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Efficient object sampling via weak references

Published:16 October 2000Publication History

ABSTRACT

The performance of automatic memory management may be improved if the policies used in allocating and collecting objects had knowledge of the lifetimes of objects. To date, approaches to the pretenuring of objects in older generations have relied on profile-driven feedback gathered from trace runs. This feedback has been used to specialize allocation sites in a program. These approaches suffer from a number of limitations. We propose an alternative that through efficient sampling of objects allows for on-line adaption of allocation sites to improve the efficiency of the memory system. In doing so, we make use of a facility already present in many collectors such as those found in Java™ virtual machines: weak references. By judiciously tracking a subset of allocated objects with weak references, we are able to gather the necessary statistics to make better object-placement decisions.

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        cover image ACM Conferences
        ISMM '00: Proceedings of the 2nd international symposium on Memory management
        October 2000
        178 pages
        ISBN:1581132638
        DOI:10.1145/362422

        Copyright © 2000 ACM

        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. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 16 October 2000

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        Acceptance Rates

        ISMM '00 Paper Acceptance Rate18of39submissions,46%Overall Acceptance Rate72of156submissions,46%

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