Intelligent Memory Management Heuristics

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Automatic memory management is crucial in implementation of runtime systems even though it induces a significant computational overhead. In this thesis I explore the use of statistical properties of the directed graph describing the set of live data to decide between garbage collection and heap expansion in a memory management algorithm combining the dynamic array represented heaps with a mark and sweep garbage collector to enhance its performance. The sampling method predicting the density and the distribution of useful data is implemented as a partial marking algorithm. The algorithm randomly marks the nodes of the directed graph representing the live ... continued below

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Panthulu, Pradeep December 2003.

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This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 210 times . More information about this thesis can be viewed below.

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  • Panthulu, Pradeep

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Description

Automatic memory management is crucial in implementation of runtime systems even though it induces a significant computational overhead. In this thesis I explore the use of statistical properties of the directed graph describing the set of live data to decide between garbage collection and heap expansion in a memory management algorithm combining the dynamic array represented heaps with a mark and sweep garbage collector to enhance its performance. The sampling method predicting the density and the distribution of useful data is implemented as a partial marking algorithm. The algorithm randomly marks the nodes of the directed graph representing the live data at different depths with a variable probability factor p. Using the information gathered by the partial marking algorithm in the current step and the knowledge gathered in the previous iterations, the proposed empirical formula predicts with reasonable accuracy the density of live nodes on the heap, to decide between garbage collection and heap expansion. The resulting heuristics are tested empirically and shown to improve overall execution performance significantly in the context of the Jinni Prolog compiler's runtime system.

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  • December 2003

Added to The UNT Digital Library

  • Feb. 15, 2008, 3:01 p.m.

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  • Jan. 21, 2014, 1:48 p.m.

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Citations, Rights, Re-Use

Panthulu, Pradeep. Intelligent Memory Management Heuristics, thesis, December 2003; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc4399/: accessed September 19, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .