Tupling is a well-known transformation tactic to obtain new efficient recursive functions by grouping some recursive functions into a tuple. It may be applied to eliminate multiple traversals over the common data structure. The major difficulty in tupling transformation is to find what functions are to be tupled and how to transform the tupled function into an efficient one. Previous approaches to tupling transformation are essentially based on fold/unfold transformation. Though general, they suffer from the high cost of keeping track of function calls to avoid infinite unfolding, which prevents them from being used in a compiler. To remedy this situation, we propose a new method to expose recursive structures in recursive definitions and show how this structural information can be explored for calculating out efficient programs by means of tupling. Our new tupling calculation algorithm can eliminate most of multiple data traversals and is easy to be implemented. 1 Introduction Tupli...

ion versus efficiency For concreteness, let us first examine a number of examples of the type of optimisation that we wish to capture, and the kind of programs on which they operate. This will give us a specific aim when developing the machinery for automating the process, and a yardstick for evaluating our results. 2.1 Minimum depth of a tree Consider the data type of leaf labelled binary trees: dataBtreea = Leaf a j Bin (Btree a)(Btree a) The minimum depth of such a tree is returned by the function mindepth :: Btree a ! Int : mindepth (Leaf a) = 0 mindepth (Bin s t) = min (mindepth s)(mindepth t) + 1 This program is clear, but rather inefficient. It traverses the whole tree, regardless of leaves that may occur at a small depth. A better program would keep track of the `minimum depth so far', and never explore subtrees beyond that current best solution. One possible implementation of that idea is mindepth t = md t 01 md (Leaf a)d m = mindm md (Bin s t)d m = if d 0 m then m...

This paper presents a system for searching for desired small functional programs by just generating a sequence of type-correct programs in a systematic and exhaustive manner and evaluating them. The main goal of this line of research is to ease functional programming, along with the subgoal to provide an axis to evaluate heuristic approaches to program synthesis such as genetic programming by telling the best performance possible by exhaustive search algorithms. While our previous approach to that goal used combinatory expressions in order to simplify the synthesis process, which led to redundant combinator expressions with complex types, this time we use de Bruijn lambda expressions and enjoy improved results. 1

This paper introduces a new approach to optimising array algorithms in functional languages. We are specifically aiming at an efficient implementation of irregular array algorithms that are hard to implement in conventional array languages such as Fortran. We optimise the storage layout of arrays containing complex data structures and reduce the running time of functions operating on these arrays by meansofequationalprogramtransformations. Inparticular, this paper discusses a novel form of combinator loop fusion, whichbyremovingintermediatestructuresoptimisestheuse of the memory hierarchy. We identify a combinator named loopP that provides a general scheme for iterating over an array and that in conjunction with an array constructor replicateP is sufficient to express a wide range of array algorithms. On this basis, we define equational transformation rules that combine traversals of loopP and replicateP as well as sequences of applications of loopP into a single loopP traversal. Our approach naturally generalises to a parallel implementation and includes facilities for optimising load balancing and communication. A prototype implementation based on the rewrite rule pragma of the Glasgow Haskell Compiler is significantly faster than standard Haskell arrays and approaches the speed of hand coded C for simple examples. 1.

Parallel skeletons intend to encourage programmers to build...

Abstract programming supports the separation of logical concerns from issues of control in program construction. While this separation of concerns leads to reduced code size and increased reusability of code, its main disadvantage is the computational overhead it incurs. Fusion techniques can be used to combine the reusability of abstract programs with the e#ciency of specialized programs.

Fusion laws permit to eliminate various of the intermediate data structures that are created in function compositions. The fusion laws associated with the traditional recursive operators on datatypes cannot in general be used to transform recursive programs with effects. Motivated by this fact, this paper addresses the definition of two recursive operators on datatypes that capture functional programs with effects. Effects are assumed to be modeled by monads. The main goal is thus the derivation of fusion laws for the new operators. One of the new operators is called monadic unfold. It captures programs (with effects) that generate a data structure in a standard way. The other operator is called monadic hylomorphism, and corresponds to programs formed by the composition of a monadic unfold followed by a function defined by structural induction on the data structure that the monadic unfold generates. 1 Introduction A common approach to program design in functional programmin...

The deforestation of a functional program is a transformation which gets rid of intermediate data structures constructions that appear when two functions are composed. The descriptional composition, initially introduced by Ganzinger and Giegerich, is a deforestation method dedicated to the composition of two attribute grammars. This article presents a new functional deforestation technique, called symbolic composition, based on the descriptional composition mechanism, but extending it. An automatic translation from a functional program into an equivalent attribute grammar allows symbolic composition to be applied, and then the result can be translated back into a functional program. This yields a source to source functional program transformation. The resulting deforestation method provides a better deforestation than other existing functional techniques. Symbolic composition, that uses the declarative and descriptional features of attribute grammars is intrinsically more powerful th...

The functional programming community is paying increasing attention to static structure-based transformations. For example, generic control operators, such as fold, have been introduced in functional programming to increase the power and applicability of a particular kind of static transformation, called deforestation, which prevents the construction of useless intermediate data structures in function composition. This is achieved by making the structure of the data more explicit in program specifications. We argue that one of the original concepts of Attribute Grammars is precisely to make data structures explicit in program specifications. Furthermore, there exists a powerful static deforestation-like transformation in their context. In this paper, we present similarities between deforestation methods, on the one hand with the functional approach, and on the other hand with the Attribute Grammars approach. In order to gain a grasp of these similarities, we first make a simple compar...

Abstract programming supports the separation of logical concerns from issues of control in program construction. While this separation of concerns leads to reduced code size and increased reusability of code, its main disadvantage is the computational overhead it incurs. Fusion techniques can be used to combine the reusability of abstract programs with the e#ciency of specialized programs.