Thesis (M.S.)--Computing, Georgia Institute of Technology, 2005. / Pande, Santosh, Committee Chair ; Lee, Hsien-Hsin Sean, Committee Member ; Uh, Gang-Ryung, Committee Member. Includes bibliographical references.

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 63-64). Also available on the Internet.

Thesis (M. Phil.)--University of Hong Kong, 1996. / Includes bibliographical references (leaf 60-64).

Thesis (Ph. D.)--University of Texas at Austin, 1998. / Vita. Includes bibliographical references (leaves 113-124). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--University of Washington, 1996. / Vita. Includes bibliographical references (p. [149]-157).

Thesis (M.S.)--University of Missouri-Columbia, 2004. / Typescript. Includes bibliographical references (leaves 63-64). Also available on the Internet.

Thesis (Ph. D.)--University of Texas at Austin, 2007. / Vita. Includes bibliographical references.

published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / Computer Science / Master / Master of Philosophy

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.