Compiling Actions by Partial Evaluation, Revisited

We revisit Bondorf and Palsberg's compilation of actions using< the offline syntax-directed partial evaluator Similix (FPCA'93, JFP'96), and we compare it in detail with using an online type-directed partial evaluator. In contrast to Similix, our type-directed partial evaluator is idempotent and requires no "binding-time improvements." It also appears to consume about 7 times less space and to be about 28 times faster than Similix, and to yield residual programs that are perceptibly more efficient than those generated by Similix

Offline Specialisation in Prolog Using a Hand-Written Compiler Generator

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 the "cogen" approach is also applicable to the specialisation of logic programs (called partial deduction when applied to pure logic programs) and leads to effective specialisers. Moreover, using good binding-time annotations, the speed-ups of the specialised programs are comparable to the speed-ups obtained with online specialisers. The paper first develops a generic approach to offline partial deduction and then a specific offline partial deduction method, leading to the offline system LIX for pure logic programs. While this is a usable specialiser by itself, its specialisation strategy is used to develop the "cogen" system LOGEN. Given a program, a specification of what inputs will be static, and an annotation specifying which calls should be unfolded, LOGEN generates a specialised specialiser for the program at hand. Running this specialiser with particular values for the static inputs results in the specialised program. While this requires two steps instead of one, the efficiency of the specialisation process is improved in situations where the same program is specialised multiple times. The paper also presents and evaluates an automatic binding-time analysis that is able to derive the annotations. While the derived annotations are still suboptimal compared to hand-crafted ones, they enable non-expert users to use the LOGEN system in a fully automated way Finally, LOGEN is extended so as to directly support a large part of Prolog's declarative and non-declarative features and so as to be able to perform so called mixline specialisations. In mixline specialisation some unfolding decisions depend on the outcome of tests performed at specialisation time instead of being hardwired into the specialiser

Computation over partial information : a principled approach to accurate partial evaluation

On est habitué à penser comme suit à un programme qui exécute: une donnée entre (un input), un moment passe, et un résultat ressort. On assume tacitement de l'information complète sur le input, le résultat, et n'importe quels résultats intermédiaires. Dans ce travail-ci, on demande ce que ça voudrait dire d'exécuter un programme sur de l'information partielle. Comme réponse possible, on introduit l'interprétation partielle, notre contribution principale. Au lieu de considérer un seul input, on considère un ensemble de inputs possibles. Au lieu de calculer un seul résultat, on calcule un ensemble de résultats possibles, et des ensembles de résultats intermédiaires possibles. On approche l'interprétation partielle à partir du problème de la spécialisation de programme: l'optimisation d'un programme pour certains inputs. Faire ça automatiquement porte historiquement le nom d'évaluation partielle. Ç'a été appliqué avec succès à plusieurs problèmes spécifiques. On croit que ça devrait être un outil de programmation commun, pour spécialiser des librairies générales pour usage spécifique - mais ce n'est pas le cas. Souvent, une implantation donnée de l'évaluation partielle ne fonctionne pas uniformément bien sur tous les programmes. Ça se prête mal à un usage commun. On voit ce manque de régularité comme un problème de précision: si l'évaluateur partiel était très précis, il trouverait la bonne spécialisation, indépendamment de notre style de programme. On propose donc une approche de principe à l'évaluation partielle, visant la précision complète, retirée d'exemples particuliers. On reformule l'évaluation partielle pour la baser sur l'interprétation partielle: le calcul sur de l'information partielle. Si on peut déterminer ce qu'on sait sur chaque donnée dans le programme, on peut décider quelles opérations peuvent être éliminées pour spécialiser le programme: les opérations dont le résultat est unique. On définit une représentation d'ensembles qui ressemble à la définition en compréhension, en mathématiques. On modifie un interpréteur pour des programmes fonctionnels, pour qu'il calcule sur ces ensembles. On utilise un solver SMT pour réaliser les opérations sur les ensembles. Pour assurer la terminaison de l'interpréteur modifié, on applique des idées de l'interprétation abstraite: le calcul de point fixe, et le widening. Notre implantation initiale produit de bons résultats, mais elle est lente pour de plus gros exemples. On montre comment l'accélérer mille fois, en dépendant moins de SMT.We are used to the following picture of an executing program: an input is provided, the program runs for a while, and a result comes out. We tacitly assume complete information about the input, the result, and any intermediate results in between. In this work, we ask what it would mean to execute a program over partial information. As a possible answer, we introduce partial interpretation, our main contribution. Instead of considering a unique input, we consider a set of possible inputs. Instead of computing a unique result, we compute a set of possible results, and sets of possible intermediate results. We approach partial interpretation from the problem of program specialization: the optimization of a program's execution time for certain inputs. Doing this automatically is historically known as partial evaluation. Partial evaluation has been applied successfully to many specific problems. We believe it should be a mainstream programming tool, to specialize general libraries for specific use - but such a tool has not been delivered. One common problem is that a given implementation of partial evaluation is inconsistent: it does not work uniformly well on all input programs. This inconsistency makes it unsuited for mainstream use. We view this inconsistency as an accuracy problem: if the partial evaluator was very accurate, it would find the correct specialization, no matter how we present the input program. We therefore propose a principled approach to partial evaluation, aimed at complete accuracy, removed from any particular example program. We reformulate partial evaluation to root it in partial interpretation: computation over partial information. If we can determine what we know about every piece of data in the program, we can decide which operations can be removed to specialize the program: those operations whose result is uniquely known. We represent sets with a kind of mathematical set comprehension. We modify an interpreter for functional programs, to compute over these sets. We use an SMT solver (Satisfiability Modulo Theories) to perform set operations. To ensure termination of the modified interpreter, we apply ideas from abstract interpretation: fixed point computation, and widening. Our initial implementation produces good results, but it is slow for larger examples. We show how to speed it up a thousandfold, by relying less on SMT

