On the Usefulness of Liveness for Garbage Collection and Leak Detection

Hirzel, Martin; Diwan, Amer; Hosking, Antony L.

doi:10.1007/3-540-45337-7_11

Cited by 8 publications

(7 citation statements)

References 24 publications

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“…These results generally coincide with previous studies of both C and Java programs. Hirzel et al compare liveness to reachability on a benchmark suite including seven C applications [37]. For these, they find that when using an aggressive, interprocedural liveness analysis, they find a significant gap for two of their benchmarks, reducing average object lifetime by 11% for gzip (in allocation time) and 21% for yacr2); for the others, the gap remains below 2%.…”

Section: Reachability Versus Livenessmentioning

confidence: 99%

Quantifying the performance of garbage collection vs. explicit memory management

Hertz

Berger

2005

SIGPLAN Not.

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Garbage collection yields numerous software engineering benefits, but its quantitative impact on performance remains elusive. One can compare the cost of conservative garbage collection to explicit memory management in C/C++ programs by linking in an appropriate collector. This kind of direct comparison is not possible for languages designed for garbage collection (e.g., Java), because programs in these languages naturally do not contain calls to free. Thus, the actual gap between the time and space performance of explicit memory management and precise, copying garbage collection remains unknown.We introduce a novel experimental methodology that lets us quantify the performance of precise garbage collection versus explicit memory management. Our system allows us to treat unaltered Java programs as if they used explicit memory management by relying on oracles to insert calls to free. These oracles are generated from profile information gathered in earlier application runs. By executing inside an architecturally-detailed simulator, this "oracular" memory manager eliminates the effects of consulting an oracle while measuring the costs of calling malloc and free. We evaluate two different oracles: a liveness-based oracle that aggressively frees objects immediately after their last use, and a reachabilitybased oracle that conservatively frees objects just after they are last reachable. These oracles span the range of possible placement of explicit deallocation calls.We compare explicit memory management to both copying and non-copying garbage collectors across a range of benchmarks using the oracular memory manager, and present real (non-simulated) runs that lend further validity to our results. These results quantify the time-space tradeoff of garbage collection: with five times as much memory, an Appel-style generational collector with a noncopying mature space matches the performance of reachabilitybased explicit memory management. With only three times as much memory, the collector runs on average 17% slower than explicit memory management. However, with only twice as much memory, garbage collection degrades performance by nearly 70%. When * Work performed at the University of Massachusetts Amherst.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. General Terms Experimentation, Measurement, PerformanceKeywords oracular memory management, garbage collection, explicit memory management, performance analysis, time-space tradeoff, throughput, paging IntroductionGarbage collection, or automatic memory management, provides significant software engineering benefits over explicit memory management. For example, garbage collection frees programmers from the b...

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Section: Reachability Versus Livenessmentioning

confidence: 99%

Quantifying the performance of garbage collection vs. explicit memory management

Hertz

Berger

2005

SIGPLAN Not.

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“…The re-hashing will call the hashCode() method of the junk objects, so even though they are junk they do not appear to be stale. A number of other researchers have found that the notion of staleness is a useful one in practice [3,6,18,19,32,[34][35][36]. Graph theoretic background.…”

Section: Discussionmentioning

confidence: 99%

Object ownership profiling

Rayside

Mendel

2007

Proceedings of the Twenty-Second IEEE/ACM International Conference on Automated Software Engineering

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We introduce object ownership profiling, a technique for finding and fixing memory leaks in object-oriented programs. Object ownership profiling is the first memory profiling technique that reports both a hierarchy of allocated objects along with size and time information aggregated up that hierarchy. In addition, it reveals the cross-hierarchy interactions that are essential to pinpointing the source of a leak.We identify five memory management anti-patterns, including two that involve rich heap structure and object interactions, and are novel contributions of this work.We apply object ownership profiling to find and fix memory leaks in the Alloy IDE v3 that users had complained about for years, and that had eluded detection by code reviews and type-based commercial memory profilers.

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“…As Hirzel et al [12,13] show, the quality of the results of a reachability-based leak detector largely depends on the type and liveness accuracy of its reachability traversal. They find that the liveness accuracy is more important.…”

Section: Memory Leak Detectionmentioning

confidence: 97%

Low-overhead memory leak detection using adaptive statistical profiling

Hauswirth¹,

Chilimbi

2004

SIGOPS Oper. Syst. Rev.

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Sampling has been successfully used to identify performance optimization opportunities. We would like to apply similar techniques to check program correctness. Unfortunately, sampling provides poor coverage of infrequently executed code, where bugs often lurk. We describe an adaptive profiling scheme that addresses this by sampling executions of code segments at a rate inversely proportional to their execution frequency.To validate our ideas, we have implemented SWAT, a novel memory leak detection tool. SWAT traces program allocations/ frees to construct a heap model and uses our adaptive profiling infrastructure to monitor loads/stores to these objects with low overhead. SWAT reports 'stale' objects that have not been accessed for a 'long' time as leaks. This allows it to find all leaks that manifest during the current program execution. Since SWAT has low runtime overhead (< 5%), and low space overhead (< 10% in most cases and often less than 5%), it can be used to track leaks in production code that take days to manifest. In addition to identifying the allocations that leak memory, SWAT exposes where the program last accessed the leaked data, which facilitates debugging and fixing the leak. SWAT has been used by several product groups at Microsoft for the past 18 months and has proved effective at detecting leaks with a low false positive rate (<10%).

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On the Usefulness of Liveness for Garbage Collection and Leak Detection

Cited by 8 publications

References 24 publications

Quantifying the performance of garbage collection vs. explicit memory management

Quantifying the performance of garbage collection vs. explicit memory management

Object ownership profiling

Low-overhead memory leak detection using adaptive statistical profiling

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