via delle Scienze 206, 33100 Udine, Italy (falaschi@dimi.uniud.it). This paper summarizes our experience gained using the system, a narrowing-driven partial evaluator for functional logic programs which combines in a useful and effective way the propagation of partial data structures, by means of logical variables and unification, with better opportunities for optimization thanks the functional dimension. allows the user to select either a call-by-value ( , eager) or a call-by-name (outside-in, ) narrowing strategy to construct lo...