A systematic approach is given for deriving incremental programs that exploit caching. The cache-and-prune method presented in the article consists of three stages: (I) the original program is extended to cache the results of all its intermediate subcomputations as well as the final result, (II) the extended program is incrementalized so that computation on a new input can use all intermediate results on an old input, %using existing techniques, and (III) unused results cached by the extended program and maintained by the incremental program are pruned away, leaving a pruned extended program that caches only useful intermediate results and a pruned incremental program that uses and maintains only the useful results. All three stages utilize static analyses and semantics-preserving transformations. Stages I and III are simple, clean, and fully automatable. The overall method has a kind of optimality with respect to the techniques used in Stage II. The method can be applied straightforwardly to provide a systematic approach to program improvement via caching.

This paper presents a new al gS ithm for operator strengM reduction, called OSR. OSR improves upon an earlier alg orithm due to Allen, Cocke, and Kennedy [Allen et al. 1981]. OSR operates on the static sing e assig4 ent (SSA) form of a procedure [Cytron et al. 1991]. By taking advantag of the properties of SSA form, we have derived an alg--- ithm that is simple to understand, quick to implement, and, in practice, fast to run. Its asymptotic complexity is, in the worst case, the same as the Allen, Cocke, and Kennedy al gS ithm (ACK). OSR achieves optimization results that are equivalent to those obtained with the ACK alg orithm. OSR has been implemented in several research and production compilers

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

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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...

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.

Concurrent Class Machines are a novel state-machine model that directly captures a variety of object-oriented concepts, including classes and inheritance, objects and object creation, methods, method invocation and exceptions, multithreading and abstract collection types. The model can be understood as a precise definition of UML activity diagrams which, at the same time, offers an executable, object-oriented alternative to event-based statecharts. It can also be understood as a visual, combined control and data flow model for multithreaded object-oriented programs. We first introduce a visual notation and tool for Concurrent Class Machines and discuss their benefits in enhancing system design. We then equip this notation with a precise semantics that allows us to define refinement and modular refinement rules. Finally, we summarize our work on generation of optimized code, implementation and experiments, and compare with related work. 1.

Array programming languages such as Fortran 90, High Performance Fortran and ZPL are well-suited to scientic computing because they free the scientist from the responsibility of managing burdensome low-level details that complicate programming in languages like C and Fortran 77. However, these burdensome details are critical to performance, thus necessitating aggressive compilation techniques for their optimization. In this paper, we present a new compiler optimization called Array Subexpression Elimination (ASE) that lets a programmer take advantage of the expressibility aorded by array languages and achieve enviable portability and performance. We design a set of micro-benchmarks that model an important class of computations known as stencils and we report on our implementation of this optimization in the context of this micro-benchmark suite. Our results include a 125% improvement on one of these benchmarks and a 50% average speedup across the suite. Also we show a speedup of 32% improvement on the ZPL port of the NAS MG Parallel Benchmark and a 29% speedup over the handoptimized Fortran version. Further, the compilation time is only negligibly aected.

Traditional optimising compilers for embedded applications usually only optimise for speed or size but never try to search for a trade-o between these two issues. The oceans project proposes a dierent compilation strategy, called iterative compilation, to achieve this. Iterative compilation allows exploring multiple sets of optimising program transformations and a high degree of control between high and low level optimisations. In this thesis I show the impact of iterative compilation and discuss the major key in iterative compilation in the framework I implemented: feedback and trade-o between code size and performance. Keywords: Iterative compilation, Code optimisation, Feedback, Instruction level parallelism, Compilation strategy, Embedded systems. 1 Acknowledgments A master thesis is never the work of a single person but the sweat of one person and the help of many, therefore I would thank the following persons: Francois, my room mate Erven, Andre, Yann, Yevon and everybody ...

An image manipulation system can be thought of as a domain-specific programming language: by composing and manipulating pictures, the user builds up an expression in such a programming language. Each time the picture is displayed, the expression is evaluated. To gain the required efficiency of display, the expression must be optimized and compiled on the fly. This paper introduces a small language that could form the basis of an image manipulation system, and it describes a preliminary implementation. To compile an expression in the language, we inline all function definitions and use algebraic laws of the primitive operations to optimize composites. We apply a form of code motion to recover (as far as possible) the sharing lost in the inlining phase. Finally, we generate intermediate code that is passed to a JIT compiler.