Recursive Program Optimization Through Inductive Synthesis Proof Transformation

The research described in this paper involved developing transformation techniques which increase the efficiency of the noriginal program, the source, by transforming its synthesis proof into one, the target, which yields a computationally more efficient algorithm. We describe a working proof transformation system which, by exploiting the duality between mathematical induction and recursion, employs the novel strategy of optimizing recursive programs by transforming inductive proofs. We compare and contrast this approach with the more traditional approaches to program transformation, and highlight the benefits of proof transformation with regards to search, correctness, automatability and generality

Transformations of Logic Programs with Goals as Arguments

We consider a simple extension of logic programming where variables may range over goals and goals may be arguments of predicates. In this language we can write logic programs which use goals as data. We give practical evidence that, by exploiting this capability when transforming programs, we can improve program efficiency. We propose a set of program transformation rules which extend the familiar unfolding and folding rules and allow us to manipulate clauses with goals which occur as arguments of predicates. In order to prove the correctness of these transformation rules, we formally define the operational semantics of our extended logic programming language. This semantics is a simple variant of LD-resolution. When suitable conditions are satisfied this semantics agrees with LD-resolution and, thus, the programs written in our extended language can be run by ordinary Prolog systems. Our transformation rules are shown to preserve the operational semantics and termination.Comment: 51 pages. Full version of a paper that will appear in Theory and Practice of Logic Programming, Cambridge University Press, U

A survey of program transformation with special reference to unfold/fold style program development

This paper consists of a survey of current, and past, work on *program transformation* for the purpose of optimization. We first discuss some of the general methodological frameworks for program modification, such as *analogy*, *explanation based learning*, *partial evaluation*, *proof theoretic optimization*, and the *unfold/fold* technique. These frameworks are not mutually exclusive, and the latter, unfold/fold, is certainly the most widely used technique, in various guises, for program transformation. Thus we shall often have occasion to: compare the relative merits of systems that employ the technique in some form, *and*; compare the unfold/fold systems with those that employ alternative techniques. We also include (and compare with unfold/fold) a brief survey of recent work concerning the use of *formal methods* for program transformation

The HERMIT in the Tree

This paper describes our experience using the HERMIT tool- kit to apply well-known transformations to the internal core language of the Glasgow Haskell Compiler. HERMIT provides several mechanisms to support writing general-purpose transformations: a domain-specific language for strategic programming specialized to GHC's core language, a library of primitive rewrites, and a shell-style{based scripting language for interactive and batch usage. There are many program transformation techniques that have been described in the literature but have not been mechanized and made available inside GHC - either because they are too specialized to include in a general-purpose compiler, or because the developers' interest is in theory rather than implementation. The mechanization process can often reveal pragmatic obstacles that are glossed over in pen-and-paper proofs; understanding and removing these obstacles is our concern. Using HERMIT, we implement eleven examples of three program transformations, report on our experience, and describe improvements made in the process

Program Improvement by Automatic Redistribution of Intermediate Results

This paper was originally a Ph.D. thesis proposal.The problem of automatically improving the performance of computer programs has many facets. A common source of program inefficiency is the use of abstraction techniques in program design: general tools used in a specific context often do unnecessary or redundant work. Examples include needless copy operations, redundant subexpressions, multiple traversals of the same datastructure and maintenance of overly complex data invariants. I propose to focus on one broadly applicable way of improving a program's performance: redistributing intermediate results so that computation can be avoided. I hope to demonstrate that this is a basic principle of optimization from which many of the current approaches to optimization may be derived. I propose to implement a system that automatically finds and exploits opportunities for redistribution in a given program. In addition to the program source, the system will accept an explanation of correctness and purpose of the code. Beyond the specific task of program improvement, I anticipate that the research will contribute to our understanding of the design and explanatory structure of programs. Major results will include (1) definition and manipulation of representation of correctness and purpose of a program's implementation, and (2) definition, construction, and use of a representation of a program's dynamic behavior.MIT Artificial Intelligence Laborator

Transformation of logic programs: Foundations and techniques

AbstractWe present an overview of some techniques which have been proposed for the transformation of logic programs. We consider the so-called “rules + strategies” approach, and we address the following two issues: the correctness of some basic transformation rules w.r.t. a given semantics and the use of strategies for guiding the application of the rules and improving efficiency. We will also show through some examples the use and the power of the transformational approach, and we will briefly illustrate its relationship to other methodologies for program development