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Efficient Type Inference for HigherOrder BindingTime Analysis
 In Functional Programming and Computer Architecture
, 1991
"... Bindingtime analysis determines when variables and expressions in a program can be bound to their values, distinguishing between early (compiletime) and late (runtime) binding. Bindingtime information can be used by compilers to produce more efficient target programs by partially evaluating prog ..."
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Cited by 98 (4 self)
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Bindingtime analysis determines when variables and expressions in a program can be bound to their values, distinguishing between early (compiletime) and late (runtime) binding. Bindingtime information can be used by compilers to produce more efficient target programs by partially evaluating programs at compiletime. Bindingtime analysis has been formulated in abstract interpretation contexts and more recently in a typetheoretic setting. In a typetheoretic setting bindingtime analysis is a type inference problem: the problem of inferring a completion of a λterm e with bindingtime annotations such that e satisfies the typing rules. Nielson and Nielson and Schmidt have shown that every simply typed λterm has a unique completion ê that minimizes late binding in TML, a monomorphic type system with explicit bindingtime annotations, and they present exponential time algorithms for computing such minimal completions. 1 Gomard proves the same results for a variant of his twolevel λcalculus without a socalled “lifting ” rule. He presents another algorithm for inferring completions in this somewhat restricted type system and states that it can be implemented in time O(n 3). He conjectures that the completions computed are minimal.
Binding Time Analysis: A New PERspective
 In Proceedings of the ACM Symposium on Partial Evaluation and SemanticsBased Program Manipulation (PEPM'91
, 1991
"... Given a description of the parameters in a program that will be known at partial evaluation time, a binding time analysis must determine which parts of the program are dependent solely on these known parts (and therefore also known at partial evaluation time). In this paper a binding time analysis f ..."
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Cited by 34 (6 self)
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Given a description of the parameters in a program that will be known at partial evaluation time, a binding time analysis must determine which parts of the program are dependent solely on these known parts (and therefore also known at partial evaluation time). In this paper a binding time analysis for the simply typed lambda calculus is presented. The analysis takes the form of an abstract interpretation and uses a novel formalisation of the problem of binding time analysis, based on the use of partial equivalence relations. A simple proof of correctness is achieved by the use of logical relations. 1 Introduction Given a description of the parameters in a program that will be known at partial evaluation time, a binding time analysis must determine which parts of the program are dependent solely on these known parts (and therefore also known at partial evaluation time). A binding time analysis performed prior to the partial evaluation process can have several practical benefits (see [...
Abstract Interpretation of Functional Languages: From Theory to Practice
, 1991
"... Abstract interpretation is the name applied to a number of techniques for reasoning about programs by evaluating them over nonstandard domains whose elements denote properties over the standard domains. This thesis is concerned with higherorder functional languages and abstract interpretations with ..."
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Abstract interpretation is the name applied to a number of techniques for reasoning about programs by evaluating them over nonstandard domains whose elements denote properties over the standard domains. This thesis is concerned with higherorder functional languages and abstract interpretations with a formal semantic basis. It is known how abstract interpretation for the simply typed lambda calculus can be formalised by using binary logical relations. This has the advantage of making correctness and other semantic concerns straightforward to reason about. Its main disadvantage is that it enforces the identification of properties as sets. This thesis shows how the known formalism can be generalised by the use of ternary logical relations, and in particular how this allows abstract values to deno...
The Impact of Laziness on Parallelism and the Limits of Strictness Analysis
 PROCEEDINGS HIGH PERFORMANCE FUNCTIONAL COMPUTING
, 1995
"... The major question examined by this paper is whether sufficient finegrain parallelism can be obtained from programs written in a lazy functional language. To answer this question, we have implemented a prototype compiler based on a novel approach to strictness analysis (called abstract demand propa ..."
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Cited by 7 (2 self)
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The major question examined by this paper is whether sufficient finegrain parallelism can be obtained from programs written in a lazy functional language. To answer this question, we have implemented a prototype compiler based on a novel approach to strictness analysis (called abstract demand propagation) and we have compared this implementation strategy (optimized lazy) with other implementations, viz., pure lazy and lenient. Although the optimized lazy implementation improves significantly the efficiency of the resulting programs over the pure lazy approach, it was found that little parallelism can effectively be identified. This remains true even when a new notion of laziness  speculative laziness  is introduced, notion well suited to parallel machines as it based on a parallel notion of headstrictness instead of the traditional sequential one. Our experiments also showed that when a program's result is known to be finite, then strictness analysis can generate almost as mu...
Strengthening invariants for efficient computation
 in Conference Record of the 23rd Annual ACM Symposium on Principles of Programming Languages
, 2001
"... This paper presents program analyses and transformations for strengthening invariants for the purpose of efficient computation. Finding the stronger invariants corresponds to discovering a general class of auxiliary information for any incremental computation problem. Combining the techniques with p ..."
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Cited by 6 (4 self)
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This paper presents program analyses and transformations for strengthening invariants for the purpose of efficient computation. Finding the stronger invariants corresponds to discovering a general class of auxiliary information for any incremental computation problem. Combining the techniques with previous techniques for caching intermediate results, we obtain a systematic approach that transforms nonincremental programs into ecient incremental programs that use and maintain useful auxiliary information as well as useful intermediate results. The use of auxiliary information allows us to achieve a greater degree of incrementality than otherwise possible. Applications of the approach include strength reduction in optimizing compilers and finite differencing in transformational programming.
