Enhanced sharing analysis techniques: a comprehensive evaluation

Bagnara, Roberto; Zaffanella, Enea; Hill, Patricia M.

doi:10.1017/s1471068404001978

Cited by 16 publications

(37 citation statements)

References 31 publications

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“…There have been much research in sharing analysis of logic programs [17,24,4,20,8,19,9,22,14,5] that have attempted to tame the computational aspects of this domain without compromising too much precision. One approach is reducing the number of groups that can possibly arise is due to Fecht [12] and Zaffanella et al [25] who use maximal elements to represent downward closed powersets of variables.…”

Section: Related Workmentioning

confidence: 99%

Lazy Set-Sharing Analysis

Li¹,

King²,

Lu³

2006

Functional and Logic Programming

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Abstract. Sharing analysis is widely deployed in the optimisation, specialisation and parallelisation of logic programs. Each abstract unification operation over the classic Jacobs and Langen domain involves the calculation of a closure operation that has exponential worst-case complexity. This paper explores a new tactic for improving performance: laziness. The idea is to compute partial sharing information eagerly and recover full sharing information lazily. The net result is an analysis that runs in a fraction of the time of the classic analysis and yet has comparable precision.

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Section: Related Workmentioning

confidence: 99%

Lazy Set-Sharing Analysis

Li¹,

King²,

Lu³

2006

Functional and Logic Programming

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“…Both frameworks track set-sharing alone, ie., they do not trace sharing as one component of a product domain [9]. This was partly to isolate the impact of collapsing on set-sharing from the effect of other domains (quantifying these interactions even for more conventional forms of sharing is a long study within itself [3]) and partly as an experiment in worstcase sharing. The rationale was that if closure collapsing had little impact in this scenario, then it would not warrant investigating how the technique can be composed with other domains.…”

Section: I M P L E M E N T a T I O Nmentioning

confidence: 99%

“…The most commonly deployed tactic for reducing the number of closure calculations is to combine set-sharing with other abstract domains [3,9] that can be used to determine whether closure calculation is actually unnecessary. This is not merely a computational tactic, but also a way to improve precision.…”

Section: R E L a T E D W O R Kmentioning

confidence: 99%

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Collapsing Closures

King

2006

Logic Programming

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Abstract. Ad e s c r i p t i o ni nt h eJ a c o b sa n dL a n g e nd o m a i ni sas e to f sharing groups where each sharing group is a set of program variables. The presence of a sharing group in a description indicates that all the variables in the group can be bound to terms that contain a common variable. The expressiveness of the domain, alas, is compromised by its intractability. Not only are descriptions potentially exponential in size, but abstract unification is formulated in terms of an operation, called closure under union, that is also exponential. This paper shows how abstract unification can be reformulated so that closures can be collapsed in two senses. Firstly, one closure operation can be folded into another so as to reduce the total number of closures that need to be computed. Secondly, the remaining closures canb ea p p l i e dt os m a l l e rd e s c r i p t i o n s . Therefore, although the operation remains exponential, the overhead of closure calculation is reduced. Experimental evaluation suggests that the cost of analysis can be substantially reduced by collapsing closures. 1I n t r o d u c t i o nThe philosophy of abstract interpretation is to simulate the behaviour of a program without actually running it. This is accomplished by replacing each operation in the program with an abstract analogue that operates, not on the concrete data, but a description of the data. The methodology applied in abstract interpretation is first to focus on the data, that is, pin down the relationship between the concrete data and a description, and then devise abstract operations that preserve this relationship. This amounts to showing that if the input to the abstract operation describes the input to the concrete operation, then the output of the abstract operation faithfully describes the output of the concrete operation. When this methodology is applied in logic programming, the focus is usually on the operation of abstract unification since this is arguably the most complicated domain operation. The projection operation, that merely removes information from a description, is rarely a major concern.In this paper, we revisit the projection operation of the classic set-sharing domain and we argue that the complexity of the abstract unification (amgu) operation can be curbed by the careful application of a reformulated projection operation. The computational problem at the heart of amgu is the closure under union operation [14] that operates on sharing abstractions which are constructed from sets of sharing groups. Each sharing group is, in turn, a set of program variables. Closure under union operation repeatedly unions together sets of sharing groups, drawn from a given sharing abstraction, until no new sharing group can be obtained. This operation is inherently exponential, hence the interest in different, and possibly more tractable, encodings of set-sharing [8,10]. However, even the most creative and beautiful set-sharing encoding proposed thus far [8], does not entirely finesse the complexity of clos...

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“…We start with the precondition: a 0 = {{xs. augmented with extra information related to groundness [11]. …”

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confidence: 99%