| Automatic heap sizing: taking real memory into account |
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International Symposium on Memory Management
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Proceedings of the 4th international symposium on Memory management
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Vancouver, BC, Canada
SESSION: New garbage collection algorithms and strategies
table of contents
Pages: 61 - 72
Year of Publication: 2004
ISBN:1-58113-945-4
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Authors
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Ting Yang
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University of Massachusetts, Amherst, MA
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Matthew Hertz
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University of Massachusetts, Amherst, MA
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Emery D. Berger
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University of Massachusetts, Amherst, MA
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Scott F. Kaplan
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Amherst College, Amherst, MA
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J. Eliot B. Moss
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University of Massachusetts, Amherst, MA
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Downloads (6 Weeks): 2, Downloads (12 Months): 39, Citation Count: 15
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ABSTRACT
Heap size has a huge impact on the performance of garbage collected applications. A heap that barely meets the application's needs causes excessive GC overhead, while a heap that exceeds physical memory induces paging. Choosing the best heap size <i>a priori</i> is impossible in multiprogrammed environments, where physical memory allocations to processes change constantly. We present an automatic heap-sizing algorithm applicable to different garbage collectors with only modest changes. It relies on an analytical model and on detailed information from the virtual memory manager. The model characterizes the relation between collection algorithm, heap size, and footprint. The virtual memory manager tracks recent reference behavior, reporting the current footprint and allocation to the collector. The collector uses those values as inputs to its model to compute a heap size that maximizes throughput while minimizing paging. We show that our adaptive heap sizing algorithm can substantially reduce running time over fixed-sized heaps.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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T. Yang, E. D. Berger, M. Hertz, S. F. Kaplan, and J. E. B. Moss. Autonomic heap sizing: Taking real memory into account. Technical Report TR-04-14, University of Massachusetts, July 2004.
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CITED BY 15
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Chengliang Zhang , Kirk Kelsey , Xipeng Shen , Chen Ding , Matthew Hertz , Mitsunori Ogihara, Program-level adaptive memory management, Proceedings of the 2006 international symposium on Memory management, June 10-11, 2006, Ottawa, Ontario, Canada
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