Generational stack collection and profile-driven pretenuring

ChengPerry,; HarperRobert,; LeePeter,

doi:10.1145/277652.277718

Cited by 21 publications

(20 citation statements)

References 16 publications

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“…Another approach to lifetime classification for heap optimizations is static profiling [4,7,8,13,18,19,23,24]. For instance, a static profiler finds allocation sites for long-lived objects in a generational collector and recompiles the program to allocate these directly to mature generations [7,8,23,24].…”

Section: Related Workmentioning

confidence: 99%

Dynamic object sampling for pretenuring

Jump

Blackburn

McKinley

2004

Proceedings of the 4th International Symposium on Memory Management

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Many state-of-the-art garbage collectors are generational, collecting the young nursery objects more frequently than old objects. These collectors perform well because young objects tend to die at a higher rate than old ones. However, these collectors do not examine object lifetimes with respect to any particular program or allocation site. This paper introduces low-cost object sampling to dynamically determine lifetimes. The sampler marks an object and records its allocation site every n bytes of allocation. The collector then computes per-site nursery survival rates. Sampling degrades total performance by only 3% on average for sample rates of 256 bytes in Jikes RVM, a rate at which overall lifetime accuracy compares well with sampling every object.An adaptive collector can use this information to tune itself. For example, pretenuring decreases nursery collection work by allocating new, but long-lived, objects directly into the mature space. We introduce a dynamic pretenuring mechanism that detects long-lived allocation sites and pretenures them, given sufficient samples. To react to phase changes, it occasionally backsamples. As with previous online pretenuring, consistent performance improvements on SPECjvm98 benchmarks are difficult to attain since only two combine sufficient allocation load with high nursery survival. Our pretenuring system consistently improves one of these, 213 javac, by 2% to 9% of total time by decreasing collection time by over a factor of two. Sampling and pretenuring overheads slow down all the others. This paper thus provides an efficient sampling mechanism that accurately predicts lifetimes, but leaves open optimization policies that can exploit this information.

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Section: Related Workmentioning

confidence: 99%

Dynamic object sampling for pretenuring

Jump

Blackburn

McKinley

2004

Proceedings of the 4th International Symposium on Memory Management

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“…[26][27][28][29]). Hirzel et al studied a suite of benchmarks and concluded that object connectivity correlates strongly with object lifetime [30].…”

Section: Profilingmentioning

confidence: 99%

Understanding the Impact of Collection Contracts on Design

Nelson

Pearce

Noble

2010

Objects, Models, Components, Patterns

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Abstract. Java provides a specification for a user-defined general purpose equivalence operator for objects, but collections such as Set have more stringent requirements. This inconsistency breaks polymorphism: programmers must take care to follow Set's contract rather than the more general Object contract if their object could enter a Set. We have dynamically profiled 30 Java applications to better understand the way programmers design their objects, to determine whether they program with collections in mind. Our results indicate that objects which enter collections behave very differently to objects which do not. Our findings should help developers understand the impact of design choices they make, and guide future language designers when adding support for collections and/or equality.

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“…Incremental schemes could be used to eliminate repeated scans of a stack [10], and a thread does not need to be scanned more than once while it is suspended. Some "GC crosstalk" between processes is still possible, because a process could create many threads in an effort to get the system to scan them all.…”

Section: Process Separationmentioning

confidence: 99%

Processes in KaffeOS: Isolation, Resource Management, and Sharing in Java

Back¹,

Hsieh²,

Lepreau³

2000

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Single-language runtime systems, in the form of Java virtual machines, are widely deployed platforms for executing untrusted mobile code. These runtimes provide some of the features that operating systems provide: inter-application memory protection and basic system services. They do not, however, provide the ability to isolate applications from each other, or limit their resource consumption. This paper describes KaffeOS, a system that provides these features for a Java runtime. The KaffeOS architecture takes many lessons from operating system design, such as the use of a user/kernel boundary.The KaffeOS architecture supports the OS abstraction of a process in a Java virtual machine. Each process executes as if it were run in its own virtual machine, including separate garbage collection of its own heap. The difficulty in designing KaffeOS lay in balancing the goals of isolation and resource management against the goal of allowing direct sharing. Overall, KaffeOS is up to 11% slower than the freely available JVM on which it is based, which is an acceptable penalty for the safety that it provides. KaffeOS is substantially slower than commercial JVMs, but exhibits much better performance scaling in the presence of uncooperative code.

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Generational stack collection and profile-driven pretenuring

Cited by 21 publications

References 16 publications

Dynamic object sampling for pretenuring

Dynamic object sampling for pretenuring

Understanding the Impact of Collection Contracts on Design

Processes in KaffeOS: Isolation, Resource Management, and Sharing in Java

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