Implementing Projectionbased Strictness Analysis
 In Glasgow Workshop on Functional Programming
, 1992
"... Projectionbased backwards strictness analysis has been understood for some years. Surprisingly, even though the method is fairly simple and quite general, no reports of its implementation have appeared. This paper describes ideas underlying our prototype implementation of the analysis for a simple ..."
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Projectionbased backwards strictness analysis has been understood for some years. Surprisingly, even though the method is fairly simple and quite general, no reports of its implementation have appeared. This paper describes ideas underlying our prototype implementation of the analysis for a simple programming language. The implementation serves as a case study before applying the method in the Glasgow Haskell compiler. 1 Introduction The method of projectionbased backwards strictness analysis for firstorder, lazy functional languages was first presented by Wadler and Hughes [8] in 1987. Since then it has been generalised by Hughes [4] and Hughes and Launchbury [3] to work for userdefined types and for polymorphism. Yet, to our knowledge, it has never been implemented even though the method is fairly simple and quite general. The time has come for projectionbased strictness analysis to meet practice. This paper describes a prototype implementation. Initially we expected that build...
Projectionbased Program Analysis
, 1994
"... Projectionbased program analysis techniques are remarkable for their ability togive highly detailed and useful information not obtainable by other methods. The rst proposed projectionbased analysis techniques were those of Wadler and Hughes for strictness analysis, and Launchbury for bindingtime ..."
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Cited by 3 (0 self)
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Projectionbased program analysis techniques are remarkable for their ability togive highly detailed and useful information not obtainable by other methods. The rst proposed projectionbased analysis techniques were those of Wadler and Hughes for strictness analysis, and Launchbury for bindingtime analysis � both techniques are restricted to analysis of rstorder monomorphic languages. Hughes and Launchbury generalised the strictness analysis technique, and Launchbury the bindingtime analysis technique, to handle polymorphic languages, again restricted to rst order. Other than a general approach to higherorder analysis suggested by Hughes, and an ad hoc implementation of higherorder bindingtime analysis by Mogensen, neither of which had any formal notion of correctness, there has been no successful generalisation to higherorder analysis. We present a complete redevelopment of monomorphic projectionbased program analysis from rst principles, starting by considering the analysis of functions (rather than programs) to establish bounds on the intrinsic power of projectionbased analysis, showing also that projectionbased analysis can capture interesting termination
Incremental computation for transformational software development
"... Given a program f and an input change, w e wish to obtain an incremental program that computes f (x y) e ciently by making use of the value of f (x), the intermediate results computed in computing f (x), and auxiliary information about x that can be inexpensively maintained. Obtaining such increment ..."
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Cited by 2 (2 self)
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Given a program f and an input change, w e wish to obtain an incremental program that computes f (x y) e ciently by making use of the value of f (x), the intermediate results computed in computing f (x), and auxiliary information about x that can be inexpensively maintained. Obtaining such incremental programs is an essential part of the transformationalprogramming approach to software development and enhancement. This paper presents a systematic approach that discovers a general class of useful auxiliary information, combines it with useful intermediate results, and obtains an e cient incremental program that uses and maintains these intermediate results and auxiliary information. We g i v e a n umbe r of examples from list processing, VLSI circuit design, image processing, etc. 1
Representing Demand by Partial Projections
 J. of Functional Programming
, 1993
"... The projectionbased strictness analysis of Wadler and Hughes is elegant and theoretically satisfying except in one respect: the need for lifting. The domains and functions over which the analysis is performed need to be transformed, leading to a less direct correspondence between analysis and progr ..."
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The projectionbased strictness analysis of Wadler and Hughes is elegant and theoretically satisfying except in one respect: the need for lifting. The domains and functions over which the analysis is performed need to be transformed, leading to a less direct correspondence between analysis and program than might be hoped for. In this paper we shall see that the projection analysis may be reformulated in terms of partial projections, so removing this infelicity. There are additional benefits of the formulation: the two forms of information captured by the projection are distinguished, and the operational significance of the range of the projection fits exactly with the theory of unboxed types. 1 Introduction The method of projectionbased backwards strictness analysis for firstorder, lazy functional languages was first presented by Wadler and Hughes (1987) in 1987, and has undergone significant development since then. The method is elegant and theoretically satisfying except in one r...
PERs from Projections for BindingTime Analysis
 Journal of Lisp and Symbolic Computation
, 1994
"... Firstorder projectionbased bindingtime analysis has proven genuinely useful in partial evaluation [Lau91a, Lau91c]. There have been three notable generalisations of projectionbased analysis to higher order. The first lacked a formal basis [Mog89]; the second used structures strictly more general ..."
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Firstorder projectionbased bindingtime analysis has proven genuinely useful in partial evaluation [Lau91a, Lau91c]. There have been three notable generalisations of projectionbased analysis to higher order. The first lacked a formal basis [Mog89]; the second used structures strictly more general that projections, namely partial equivalence relations (PERs) [HS91]; the third involved a complex construction that gave rise to impractically large abstract domains [Dav93]. This paper presents a technique free of these shortcomings: it is simple, entirely projectionbased, satisfies a formal correctness condition, and gives rise to reasonably small abstract domains. Though the technique is cast in terms of projections, there is also an interpretation in terms of PERs. The principal limitation of the technique is the restriction to monomorphic typing. 1 Introduction and Background We take as given that bindingtime analysis is essential for good partial evaluation, and we do not address...