This paper presents program analyses and transformations that discover a general class of auxiliary information for any incremental computation problem. Combining these techniques with previous techniques for caching intermediate results, we obtain a systematic approach that transforms nonincremental programs into efficient incremental programs that use and maintain useful auxiliary information as well as useful intermediate results. The use of auxiliary information allows us to achieve a greater degree of incrementality than otherwise possible. Applications of the approach include strength reduction in optimizing compilers and finite differencing in transformational programming.

This paper presents a principled approach for optimizing iterative (or recursive) programs. The approach formulates a loop body as a function f and a change operation \Phi, incrementalizes f with respect to \Phi, and adopts an incrementalized loop body to form a new loop that is more efficient. Three general optimizations are performed as part of the adoption; they systematically handle initializations, termination conditions, and final return values on exits of loops. These optimizations are either omitted, or done in implicit, limited, or ad hoc ways in previous methods. The new approach generalizes classical loop optimization techniques, notably strength reduction, in optimizing compilers, and it unifies and systematizes various optimization strategies in transformational programming. Such principled strength reduction performs drastic program efficiency improvement via incrementalization and appreciably reduces code size via associated optimizations. We give examples where this app...

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 presents program analyses and transformations for strengthening invariants for the purpose of efficient computation. Finding the stronger invariants corresponds to discovering a general class of auxiliary information for any incremental computation problem. Combining the techniques with previous techniques for caching intermediate results, we obtain a systematic approach that transforms non-incremental programs into ecient incremental programs that use and maintain useful auxiliary information as well as useful intermediate results. The use of auxiliary information allows us to achieve a greater degree of incrementality than otherwise possible. Applications of the approach include strength reduction in optimizing compilers and finite differencing in transformational programming.

Given a program f and an input change \Phi, we wish to obtain an incremental program that computes f(x \Phi y) efficiently by making use of the value of f(x), the intermediate results computed in computing f(x), and auxiliary information about x that can be inexpensively maintained. Obtaining such incremental programs is an essential part of the transformational-programming approach to software development and enhancement. This paper presents a systematic approach that discovers a general class of useful auxiliary information, combines it with useful intermediate results, and obtains an efficient incremental program that uses and maintains these intermediate results and auxiliary information. We give a number of examples from list processing, VLSI circuit design, image processing, etc. 1 Introduction Software engineering is the systematic approach to the development, operation, maintenance, and retirement of software [1]. The transformational-programming approach to software engine...