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66
Mechanical Translation of Set Theoretic Problem Specifications Into Efficient RAM Code  A Case Study
 Proc. EUROCAL 85
, 1985
"... This paper illustrates a fully automatic topdown approach to program development in which formal problem specifications are mechanically translated into efficient RAM code. This code is guaranteed to be totally correct and an upper bound on its worst case asymptotic running time is automatically de ..."
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Cited by 26 (8 self)
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This paper illustrates a fully automatic topdown approach to program development in which formal problem specifications are mechanically translated into efficient RAM code. This code is guaranteed to be totally correct and an upper bound on its worst case asymptotic running time is automatically determined. The user is only required to supply the system with a formal problem specification, and is relieved of all responsibilities in the rest of the program development process. These results are obtained, in part, by greatly restricting the system to handle a class of determinate, set theoretic, tractable problems. The most essential transformational techniques that are used are fixed point iteration, finite differencing, and data structure selection. Rudimentary forms of these techniques have been implemented and used effectively in the RAPTS transformational programming system. This paper explains the conceptual underpinnings of our approach by considering the problem of attribute closure for relational databases and systematically deriving a program that implements a linear time solution. 1.
Concatenate, Reverse and Map Vanish For Free
, 2002
"... We introduce a new transformation method to eliminate intermediate data structures occurring in functional programs due to repeated list concatenations and other data manipulations (additionally exemplified with list reversal and mapping of functions over lists). The general idea is to uniformly abs ..."
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Cited by 25 (9 self)
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We introduce a new transformation method to eliminate intermediate data structures occurring in functional programs due to repeated list concatenations and other data manipulations (additionally exemplified with list reversal and mapping of functions over lists). The general idea is to uniformly abstract from data constructors and manipulating operations by means of rank2 polymorphic combinators that exploit algebraic properties of these operations to provide an optimized implementation. The correctness of transformations is proved by using the free theorems derivable from parametric polymorphic types.
A Functional Database
, 1989
"... A Functional Database Phil Trinder D.Phil. Thesis Wolfson College Michaelmas Term, 1989 This thesis explores the use of functional languages to implement, manipulate and query databases. Implementing databases. A functional language is used to construct a database manager that allows efficient and c ..."
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Cited by 23 (3 self)
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A Functional Database Phil Trinder D.Phil. Thesis Wolfson College Michaelmas Term, 1989 This thesis explores the use of functional languages to implement, manipulate and query databases. Implementing databases. A functional language is used to construct a database manager that allows efficient and concurrent access to shared data. In contrast to the locking mechanism found in conventional databases, the functional database uses data dependency to provide exclusion. Results obtained from a prototype database demonstrate that data dependency permits an unusual degree of concurrency between operations on the data. The prototype database is used to exhibit some problems that seriously restrict concurrency and also to demonstrate the resolution of these problems using a new primitive. The design of a more realistic database is outlined. Some restrictions on the data structures that can be used in a functional database are also uncovered. Manipulating databases. Functions over the database a...
Discovering auxiliary information for incremental computation
 In Symp. on Princ. of Prog. Lang
, 1996
"... This paper presents program analyses and transformations that discover a general class of auxiliary information for any incremental computation problem. Combining these techniques with previous techniques for caching intermediate results, we obtain a systematic approach that transforms nonincrementa ..."
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Cited by 22 (12 self)
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This paper presents program analyses and transformations that discover a general class of auxiliary information for any incremental computation problem. Combining these techniques with previous techniques for caching intermediate results, we obtain a systematic approach that transforms nonincremental programs into e cient 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 i nclude strength reduction in optimizing compilers and nite di erencing in transformational programming. 1
Incrementalization across object abstraction
 In OOPSLA ’05: Proceedings of the 20th annual ACM SIGPLAN conference on Object oriented programming, systems, languages, and applications
, 2005
"... Object abstraction supports the separation of what operations are provided by systems and components from how the operations are implemented, and is essential in enabling the construction of complex systems from components. Unfortunately, clear and modular implementations have poor performance when ..."
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Cited by 21 (12 self)
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Object abstraction supports the separation of what operations are provided by systems and components from how the operations are implemented, and is essential in enabling the construction of complex systems from components. Unfortunately, clear and modular implementations have poor performance when expensive query operations are repeated, while efficient implementations that incrementally maintain these query results are much more difficult to develop and to understand, because the code blows up significantly, and is no longer clear or modular. This paper describes a powerful and systematic method that first allows the “what ” of each component to be specified in a clear and modular fashion and implemented straightforwardly in an objectoriented language; then analyzes the queries and updates, across object abstraction, in the straightforward implementation; and finally derives the sophisticated and efficient “how ” of each component by incrementally maintaining the results of repeated expensive queries with respect to updates to their parameters. Our implementation and experimental results for example applications in query optimization, rolebased access control, etc. demonstrate the effectiveness and benefit of the method.
