Early storage reclamation in a tracing garbage collector

Harris, Timothy L.

doi:10.1145/312009.312035

Cited by 9 publications

(7 citation statements)

References 8 publications

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“…Cbgc also does not suffer from nepotism (nepotism is when a dead object in U falsely keeps an object in C live). Furthermore, Cbgc allows early reclamation in that some objects in C can be reclaimed even before all of C is collected (Harris used a similar approach for early reclamation during full GC [20]). Early reclamation means that when collecting partition q, the memory reclaimed earlier during the collection of q's predecessors can already be reused.…”

Section: Discussionmentioning

confidence: 99%

Connectivity-based garbage collection

2003

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We introduce a new family of connectivity-based garbage collectors (Cbgc) that are based on potential objectconnectivity properties. The key feature of these collectors is that the placement of objects into partitions is determined by performing one of several forms of connectivity analyses on the program. This enables partial garbage collections, as in generational collectors, but without the need for any write barrier.The contributions of this paper are 1) a novel family of garbage collection algorithms based on object connectivity; 2) a detailed description of an instance of this family; and 3) an empirical evaluation of Cbgc using simulations. Simulations help explore a broad range of possibilities for Cbgc, ranging from simplistic ones that determine connectivity based on type information to oracular ones that use run-time information to determine connectivity. Our experiments with the oracular Cbgc configurations give an indication of the potential for Cbgc and also identify weaknesses in the realistic configurations. We found that even the simplistic implementations beat state-of-the-art generational collectors with respect to some metrics (pause times and memory footprint).

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Section: Discussionmentioning

confidence: 99%

Connectivity-based garbage collection

2003

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“…Early reclamation, proposed by Harris, is probably one of the most promising approaches to low worst-case granularity garbage collection [50]. Two important observations were made by Harris.…”

Section: Existing Non-real-time Garbage Collectorsmentioning

confidence: 99%

“…A possible alternative to the algorithm proposed in this thesis is to modify, according to our design criteria, those tracing collectors that can perform incremental reclamation, such as Harris and Hirzel et al's algorithms [50,53]. Such a new algorithm could have both the low GC granularity and the ability to reclaim all the garbage.…”

Section: Limitations and Future Workmentioning

confidence: 99%

“…Although the exact maximum amount of cyclic garbage introduced by each release of each hard real-time user task may be very difficult to obtain, safe and tight estimations should be able to achieve. In the future, it could be tried to combine the class hierarchy analysis proposed by Harris [50], Java generics [47] and automated program verification tools such as the Java PathFinder [51,104], to accurately identify all the classes that could be used to build cyclic structures in each task. Then, an approximation of the maximum amount of cyclic garbage could possibly be deduced on the basis of the above information.…”

Section: Limitations and Future Workmentioning

confidence: 99%

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Garbage Collection for Flexible Hard Real-Time Systems

Chang

Wellings

2010

IEEE Trans. Comput.

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Garbage collection is key to improving the productivity and code quality in virtually every computing system with dynamic memory requirements. It dramatically reduces the software complexity and consequently allows the programmers to concentrate on the real problems rather than struggling with memory management issues, which finally results in higher productivity. The code quality is also improved because the two most common memory related errors -dangling pointers and memory leaks -can be prevented (assuming that the garbage collectors are implemented correctly and the user programs are not malicious).However, real-time systems, particularly those with hard real-time requirements, always choose not to use garbage collection (or even dynamic memory) in order to avoid the unpredictable executions of garbage collectors as well as the chances of being blocked due to the lack of free memory. Much effort has been expended trying to build predictable garbage collectors which can provide both temporal and spatial guarantees. Unfortunately, most existing work leads to systems that cannot easily achieve a balance between temporal and spatial performance, although their worst-case behaviours in both dimensions can be predicted. Moreover, the real-time systems targeted by the existing real-time garbage collectors are not the state-ofthe-art ones. The scheduling of those garbage collectors has not been integrated into the modern real-time scheduling frameworks, which makes the benefits provided by those systems very difficult to obtain. This thesis argues that the aforementioned issues should be tackled by introducing new design criteria for real-time garbage collectors. The existing criteria are not enough to reflect the unique requirements of real-time systems. A new performance indicator is proposed to describe the capability of a real-time garbage collector to achieve better balance between temporal and spatial performance. Moreover, it is argued that new real-time garbage collectors should be integrated with the real-time task model and more advanced scheduling techniques should be adopted as well.A hybrid garbage collection algorithm, which combines both reference counting and mark-sweep, is designed according to the new design criteria. On the one hand, the reference counting component helps reduce the overall memory requirements. On the other hand, the mark-sweep component periodically identifies cyclic garbage, which cannot be found by the reference counting component. Both parts of the collector are executed by segregated tasks, which are released periodically as the hard real-time user tasks. In order to improve the performance of soft-or non-realtime tasks in our system while still guaranteeing the hard real-time requirements, the dual-priority scheduling algorithm is used for all the tasks including the GC tasks. A multi-heap approach is also proposed to bound the impacts of the softor non-real-time tasks on the overall memory usage as well as the executions of the GC tasks. More importantly, static ana...

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“…The body of literature on pointer analyses is vast [Hind 2001]. At one extreme, exemplified by Steensgaard [1996] and analyses with type-based heap abstraction [Harris 1999;Tip and Palsberg 2000;Diwan et al 2001], the analyses are fast, but imprecise. At the other extreme, exemplified by shape analyses [Hendren 1990;Sagiv et al 1999], the analyses are slow, but precise enough to discover the shapes of many data structures.…”

Section: Offline Analysismentioning

confidence: 99%

Fast online pointer analysis

Hirzel

Dincklage

Diwan

et al. 2007

ACM Trans. Program. Lang. Syst.

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Pointer analysis benefits many useful clients, such as compiler optimizations and bug finding tools. Unfortunately, common programming language features, such as dynamic loading, reflection, and foreign language interfaces, make pointer analysis difficult. This paper describes how to deal with these features by performing pointer analysis online, during program execution. For example, dynamic loading may load code that is not available for analysis before the program starts.Only an online analysis can analyze such code, and thus support clients that optimize it or find bugs in it. This paper identifies all problems in performing Andersen's pointer analysis for the full Java language, presents solutions to these problems, and uses a full implementation of the solutions in a Java virtual machine for validation and performance evaluation. Our analysis is fast: on average over our benchmark suite, if the analysis recomputes points-to results upon each program change, most analysis pauses take under 0.1 seconds, and add up to 64.5 seconds.

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Early storage reclamation in a tracing garbage collector

Cited by 9 publications

References 8 publications

Connectivity-based garbage collection

Connectivity-based garbage collection

Garbage Collection for Flexible Hard Real-Time Systems

Fast online pointer analysis

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