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Garbage collection is the automatic reclamation of computer storage [Knu73, Coh81, Wil92, Wil95]. While in many systems, programmers must explicitly reclaim heap memory at some point in their program by using a "free" or "dispose" statement, garbage collected systems free the programmer from this burden. In spite of its obvious attractiveness for many applications, garbage collection for real-time programs is not popular. This is largely due to the perceived cost and disruptiveness of garbage collection in general, and of incremental garbage collection in particular. Most existing "real-time" garbage collectors are not in fact usefully real-time, largely due to the use of a read barrier to trigger incremental copying of data structures being traversed by the running application. This may slow down running applications unpredictably, even though individual increments of garbage collection work are small and bounded. We have developed a hard real-time garbage collector which us...

this paper is much higher than the original one for cyclic reference counting with local mark-scan. More shared cells will now be claimed directly, without any need for mark-scan. The deletion of the last pointer to a shared cell will recycle it immediately, regardless of whether there is a reference to it on the queue. The queue will be left basically with pointers to cycles and pointers to green cells in the free-list or recycled. In this case again, our algorithm performs far better than the original one. In the best case, only one local mark-scan will be performed per cycle, instead of as many as the number of external references to a cycle, as before. Acknowledgements

The article introduces a novel notion of lazy rewriting. By annotating argument positions as lazy, redundant rewrite steps are avoided, and the termination behaviour of a term rewriting system can be improved. Some transformations of rewrite rules enable an implementation using the same primitives as an implementation of eager rewriting. 1

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A stop-and-copy garbage collector updates one-bit reference counting with essentially no extra space and minimal memory cycles beyond the conventional collection algorithm. Any object that is uniquely referenced during a collection becomes a candidate for cheap recovery before the next one, or faster recopying then if it remains uniquely referenced. Since most objects stay uniquely referenced, subsequent collections run faster even if none are recycled between garbage collections. This algorithm extends to generation scavenging, it admits uncounted references from roots, and it corrects conservatively stuck counters, that result from earlier uncertainty whether references were unique. CR categories and Subject Descriptors: D.4.2 [Storage Management]: Allocation/Deallocation strategies; E.2 [Data Storage Representations]: Linked representations. General Term: Algorithms. Additional Key Words and Phrases: multiple reference bit, MRB. Research reported herein was sponsored, in part...

Weighted Reference Counting is a low communication distributed storage reclamation scheme for loosely-couple multiprocessors. The algorithm we present herein extends weighted reference counting to allow the collection of cyclic data structures. To do so, the algorithm identifies candidate objects that may be part of cycles and performs a tricolour mark-scan on their subgraph in a lazy manner to discover whether the subgraph is still in use. The algorithm is concurrent in the sense that multiple useful computation processes and garbage collection processes can be performed simultaneously. Keywords: Memory management, Distributed memory, Reference counting, Garbage collection. Introduction Computation on distributed systems involving several processors is already a reality. In a distributed multiprocessor system each processor is responsible for allocating and reclaiming structures residing in its local memory; interprocessor communication is far less efficient than local memory access...

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.

Existing techniques for garbage collection and machine code optimizations can interfere with each other. The inability to fully optimize code in a garbage-collected system is a hidden cost of garbage collection. One solution to this problem is proposed; an inexpensive protocol that permits most optimizations and garbage collection to coexist. A second approach to this problem and a separate problem in its own right is to reduce the need for garbage collection. This requires analysis of storage lifetime. Inferring storage lifetime is di#cult in a language with nested and recursive data structures, but it is precisely these languages in which garbage collection is most useful. An improved analysis for "storage containment" is described. Containment information can be represented in a directed graph. The derivation of this graph falls into a monotone data-flow analysis framework; in addition, the derivation has the Church-Rosser property. The graphs produced in the analysis of a value-a...

In recent years a range of single programming language systems have been developed that are supported by a persistent store. Examples of such systems include Argus, Galileo, PS-algol and Smalltalk. Although each of these systems is based on a subtly different concept of persistence a common approach is to utilise a layered architecture. This paper presents the design of one such layered architecture that can be used to support a persistent object store where the protection is enforced by a high level type system. The architecture has been used to construct the persistent programming system for Napier88 and is powerful enough to support languages with similar type systems. 2 1 Introduction In recent years a range of single programming language systems have been developed that are supported by a persistent store[atk82,atk83b,ros83,bro85,that86,bro89]. Examples of such systems include Argus[lis84], Galileo[alb85a], PS-algol[psa88] and Smalltalk[gol83]. Although each of these s...