Linear logic has been proposed as one solution to the problem of garbage collection and providing efficient "updatein -place" capabilities within a more functional language. Linear logic conserves accessibility, and hence provides a mechanical metaphor which is more appropriate for a distributed-memory parallel processor in which copying is explicit. However, linear logic's lack of sharing may introduce significant inefficiencies of its own. We show an efficient implementation of linear logic called Linear Lisp that runs within a constant factor of non-linear logic. This Linear Lisp allows RPLACX operations, and manages storage as safely as a non-linear Lisp, but does not need a garbage collector. Since it offers assignments but no sharing, it occupies a twilight zone between functional languages and imperative languages. Our Linear Lisp Machine offers many of the same capabilities as combinator/graph reduction machines, but without their copying and garbage collection problems. Intr...

Higher order functional programs constantly allocate objects dynamically. These objects are typically cons cells, closures, and records and are generally allocated in the heap and reclaimed later by some garbage collection process. This paper describes a compile time analysis, called escape analysis, for determining the lifetime of dynamically created objects in higher order functional programs, and describes optimizations that can be performed, based on the analysis, to improve storage allocation and reclamation of such objects. In particular, our analysis can be applied to programs manipulating lists, in which case optimizations can be performed to allow whole cons cells in spines of lists to be either reclaimed at once or reused without incurring any garbage collection overhead. In a previous paper on escape analysis [10], we had left open the problem of performing escape analysis on lists. Escape analysis simply determines when the argument (or some part of the argument) to a func...

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.

Given a description of the parameters in a program that will be known at partial evaluation time, a binding time analysis must determine which parts of the program are dependent solely on these known parts (and therefore also known at partial evaluation time). In this paper a binding time analysis for the simply typed lambda calculus is presented. The analysis takes the form of an abstract interpretation and uses a novel formalisation of the problem of binding time analysis, based on the use of partial equivalence relations. A simple proof of correctness is achieved by the use of logical relations. 1 Introduction Given a description of the parameters in a program that will be known at partial evaluation time, a binding time analysis must determine which parts of the program are dependent solely on these known parts (and therefore also known at partial evaluation time). A binding time analysis performed prior to the partial evaluation process can have several practical benefits (see [...

A fast and scalable interprocedural escape analysis algorithm is presented. The analysis computes a description of a subset of created objects whose lifetime is bounded by the lifetime of a runtime stack frame. The analysis results can be used for many purposes, including stack allocation of objects, thread synchronization elimination, deadstore removal, code motion, and iterator reduction. A method to use the analysis results for transforming a program to allocate some objects on the runtime stack is also presented. For non-trivial programs, typically 10%-20% of all allocated objects are placed on the runtime stack after the transformation.

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A systematic approach is given for symbolically caching intermediate results useful for deriving incremental programs from non-incremental programs. We exploit a number of program analysis and transformation techniques, centered around effective caching based on its utilization in deriving incremental programs, in order to increase the degree of incrementality not otherwise achievable by using only the return values of programs that are of direct interest. Our method can be applied straightforwardly to provide a systematic approach to program improvement via caching. 1 Introduction Incremental programs take advantage of repeated computations on inputs that differ only slightly from one another, making use of the old output in computing a new output rather than computing from scratch. Methods of incremental computation have widespread application, e.g., optimizing compilers [2, 9, 11], transformational programming [29, 32, 42], interactive editing systems [4, 38], etc. In this paper, ...

We present a method, adapted to polymorphically typed functional languages, to detect and collect more garbage than existing GCs. It can be applied to strict or lazy higher order languages and to several garbage collection schemes. Our GC exploits the information on utility of arguments provided by polymorphic types of functions. It is able to detect garbage that is still referenced from the stack and may collect useless parts of otherwise useful data structures. We show how to partially collect shared data structures and to extend the type system to infer more precise information. We also present how this technique can plug several common forms of space leaks.

Abstract. This paper describes a general and powerful method for dead code analysis and elimination in the presence of recursive data constructions. We represent partially dead recursive data using liveness patterns based on general regular tree grammars extended with the notion of live and dead, and we formulate the analysis as computing liveness patterns at all program points based on program semantics. This analysis yields a most precise liveness pattern for the data at each program point, which is signi cantly more precise than results from previous methods. The analysis algorithm takes cubic time in terms of the size of the program in the worst case but is very e cient in practice, as shown by our prototype implementation. The analysis results are used to identify and eliminate dead code. The general framework for representing and analyzing properties of recursive data structures using general regular tree grammars applies to other analyses as well. 1

This paper describes a general approach for optimized live heap space and live heap space-bound analyses for garbage-collected languages.