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Parallel Programming using Functional Languages
, 1991
"... I am greatly indebted to Simon Peyton Jones, my supervisor, for his encouragement and technical assistance. His overwhelming enthusiasm was of great support to me. I particularly want to thank Simon and Geoff Burn for commenting on earlier drafts of this thesis. Through his excellent lecturing Cohn ..."
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Cited by 54 (3 self)
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I am greatly indebted to Simon Peyton Jones, my supervisor, for his encouragement and technical assistance. His overwhelming enthusiasm was of great support to me. I particularly want to thank Simon and Geoff Burn for commenting on earlier drafts of this thesis. Through his excellent lecturing Cohn Runciman initiated my interest in functional programming. I am grateful to Phil Trinder for his simulator, on which mine is based, and Will Partain for his help with LaTex and graphs. I would like to thank the Science and Engineering Research Council of Great Britain for their financial support. Finally, I would like to thank Michelle, whose culinary skills supported me whilst I was writingup.The Imagination the only nation worth defending a nation without alienation a nation whose flag is invisible and whose borders are forever beyond the horizon a nation whose motto is why have one or the other when you can have one the other and both
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...
A Relational Approach to Strictness Analysis for HigherOrder Polymorphic Functions
 In Proc. ACM Symposium on Principles of Programming Languages
, 1991
"... This paper defines the categorical notions of relators and transformations and shows that these concepts enable us to give a semantics for polymorphic, higher order functional programs. We demonstrate the pertinence of this semantics to the analysis of polymorphic programs by proving that strictness ..."
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This paper defines the categorical notions of relators and transformations and shows that these concepts enable us to give a semantics for polymorphic, higher order functional programs. We demonstrate the pertinence of this semantics to the analysis of polymorphic programs by proving that strictness analysis is a polymorphic invariant. 1 Introduction Recently, there has been some effort to construe the semantics of polymorphic functional programming languages using the categorical notion of a natural transformation. The idea can be sketched as follows: we have a "universe of computational discourse" given by some category (in practice, a suitable category of domains). Types are objects of . Type constructions (e.g. product, function space) are functors (of appropriate arity) over . Monomorphic functional programs are morphisms of ; polymorphic programs are natural transformations. E.g. append : 8t: t ? \Theta t ? ! t ? append : (\Delta) ? \Theta (\Delta) ? : ! (\Delta) ? w...
Strictness logic and polymorphic invariance
 In Proc. Logical Found. Comp. Sci
, 1992
"... We describe a logic for reasoning about higherorder strictness properties of typed lambda terms. The logic arises from axiomatising the inclusion order on certain closed subsets of domains. The axiomatisation of the lattice of strictness properties is shown to be sound and complete, and we then giv ..."
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We describe a logic for reasoning about higherorder strictness properties of typed lambda terms. The logic arises from axiomatising the inclusion order on certain closed subsets of domains. The axiomatisation of the lattice of strictness properties is shown to be sound and complete, and we then give a program logic for assigning properties to terms. This places work on strictness analysis via type inference on a firm theoretical foundation. We then use proof theoretic techniques to show how the derivable strictness properties of different instances of polymorphically typed terms are related. 1
Projections for Polymorphic FirstOrder Strictness Analysis
 Math. Struct. in Comp. Science
, 1991
"... this paper, that results from this kind of analysis are, in a sense, polymorphic. This confirms an earlier conjecture [19], and shows how the technique can be applied to firstorder polymorphic functions. The paper is organised as follows. In the next section, we review projectionbased strictness a ..."
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Cited by 6 (1 self)
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this paper, that results from this kind of analysis are, in a sense, polymorphic. This confirms an earlier conjecture [19], and shows how the technique can be applied to firstorder polymorphic functions. The paper is organised as follows. In the next section, we review projectionbased strictness analysis very briefly. In Section 3 we introduce the types we will be working with: they are the objects of a category. We show that parameterised types are functors, with certain cancellation properties. In Section 4 we define strong and weak polymorphism: polymorphic functions in programming languages are strongly polymorphic, but we will need to use projections with a slightly weaker property. We prove that, under certain conditions, weakly polymorphic functions are characterised by any nontrivial instance. We can therefore analyse one monomorphic instance of a polymorphic function using existing techniques, and apply the results to every instance. In Section 5 we choose a finite set of projections for each type, suitable for use in a practical compiler. We call these specially chosen projections contexts, and we show examples of factorising contexts for compound types in order to facilitate application of the results of Section 4. We give a number of examples of polymorphic strictness analysis. Finally, in Section 6 we discuss related work and draw some conclusions. 2. Projections for Strictness Analysis
Partitioning Nonstrict Languages for Multithreaded Code Generation
 Master's thesis, Dept. of EECS, MIT
, 1994
"... In a nonstrict language, functions may return values before their arguments are available, and data structures may be defined before all their components are defined. Compiling such languages to conventional hardware is not straightforward; instructions do not have a fixed compile time ordering. Su ..."
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In a nonstrict language, functions may return values before their arguments are available, and data structures may be defined before all their components are defined. Compiling such languages to conventional hardware is not straightforward; instructions do not have a fixed compile time ordering. Such an ordering is necessary to execute programs efficiently on current microprocessors. Partitioning is the process of compiling a nonstrict program into threads (i.e., a sequence of instructions). This process involves detecting data dependencies at compile time and using these dependencies to "sequentialize" parts of the program. Previous work on partitioning did not propagate dependence information across recursive procedure boundaries. Using a representation known as Paths we are able to represent dependence information of recursive functions. Also, we incorporate them into a known partitioning algorithm. However, this algorithm fails to make use of all the information contained in pat...
Semantic Analyses for Storage Management Optimizations in Functional Language Implementations
, 1991
"... One of the major overheads in implementing functional languages is the storage management overhead due to dynamic allocation and automatic reclamation of indefiniteextent storage. This dissertation investigates the problems of statically inferring lifetime information about dynamicallyallocated ob ..."
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One of the major overheads in implementing functional languages is the storage management overhead due to dynamic allocation and automatic reclamation of indefiniteextent storage. This dissertation investigates the problems of statically inferring lifetime information about dynamicallyallocated objects in higherorder polymorphic functional languages, both strict and nonstrict, and of applying that information to reduce the storage management overhead. We have developed a set of compiletime semantic analyses for a higherorder, monomorphic, strict functional language based on denotational semantics and abstract interpretation. They are 1) escape analysis, which provides information about the relative lifetimes of objects such as arguments and local objects defin...
Strictness Analysis in 4D
 In Glasgow Workshop on Functional Programming
, 1990
"... Strictness analysis techniques can be classified along four different dimensions: firstorder vs. higherorder, flat vs. nonflat, low fidelity vs. high fidelity, and forward vs. backward. Plotting a table of the positions of known techniques within this space reveals that certain regions are densely ..."
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Strictness analysis techniques can be classified along four different dimensions: firstorder vs. higherorder, flat vs. nonflat, low fidelity vs. high fidelity, and forward vs. backward. Plotting a table of the positions of known techniques within this space reveals that certain regions are densely occupied while others are empty. In particular, techniques for highfidelity forward and lowfidelity backward analysis are well known, while those for lowfidelity forward and highfidelity backward analysis are lacking. This paper fills in the gaps: the lowfidelity forward methods provide faster analyses than the highfidelity forward methods, at the cost of accuracy, while the highfidelity backward methods provide more information than the lowfidelity backward methods, at the cost of time. 1 Introduction Strictness analysis is an important part of many compilers for lazy functional languages, and a wide variety of strictness analysis techniques have been proposed. It is not clear how...