We survey basic garbage collection algorithms, and variations such as incremental and generational collection; we then discuss low-level implementation considerations and the relationships between storage management systems, languages, and compilers. Throughout, we attempt to present a uni ed view based on abstract traversal strategies, addressing issues of conservatism, opportunism, and immediacy of reclamation; we also point outavariety of implementation details that are likely to have a significant impact on performance.

Much research has been devoted to studies of and algorithms for memory management based on garbage collection or explicit allocation and deallocation. An alternative approach, region-based memory management, has been known for decades, but has not been wellstudied. In a region-based system each allocation specifies a region, and memory is reclaimed by destroying a region, freeing all the storage allocated therein. We show that on a suite of allocation-intensive C programs, regions are competitive with malloc/free and sometimes substantially faster. We also show that regions support safe memory management with low overhead. Experience with our benchmarks suggests that modifying many existing programs to use regions is not difficult. 1 Introduction The two most popular memory management techniques are explicit allocation and deallocation, as in C's malloc/free, and various forms of garbagecollection [Wil92]. Both have well-known advantages and disadvantages, discussed further below. A t...

Programmers are writing a large and rapidly growing number of programs in object-oriented languages such as Java that require garbage collection (GC). To explore the design and evaluation of GC algorithms quickly, researchers are using simulation based on traces of object allocation and lifetime behavior. The brute force method generates perfect traces using a whole-heap GC at every potential GC point in the program. Because this process is prohibitively expensive, researchers often use granulated traces by collecting only periodically, e.g., every 32K bytes of allocation.

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 describe an efficient server-based algorithm for garbage collecting object-oriented databases in a workstation/server environment. The algorithm is incremental and runs concurrently with client transactions, however, it does not hold any locks on data and does not require callbacks to clients. It is fault tolerant, but performs very little logging. The algorithm has been designed to be integrated into existing OODB systems, and therefore it works with standard implementation techniques such as two-phase locking and write-ahead-logging. In addition, it supports client-server performance optimizations such as client caching and flexible management of client buffers. We describe an implementation of the algorithm in the EXODUS storage manager and present results from an initial performance study of the implementation. These results demonstrate that the introduction of the garbage collector adds minimal overhead to client operations. 1 Introduction A primary strength of Object Oriented...

A reference counting garbage collector cannot reclaim unreachable cyclic structures of objects. Therefore, reference counting collectors either use a backup tracing collector seldom, or employ a cycle collector to reclaim cyclic structures. Recently, the first on-the-fly cycle collector, that may run concurrently with program threads, was presented by Bacon and Rajan [3]. This demonstrated the ability to run on-the-fly reference counting without resorting to an auxiliary tracing collector. In this paper

Abstract: Reference Counting is the memory management technique of most widespread use today. Very often applications handle objects that are either permanent or get tenured. This paper uses this information to make cyclic reference counting more efficient.

We present a garbage collection scheme based on reference counting and region inference which, unlike the standard reference counting algorithm, handles cycles correctly. In our algorithm, the fundamental operations of region inference are performed dynamically. No assistance is required from the programmer or the compiler, making our algorithm particularly well-suited for use in dynamically-typed languages such as scripting languages. We provide a detailed algorithm and demonstrate how it can be implemented efficiently.

This paper presents the main logging techniques issues through a study of four existing logging systems implemented as independent storage systems: Clio, Camelot DLF, K i t L o g and Sprite LFS. It aims to provide a clear view of problems and actual solutions. Key words and phrases: Logging techniques, compaction, log end, quick data lookup. Introduction

A reference counting garbage collector cannot reclaim unreachable cyclic structures of objects. Therefore, reference counting collectors either use a backup tracing collector seldom, or employ a cycle collectors to reclaim cyclic structures. Recently, the first on-the-fly cycle collector, that may run concurrently with program threads, was presented by Bacon and Rajan [3]. This demonstrated the ability to run on-the-fly reference counting without resorting to an auxiliary tracing collector.