A Comparative Revisitation of Some Program Transformation Techniques
 Partial Evaluation, Int'l Seminar, Dagstuhl
, 1996
"... . We revisit the main techniques of program transformation which are used in partial evaluation, mixed computation, supercompilation, generalized partial computation, rulebased program derivation, program specialization, compiling control, and the like. We present a methodology which underlines the ..."
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Cited by 21 (0 self)
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. We revisit the main techniques of program transformation which are used in partial evaluation, mixed computation, supercompilation, generalized partial computation, rulebased program derivation, program specialization, compiling control, and the like. We present a methodology which underlines these techniques as a `common pattern of reasoning' and explains the various correspondences which can be established among them. This methodology consists of three steps: i) symbolic computation, ii) search for regularities, and iii) program extraction. We also discuss some control issues which occur when performing these steps. 1 Introduction During the past years researchers working in various areas of program transformation, such as partial evaluation, mixed computation, supercompilation, generalized partial computation, rulebased program derivation, program specialization, and compiling control, have been using very similar techniques for the development and derivation of programs. Unfor...
Generic Downwards Accumulations
 Science of Computer Programming
, 2000
"... . A downwards accumulation is a higherorder operation that distributes information downwards through a data structure, from the root towards the leaves. The concept was originally introduced in an ad hoc way for just a couple of kinds of tree. We generalize the concept to an arbitrary regular d ..."
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Cited by 19 (3 self)
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. A downwards accumulation is a higherorder operation that distributes information downwards through a data structure, from the root towards the leaves. The concept was originally introduced in an ad hoc way for just a couple of kinds of tree. We generalize the concept to an arbitrary regular datatype; the resulting denition is coinductive. 1 Introduction The notion of scans or accumulations on lists is well known, and has proved very fruitful for expressing and calculating with programs involving lists [4]. Gibbons [7, 8] generalizes the notion of accumulation to various kinds of tree; that generalization too has proved fruitful, underlying the derivations of a number of tree algorithms, such as the parallel prex algorithm for prex sums [15, 8], Reingold and Tilford's algorithm for drawing trees tidily [21, 9], and algorithms for query evaluation in structured text [16, 23]. There are two varieties of accumulation on lists: leftwards and rightwards. Leftwards accumulation ...
Program Calculation Properties of Continuous Algebras
, 1991
"... Defining data types as initial algebras, or dually as final coalgebras, is beneficial, if not indispensible, for an algebraic calculus for program construction, in view of the nice equational properties that then become available. It is not hard to render finite lists as an initial algebra and, ..."
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Cited by 19 (0 self)
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Defining data types as initial algebras, or dually as final coalgebras, is beneficial, if not indispensible, for an algebraic calculus for program construction, in view of the nice equational properties that then become available. It is not hard to render finite lists as an initial algebra and, dually, infinite lists as a final coalgebra. However, this would mean that there are two distinct data types for lists, and then a program that is applicable to both finite and infinite lists is not possible, and arbitrary recursive definitions are not allowed. We prove the existence of algebras that are both initial in one category of algebras and final in the closely related category of coalgebras, and for which arbitrary (continuous) fixed point definitions ("recursion") do have a solution. Thus there is a single data type that comprises both the finite and the infinite lists. The price to be paid, however, is that partiality (of functions and values) is unavoidable.
Calculating Accumulations
, 1999
"... this paper, we shall formulate accumulations as higher order catamorphisms , and propose several general transformation rules for calculating accumulations (i.e., finding and manipulating accumulations) by calculationbased (rather than a searchbased) program transformation methods. Some examples ..."
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Cited by 16 (6 self)
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this paper, we shall formulate accumulations as higher order catamorphisms , and propose several general transformation rules for calculating accumulations (i.e., finding and manipulating accumulations) by calculationbased (rather than a searchbased) program transformation methods. Some examples are given for illustration.
Loop optimization for aggregate array computations
"... An aggregate array computation is a loop that computes accumulated quantities over array elements. Such computations are common in programs that use arrays, and the array elements involved in such computations often overlap, especially across iterations of loops, resulting in signi cant redundancy ..."
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Cited by 15 (7 self)
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An aggregate array computation is a loop that computes accumulated quantities over array elements. Such computations are common in programs that use arrays, and the array elements involved in such computations often overlap, especially across iterations of loops, resulting in signi cant redundancy in the overall computation. This paper presents a method and algorithms that eliminate such overlapping aggregate array redundancies and shows both analytical and experimental performance improvements. The method is based on incrementalization, i.e., updating the values of aggregate array computations from iteration to iteration rather than computing them from scratch in each iteration. This involves maintaining additional information not maintained in the original program. We reduce various analysis problems to solving inequality constraints on loop variables and array subscripts, and we apply results from work on array data dependence analysis. Incrementalizing aggregate array computations produces drastic program speedup compared to previous optimizations. Previous methods for loop optimizations of arrays do not perform incrementalization, and previous techniques for loop incrementalization do not handle arrays.