We present a technique to prevent the construction of intermediate data structures in functional programs, which is based on results from the theory of tree transducers. We first decompose function definitions, which correspond to macro tree transducers, into smaller pieces. Under certain restrictions...

Integrating semi-naive fixpoint iteration from deductive data bases [3, 2, 4] as well as continuations into worklist-based solvers, we derive a new application independent local fixpoint algorithm for distributive constraint systems. Seemingly different efficient algorithms for abstract interpretation like those for linear constant propagation for imperative languages [17] as well as for control-flow analysis for functional languages [13] turn out to be instances of our scheme. Besides this systematizing contribution we also derive a new efficient algorithm for abstract OLDT-resolution as considered in [15, 16, 25] for Prolog.

The aim of this work is the development and application of a partial evaluation procedure for rewriting-based functional logic programs. Functional logic programming languages unite the two main declarative programming paradigms. The rewriting-based computational model extends traditional functional programming languages by incorporating logical features, including logical variables and built-in search, into its framework. This work is the first to address the automatic specialisation of these functional logic programs. In particular, a theoretical framework for the partial evaluation of rewriting-based functional logic programs is defined and its correctness is established. Then, an algorithm is formalised which incorporates the theoretical framework for the procedure in a fully automatic technique. Constraint solving is used to represent additional information about the terms encountered during the transformation in order to improve the efficiency and size of the residual programs. ...

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...

. Tupling transformation strategy can be used to merge loops together by combining recursive calls and also to eliminate redundant calls for a class of programs. In the latter case, this transformation can produce super-linear speedup. Existing works in deriving a safe and automatic tupling only apply to a very limited class of programs. In this paper, we present a novel parameter analysis, called synchronisation analysis, to solve the termination problem for tupling. With it, we can perform tupling on functions with multiple recursion and accumulative arguments without the risk of non-termination. This significantly widens the scope for tupling, and potentially enhances its usefulness. The analysis is shown to be of polynomial complexity; this makes tupling suitable as a compiler optimisation. 1 Introduction Source-to-source transformation can achieve global optimisation through specialisation for recursive functions. Two well-known techniques are partial evaluation [9] a...

We show how programming pearls can be systematically derived via fusion, followed by tupling transformations. By focusing on the elimination of intermediate data structures (fusion) followed by the elimination of redundant calls (tupling), we systematically realise both space and time efficient algorithms from naive specifications. We illustrate our approach using a well-known maximum segment sum (MSS) problem, and a less-known maximum segment product (MSP) problem. While the two problems share similar specifications, their optimised codes are significantly different. This divergence in the transformed codes do not pose any difficulty. By relying on modular techniques, we are able to systematically reuse both code and transformation in our derivation.

Redundant call elimination has been an important program optimisation process as it can produce super-linear speedup in optimised programs. In this paper, we investigate use of the tupling transformation in achieving this optimisation over a first-order functional language. Standard tupling technique, as described in [6], works excellently in a restricted variant of the language; namely, functions with single recursion argument. We provide a semantic understanding of call redundancy, upon which we construct an analysis for handling the tupling of functions with multiple recursion arguments. The analysis provides a means to ensure termination of the tupling transformation. As the analysis is of polynomial complexity, it makes the tupling suitable as a step in compiler optimisation.

Deforestation was introduced to eliminate intermediate data structures used to connect separate parts of a functional program together.

Recursion in grammar and performance In the last 50 years of cognitive science, linguistic theory has proposed more and more articulated structures, while computer science has shown that simpler, flatter structures are more easily processed. If we are interested in adequate models of human linguistic abilities, models that explain the very rapid and accurate human recognition and production of ordinary fluent speech, it seems we need to come to some appropriate understandingoftherelationshipbetweentheseapparently opposing pressures for more and less structure. Here we show how the apparent conflict disappears when it is considered more carefully. Even when we regard the linguists ’ project as a psychological one, there is no pressure for linguists to abandon their rather deep structures in order to account for our easy production and recognition of fluent speech. The deeper, more recursive structures reflect insights into similarities among linguistic constituents and operations, but a processor can compute exactly these structures without the extra effort that deeper analyses might seem to require. To show how this works, we