Symbolic Domain The objects in our abstract symbolic domain are canonical symbolic expressions. A canonical symbolic expression is a lexicographically ordered sequence of symbolic terms. Each symbolic term is in turn a pair of an integer coefficient and a sequence of pairs of pointers to program variables in the program symbol table and their exponents. The latter sequence is also lexicographically ordered. For example, the abstract value of the symbolic expression 2ij+3jk in an environment that i is bound to (1; (( " i ; 1))), j is bound to (1; (( " j ; 1))), and k is bound to (1; (( " k ; 1))) is ((2; (( " i ; 1); ( " j ; 1))); (3; (( " j ; 1); ( " k ; 1)))). In our framework, environment is the abstract analogous of state concept; an environment is a function from program variables to abstract symbolic values. Each environment e associates a canonical symbolic value e x for each variable x 2 V ; it is said that x is bound to e x. An environment might be represented by...

Program transformation is a means to formally develop efficient programs from lucid specifications. A representative sample of the diverse range of program transformation research is classified into several different approaches based upon the motivations for and styles of constructing such formal developments. Individual techniques for supporting construction of developments are also surveyed, and are related to the various approaches.

A systematic approach is given for deriving incremental programs from non-incremental programs written in a standard functional programming language. We exploit a number of program analysis and transformation techniques and domain-specific knowledge, centered around effective utilization of caching, in order to provide a degree of incrementality not otherwise achievable by a generic incremental evaluator. 1 Introduction Incremental programs take advantage of repeated computations on inputs that differ only slightly from one another, avoiding unnecessary duplication of common computations. Given a program f and a certain input change \Phi, a program f 0 that computes the value of f(x \Phi y) efficiently by making use of the value of f(x) is called an incremental version of f under \Phi. The parameter y can be regarded as a change ffix to the input x. Methods of incremental computation have widespread applications, e.g., loop optimizations in optimizing compilers [1, 24, 9, 10] and ...

This paper describes a general approach for automatic and accurate time-bound analysis. The approach consists of transformations for building time-bound functions in the presence of partially known input structures, symbolic evaluation of the time-bound function based on input parameters, optimizations to make the overall analysis efficient as well as accurate, and measurements of primitive parameters, all at the source-language level. We have implemented this approach and performed a number of experiments for analyzing Scheme programs. The measured worst-case times are closely bounded by the calculated bounds. 1 Introduction Analysis of program running time is important for real-time systems, interactive environments, compiler optimizations, performance evaluation, and many other computer applications. It has been extensively studied in many fields of computer science: algorithms [20, 12, 13, 41], programming languages [38, 21, 30, 33], and systems [35, 28, 32, 31]. It is particularl...

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

Abstract not found

cfl200x IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution ot servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. 0 Automatic Accurate Cost-Bound Analysis for High-Level Languages\Lambda

We investigate the soundness of a specialisation technique due to Scherlis, expression procedures, in the context of a higher-order non-strict functional language. An expression procedure is a generalised procedure construct providing a contextually specialised definition. The addition of expression procedures thereby facilitates the manipulation and specialisation of programs. In the expression procedure approach, programs thus generalised are transformed by means of three key transformation rules: composition, application and abstraction. Arguably, the most notable, yet most overlooked feature of the expression procedure approach to transformation, is that the transformation rules always preserve the meaning of programs. This is in contrast to the unfold-fold transformation rules of Burstall and Darlington. In Scherlis' thesis, this distinguishing property was shown to hold for a strict first-order language. Rules for call-by-name evaluation order were stated but not proved correct....

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 significant redundancy in the overall computation. This paper presents a method and algorithms that eliminate such overlapping aggregate array redundancies and shows 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 values 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. For aggregate array computations that have significant redundancy, incrementalization produces drastic speedup compared to previous optimizations; when there is little redundancy, the benefit might be offset by cache effects and other factors. Previous methods for loop optimizations of arrays do not perform incrementalization, and previous techniques for loop incrementalization do not handle arrays. 1

A formalism is presented for obtaining a normal form to be used in representing programs for compiler testing. Examples are used to motivate the features that must be considered when developing such a formalism. It is particularly suitable for heuristically optimized code and has been successfully used in a system for proving that programs written in a subset of LISP are correctly translated to assembly language. I.