Dynamic selection of application-specific garbage collectors

Soman, Sunil; Krintz, Chandra; Bacon, David F.

doi:10.1145/1029873.1029880

Cited by 57 publications

(39 citation statements)

References 34 publications

(44 reference statements)

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“…We chose these two because they are representative GC configurations. The former is the classic tracing GC which can give better performance for some benchmarks when the heap size is large enough [27]. The latter is the best choice for most benchmarks in most heap configurations of Jikes RVM.…”

Section: Jikes Rvm Resultsmentioning

confidence: 99%

Relative factors in performance analysis of Java virtual machines

Verbrugge

Gagnon

2006

Proceedings of the 2nd International Conference on Virtual Execution Environments

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Many new Java runtime optimizations report relatively small, single-digit performance improvements. On modern virtual and actual hardware, however, the performance impact of an optimization can be influenced by a variety of factors in the underlying systems. Using a case study of a new garbage collection optimization in two different Java virtual machines, we show the relative effects of issues that must be taken into consideration when claiming an improvement. We examine the specific and overall performance changes due to our optimization and show how unintended side-effects can contribute to, and distort the final assessment. Our experience shows that VM and hardware concerns can generate variances of up to 9.5% in whole program execution time. Consideration of these confounding effects is critical to a good, objective understanding of Java performance and optimization.

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Section: Jikes Rvm Resultsmentioning

confidence: 99%

Relative factors in performance analysis of Java virtual machines

Verbrugge

Gagnon

2006

Proceedings of the 2nd International Conference on Virtual Execution Environments

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“…On the other hand, if the JVM is made virtualization-aware, it can proactively reduce its heap size. While doing so can increase the number of garbage collections and decrease application performance [12], this degradation is an order of magnitude smaller than the degradation caused by swapping to disk when the JVM does not have enough physical memory [13]. Without a virtualization-aware JVM capable of re-adjusting its heap size during runtime, Ginkgo's decisions to overcommit memory were being based on the current JVM heap size instead of attained performance as we intended.…”

Section: Constraint Per Hypervisormentioning

confidence: 99%

Applications Know Best: Performance-Driven Memory Overcommit with Ginkgo

Hines¹,

Gordon²,

Silva³

et al. 2011

2011 IEEE Third International Conference on Cloud Computing Technology and Science

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Abstract-Memory overcommitment enables cloud providers to host more virtual machines on a single physical server, exploiting spare CPU and I/O capacity when physical memory becomes the bottleneck for virtual machine deployment. However, overcommiting memory can also cause noticeable application performance degradation. We present Ginkgo, a policy framework for overcomitting memory in an informed and automated fashion. By directly correlating application-level performance to memory, Ginkgo automates the redistribution of scarce memory across all virtual machines, satisfying performance and capacity constraints. Ginkgo also achieves memory gains for traditionally fixed-size Java applications by coordinating the redistribution of available memory with the activities of the Java Virtual Machine heap. When compared to a non-overcommited system, Ginkgo runs the DayTrader 2.0 and SPECWeb 2009 benchmarks with the same number of virtual machines while saving up to 73% (50% omitting free space) of a physical server's memory while keeping application performance degradation within 7%.

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“…Third, LOS and non-LOS collections are done in different phases, which may affect the scalability of parallel garbage collection. In Soman et al [16], discussed about applying different GC algorithms in the same heap space, but their work does not involve adjusting the heap partitioning dynamically. The study done by Barrett and Zorn [4] is the only known publication that studies space boundary adjustment.…”

Section: Related Workmentioning

confidence: 99%

Space-and-Time Efficient Parallel Garbage Collector for Data-Intensive Applications

Liu

Wang²,

Li³

et al. 2010

Int J Parallel Prog

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As multithreaded server applications and runtime systems prevail, garbage collection is becoming an essential feature to support high performance systems, especially those running data-intensive applications. The fundamental issue of garbage collector (GC) design is to maximize the recycled space with minimal time overhead. This paper proposes two innovative solutions: one to improve space efficiency, and the other to improve time efficiency. To achieve space efficiency, we propose the Space Tuner that utilizes the novel concept of allocation speed to reduce wasted space. Conventional static space partitioning techniques often lead to inefficient space utilization. The Space Tuner adjusts the heap partitioning dynamically such that when a collection is triggered, all space partitions are fully filled. To achieve time efficiency, we propose a novel parallelization method that reduces the compacting GC parallelization problem into a tree traversal parallelization problem. This method can be applied for both normal and large object compaction. Object compaction is hard to parallelize due to strong data dependencies such that the source object can not be moved to its target location until the object originally in the target location has been moved out. Our proposed algorithm overcomes the difficulties by dividing the heap into equal-sized blocks and parallelizing the movement of the independent blocks. It Parallel Prog (2011) 39:451-472 is noteworthy that these proposed algorithms are generic such that they can be utilized in different GC designs. The proposed techniques have been implemented in Apache Harmony JVM and we evaluated the proposed algorithms with SPECjbb and Dacapo benchmark suites. The experiment results demonstrate that our proposed algorithms greatly improve space utilization and the corresponding parallelization schemes are scalable, which brings time efficiency.

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Dynamic selection of application-specific garbage collectors

Cited by 57 publications

References 34 publications

Relative factors in performance analysis of Java virtual machines

Relative factors in performance analysis of Java virtual machines

Applications Know Best: Performance-Driven Memory Overcommit with Ginkgo

Space-and-Time Efficient Parallel Garbage Collector for Data-Intensive Applications

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