Uniprocessor garbage collection techniques

Wilson, Paul R.

doi:10.1007/bfb0017182

Cited by 285 publications

(208 citation statements)

References 49 publications

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“…The most widelyused dynamic approach to handling memory leaks is garbage collection [35,36,37,38], which adds runtime overhead to the program. There is also research on dynamic memoryleak detection [39,40,41,42,43,44,45], which provides the location of an allocation which will be leaked later.…”

Section: Related Workmentioning

confidence: 99%

Safe Memory-Leak Fixing for C Programs

Gao

Xiong

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

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Abstract-Automatic bug fixing has become a promising direction for reducing manual effort in debugging. However, general approaches to automatic bug fixing may face some fundamental difficulties. In this paper, we argue that automatic fixing of specific types of bugs can be a useful complement.This paper reports our first attempt towards automatically fixing memory leaks in C programs. Our approach generates only safe fixes, which are guaranteed not to interrupt normal execution of the program. To design such an approach, we have to deal with several challenging problems such as inter-procedural leaks, global variables, loops, and leaks from multiple allocations. We propose solutions to all the problems and integrate the solutions into a coherent approach.We implemented our inter-procedural memory leak fixing into a tool named LeakF ix and evaluated LeakF ix on 15 programs with 522k lines of code. Our evaluation shows that LeakF ix is able to successfully fix a substantial number of memory leaks, and LeakF ix is scalable for large applications.

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Section: Related Workmentioning

confidence: 99%

Safe Memory-Leak Fixing for C Programs

Gao

Xiong

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

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“…Ramps, peaks and plateaus are three patterns of memory usage observed in variety of programs [21]. These patterns represent: constant allocation, allocation followed by freeing, and equal rate of allocation and freeing, respectively.…”

Section: Heuristic Heap Expansionmentioning

confidence: 99%

Mark without much Sweep Algorithm for Garbage Collection

2014

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In this paper two simple improvements over traditional mark-sweep collector are proposed. The core idea is placing small objects of the same type in buckets. The buckets are organised in such way to eliminate the internal fragmentation, sweeping, and freeing inside them. The measured improvement of garbage collection time over traditional mark-sweep is 19%. Another proposed improvement is more general and is applicable to other garbage collection algorithms as well. It uses heuristics to control the heap growth. The regularities in behaviour of objects of particular types are used to determine whether the collection should be performed or avoided in favour of immediate heap expansion. The heap expansion algorithm reduces garbage collection time over traditional marksweep for 49% while keeping the heap size approximately the same. Key words: Mark-sweep, Garbage collection, Heap sizing, Memory managementAlgoritam za zbrinjavanje memorije s označavanjem i smanjenim oslobadanjem. U ovomčlanku opisana su dva jednostavna poboljšanja algoritma označi-oslobodi. Osnovna ideja jest smještanje malih objekata istog tipa u pretince. Pretinci su organizirani tako da se u njima ne pojavljuje unutarnja fragmentacija, a uklanja se i potreba za oslobadanjem blokova zauzetih nedohvatljivim objektima. Vrijeme provedeno u zbrinjavanju manje je za 19% u odnosu na klasični algoritam označi-oslobodi. Drugo poboljšanje je općenitije i moguće ga je primijeniti i na druge algoritme za zbrinjavanje memorije. U njemu rastom gomile upravlja heuristički algoritam koji koristi pravilnosti u ponašanju objekata različitih tipova. Na temelju njih, algoritam odlučuje hoće li gomila biti zbrinuta ili odmah proširena. Heuristička inačica algoritma smanjuje vrijeme provedeno u zbrinjavanju u odnosu na tradicionalni algoritam označi-oslobodi za 49%, a da pri tome zahtijeva približno istu količinu memorije.

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“…The traditional GC problem (on which there is a large body of literature [12,7]) concerns reclaiming storage for heap-allocated objects (data structures) when they are no longer "reachable" from the computation. The "name" of an object is a heap address, i.e., a pointer, and GC concerns a transitive computation that locates all objects that are reachable starting with names in certain well-known places such as registers and stacks.…”

Section: Related Workmentioning

confidence: 99%

“…We refer to this issue as the garbage collection problem in Stampede. The traditional GC problem [12,7] concerns reclaiming storage for heap-allocated objects (data structures) when they are no longer "reachable" from the computation. On the other hand, Stampede's GC problem deals with determining when timestamped items in channels can be reclaimed.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Stampede Garbage Collection Algorithms

Mandviwala

Harel

Knobe

et al. 2005

Languages and Compilers for Parallel Computing

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Stampede is a parallel programming system to support interactive multimedia applications. The system maintains temporal causality in such streaming real-time applications via channels that buffer timestamped items. A Stampede application is a coarse-grain dataflow pipeline of these timestamped items. Not all timestamps are relevant for an application output due to the differential processing rates of pipeline stages. Therefore, garbage collection (GC) is crucial for Stampede runtime performance. Three GC algorithms are currently available in Stampede. In this paper, we ask the question how far off these algorithms are from an ideal garbage collector, one in which the memory usage is exactly equal to that which is required for buffering only the relevant timestamped items in channels. This oracle, while unimplementable, serves as an empirical lower-bound for memory usage. We then propose optimizations that will help us get closer to this lower-bound. Using an elaborate measurement and post-mortem analysis infrastructure in Stampede, we evaluate the performance potential for these optimizations. A color-based people tracking application is used for the performance evaluation. Our results show that these optimizations reduce the memory usage by over 60% for this application over the best GC algorithm available in Stampede.

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Uniprocessor garbage collection techniques

Cited by 285 publications

References 49 publications

Safe Memory-Leak Fixing for C Programs

Safe Memory-Leak Fixing for C Programs

Mark without much Sweep Algorithm for Garbage Collection

A Comparative Study of Stampede Garbage Collection Algorithms

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