Binding-Time Analysis in Partial Evaluation: One Size Does Not Fit All

Christensen, Niels H.; Glück, Robert; Laursen, Søren Kock

doi:10.1007/3-540-46562-6_7

Cited by 6 publications

(10 citation statements)

References 17 publications

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“…We then show that an offline strategy, together with a maximally polyvariant BTA [Christensen et al 2000], has the same accuracy in finding static information as our online system. This is contrary to the common belief that a chief disadvantage of offline strategies is the imprecision caused by the BTA which guides the offline specialization process.…”

Section: Contributionmentioning

confidence: 96%

Offline partial evaluation can be as accurate as online partial evaluation

Christensen

Glück

2004

ACM Trans. Program. Lang. Syst.

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We show that the accuracy of online partial evaluation, or polyvariant specialization based on constant propagation, can be simulated by offline partial evaluation using a maximally polyvariant binding-time analysis. We point out that, while their accuracy is the same, online partial evaluation offers better opportunities for powerful generalization strategies. Our results are presented using a flowchart language with recursive procedures.

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Section: Contributionmentioning

confidence: 96%

Offline partial evaluation can be as accurate as online partial evaluation

Christensen

Glück

2004

ACM Trans. Program. Lang. Syst.

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“…Stage 2, given the annotated program and the values of static inputs, only needs to obey the annotations; argument comparisons are not needed [Bondorf 1991;Christensen et al 2000;Consel 1993;Consel and Noël 1996;Das 1998;Glenstrup et al 1999;Glück et al 1995;Jones et al 1993;Hatcliff et al 1999;Mogensen 1988]. …”

Section: Causes Of Nontermination Duringmentioning

confidence: 99%

“…Traditionally, partial evaluation has employed monovariant binding-time divisions to avoid the risk of code explosion in the residual program Christensen et al [2000]. have shown that polyvariant binding-time divisions can be viable and desirable in some cases.…”

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confidence: 99%

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Termination analysis and specialization-point insertion in offline partial evaluation

Glenstrup

Jones

2005

ACM Trans. Program. Lang. Syst.

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Recent research suggests that the goal of fully automatic and reliable program generation for a broad range of applications is coming nearer to feasibility. However, several interesting and challenging problems remain to be solved before it becomes a reality. Solving them is also necessary, if we hope ever to elevate software engineering from its current state (a highly developed handiwork) into a successful branch of engineering, capable of solving a wide range of new problems by systematic, well-automated and well-founded methods.A key problem in all program generation is termination of the generation process. This article focuses on off-line partial evaluation and describes recent progress towards automatically solving the termination problem, first for individual programs, and then for specializers and "generating extensions," the program generators that most offline partial evaluators produce.The technique is based on size-change graphs that approximate the changes in parameter sizes at function calls. We formulate a criterion, bounded anchoring, for detecting parameters known to be bounded during specialization: a bounded parameter can act as an anchor for other parameters. Specialization points necessary for termination are computed by adding a parameter that tracks call depth, and then selecting a specialization point in every call loop where it is unanchored. By generalizing all unbounded parameters, we compute a binding-time division which together with the set of specialization points guarantees termination.Contributions of this article include a proof, based on the operational semantics of partial evaluation with memoization, that the analysis guarantees termination; and an in-depth description of safety of the increasing size approximation operator required for termination analysis in partial evaluation.Initial experiments with a prototype shows that the analysis overall yields binding-time divisions that can achieve a high degree of specialization, while still guaranteeing termination.The article ends with a list of challenging problems whose solution would bring the community closer to the goal of broad-spectrum, fully automatic and reliable program generation.

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“…However, the new method is not able to pass the so-called "KMPtest". To overcome this drawback, the information of a traditional binding-time analysis [5] (i.e., specifying which componentes of the initial call are dynamic or static) should be integrated into the partial evaluator (currently, only termination is taken into account with no assumption on the static data).…”

Section: Online Vs Offlinementioning

confidence: 99%

An offline partial evaluator for curry programs

Ramos

Silva

Vidal

2005

Proceedings of the 2005 ACM SIGPLAN Workshop on Curry and Functional Logic Programming

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Narrowing-driven partial evaluation is a powerful technique for the specialization of functional logic programs. In this paper, we describe the implementation of a narrowing-driven partial evaluator for Curry programs which follows the offline approach to ensuring termination. Although the new partial evaluator is less precise than previous (online) partial evaluators for Curry, it is much faster and, thus, allows the specialization of larger programs.

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Binding-Time Analysis in Partial Evaluation: One Size Does Not Fit All

Cited by 6 publications

References 17 publications

Offline partial evaluation can be as accurate as online partial evaluation

Offline partial evaluation can be as accurate as online partial evaluation

Termination analysis and specialization-point insertion in offline partial evaluation

An offline partial evaluator for curry programs

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