On Fast Large-Scale Program Analysis in Datalog

Designing and crafting a static program analysis is challenging due to the complexity of the task at hand. Among the challenges are modelling the semantics of the input language, finding suitable abstractions for the analysis, and handwriting efficient code for the analysis in a traditional imperative language such as C++. Hence, the development of static program analysis tools is costly in terms of development time and resources for real world languages. To overcome, or at least alleviate the costs of developing a static program analysis, Datalog has been proposed as a domain specific language (DSL).With Datalog, a designer expresses a static program analysis in the form of a logical specification. While a domain specific language approach aids in the ease of development of program analyses, it is commonly accepted that such an approach has worse runtime performance than handcrafted static analysis tools. In this work, we introduce a new program synthesis methodology for Datalog specifications to produce highly efficient monolithic C++ analyzers. The synthesis technique requires the re-interpretation of the semi-naïve evaluation as a scaffolding for translation using partial evaluation. To achieve high-performance, we employ staged compilation techniques and specialize the underlying relational data structures for a given Datalog specification. Experimentation on benchmarks for large-scale program analysis validates the superior performance of our approach over available Datalog tools and demonstrates our competitiveness with state-of-the-art handcrafted tools

Specializing Interpreters using Offline Partial Deduction

We present the latest version of the Logen partial evaluation system for logic programs. In particular we present new binding-types, and show how they can be used to effectively specialise a wide variety of interpreters.We show how to achieve Jones-optimality in a systematic way for several interpreters. Finally, we present and specialise a non-trivial interpreter for a small functional programming language. Experimental results are also presented, highlighting that the Logen system can be a good basis for generating compilers for high-level languages

Computing eigenvalues of real symmetric matrices\ud with rational filters in real arithmetic

Powerful algorithms have recently been proposed for computing eigenvalues of large matrices by methods related to contour integrals; best known are the works of Sakurai and coauthors and Polizzi and coauthors. Even if the matrices are real symmetric, most such methods rely on complex arithmetic, leading to expensive linear systems to solve. An appealing technique for overcoming this starts from the observation that certain discretized contour integrals are equivalent to rational interpolation problems, for which there is no need to leave the real axis. Investigation shows that using rational interpolation per se suffers from instability; however, related techniques involving real rational filters can be very effective. This article presents a technique of this kind that is related to previous work published in Japanese by Murakami