Program specialisation aims at improving the overall performance of programs by performing source to source transformations. A common approach within functional and logic programming, known respectively as partial evaluation and partial deduction, is to exploit partial knowledge about the input. It is achieved through a well-automated application of parts of the Burstall-Darlington unfold/fold transformation framework. The main challenge in developing systems is to design automatic control that ensures correctness, efficiency, and termination. This survey and tutorial presents the main developments in controlling partial deduction over the past 10 years and analyses their respective merits and shortcomings. It ends with an assessment of current achievements and sketches some remaining research challenges.

A systematic approach is given for deriving incremental programs that exploit caching. The cache-and-prune method presented in the article consists of three stages: (I) the original program is extended to cache the results of all its intermediate subcomputations as well as the final result, (II) the extended program is incrementalized so that computation on a new input can use all intermediate results on an old input, %using existing techniques, and (III) unused results cached by the extended program and maintained by the incremental program are pruned away, leaving a pruned extended program that caches only useful intermediate results and a pruned incremental program that uses and maintains only the useful results. All three stages utilize static analyses and semantics-preserving transformations. Stages I and III are simple, clean, and fully automatable. The overall method has a kind of optimality with respect to the techniques used in Stage II. The method can be applied straightforwardly to provide a systematic approach to program improvement via caching.

Dynamic programming is an important algorithm design technique. It is used for solving problems whose solutions involve recursively solving subproblems that share subsubproblems. While a straightforward recursive program solves common subsubproblems repeatedly and often takes exponential time, a dynamic programming algorithm solves every subsubproblem just once, saves the result, reuses it when the subsubproblem is encountered again, and takes polynomial time. This paper describes a systematic method for transforming programs written as straightforward recursions into programs that use dynamic programming. The method extends the original program to cache all possibly computed values, incrementalizes the extended program with respect to an input increment to use and maintain all cached results, prunes out cached results that are not used in the incremental computation, and uses the resulting incremental program to form an optimized new program. Incrementalization statically exploits semantics of both control structures and data structures and maintains as invariants equalities characterizing cached results. The principle underlying incrementalization is general for achieving drastic program speedups. Compared with previous methods that perform memoization or tabulation, the method based on incrementalization is more powerful and systematic. It has been implemented and applied to numerous problems and succeeded on all of them. 1

A systematic approach is given for symbolically caching intermediate results useful for deriving incremental programs from non-incremental programs. We exploit a number of program analysis and transformation techniques, centered around effective caching based on its utilization in deriving incremental programs, in order to increase the degree of incrementality not otherwise achievable by using only the return values of programs that are of direct interest. Our method can be applied straightforwardly to provide a systematic approach to program improvement via caching. 1 Introduction Incremental programs take advantage of repeated computations on inputs that differ only slightly from one another, making use of the old output in computing a new output rather than computing from scratch. Methods of incremental computation have widespread application, e.g., optimizing compilers [2, 9, 11], transformational programming [29, 32, 42], interactive editing systems [4, 38], etc. In this paper, ...

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Tupling is a transformation tactic to obtain new functions, without redundant calls and/or multiple traversals of common inputs. It achieves this feat by allowing each set (tuple) of function calls to be computed recursively from its previous set. In previous works by Chin and Khoo [8, 9], a safe (terminating) fold/unfold transformation algorithm was developed for some classes of functions which are guaranteed to be successfully tupled. However, these classes of functions currently use static-sized tables for eliminating the redundant calls. As shown by Richard Bird in [3], there are also other classes of programs whose redundant calls could only be eliminated by using dynamic-sized tabulation. This paper proposes a new solution to dynamic-sized tabulation by an extension to the tupling tactic. Our extension uses lambda abstractions which can be viewed as either dynamic-sized tables or applications of the higher-order generalisation technique to facilitate tupling. Significant speedups could be obtained after the transformed programs were vectorised, as confirmed by experiment.

ion of a function f adds an extra cache parameter to f . Simplification simplifies the definition of f given the added cache parameter. However, as to how the cache parameter should be used in the simplification to provide incrementality, KIDS provides only the observation that distributive laws can often be applied. The Munich CIP project [BMPP89,Par90] has a strategy for finite differencing that captures similar ideas. It first "defines by a suitable embedding a function f 0 ", and then "derives a recursive version of f 0 using generalized unfold/fold strategy", but it provides no special techniques for discovering incrementality. We believe that both works provide only general strategies with no precise procedure to follow and therefore are less automatable than ours. Chapter 4 Caching intermediate results The value of f 0 (x \Phi y) may often be computed faster by using not only the return value of f 0 (x), as discussed in Chapter 3, but also the values of some subcomputation...

This paper describes a systematic method for optimizing recursive functions using both indexed and recursive data structures. The method is based on two critical ideas: first, determining a minimal input increment operation so as to compute a function on repeatedly incremented input; second, determining appropriate additional values to maintain in appropriate data structures, based on what values are needed in computation on an incremented input and how these values can be established and accessed. Once these two are determined, the method extends the original program to return the additional values, derives an incremental version of the extended program, and forms an optimized program that repeatedly calls the incremental program. The method can derive all dynamic programming algorithms found in standard algorithm textbooks. There are many previous methods for deriving efficient algorithms, but none is as simple, general, and systematic as ours.

If functional programs are to be used for high-performance computing, efficient data representations and operations must be provided. Our contribution is a calculus for the analysis of the lengths of (nested) lists and a transformation into a form which is liberated from the chain of cons-operations and which sometimes permits array implementations even if the length depends on run-time values. A major advantage of functional programs vs. imperative programs is that dependence analysis is much easier, due to the absence of reassignments. One severe disadvantage of functional programs as of yet is that efficient, machine-oriented data structures (like the array) absolutely necessary for high-performance computing play a minor role in many language implementations since they do not harmonize with functional evaluation schemata (like graph reduction), which are at a higher level of abstraction. We propose to construct programs by composition of skeletons, i.e., functi...

Recursive programs may require large numbers of procedure calls and stack operations, and many such recursive programs exhibit exponential time complexity, due to the time spent re-calculating already computed sub-problems. As a result, methods which transform a given recursive program to an iterative one have been intensively studied. We propose here a new framework for transforming programs by removing recursion. The framework includes a unified method of deriving low time-complexity programs by solving recurrences extracted from the program sources. Our prototype system, ������, is an initial implementation of the framework, automatically finding simpler “closed form ” versions of a class of recursive programs. Though in general the solution of recurrences is easier if the functions have only a single recursion parameter, we show a practical technique for solving those with multiple recursion parameters.