{"id":{"repo_id":"unt","oai_identifier":"info:ark/67531/metadc4399"},"canonical_url":"https://search.dev.ndltd.org/etd/unt/info:ark/67531/metadc4399","repository":{"repo_id":"unt","name":"University of North Texas","base_url":"https://digital.library.unt.edu/oai/"},"display":{"title":"Intelligent Memory Management Heuristics","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Panthulu, Pradeep"],"institution":"University of North Texas","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Tarau, Paul","Jacob, Roy T.","Barrett, David","Mihalcea, Rada, 1974-"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2003,"date_issued":"2003-12","date_published":"2003-12","updated_at":"2026-07-24T05:35:09Z","subjects":["Memory management (Computer science)","heuristics","statistical methods","partial marking","random graphs","probability factor"],"languages":["English"],"rights":["Public","Copyright","Panthulu, Pradeep","Copyright is held by the author, unless otherwise noted. All rights reserved."],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["oclc: 54453341","https://digital.library.unt.edu/ark:/67531/metadc4399/","ark: ark:/67531/metadc4399"],"render_values":[{"text":"oclc: 54453341","href":null,"code":true},{"text":"https://digital.library.unt.edu/ark:/67531/metadc4399/","href":"https://digital.library.unt.edu/ark:/67531/metadc4399/","code":true},{"text":"ark: ark:/67531/metadc4399","href":null,"code":true}]}]},"links":{"outbound_url":"https://doi.org/10.12794/metadc4399","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tarau, Paul","Jacob, Roy T.","Barrett, David","Mihalcea, Rada, 1974-"]},{"key":"dc:creator","label":"Author","values":["Panthulu, Pradeep"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2003-12"]},{"key":"dc:publisher","label":"Institution","values":["University of North Texas"]},{"key":"dc:type","label":"Dc Type","values":["Thesis or Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Memory management (Computer science)","heuristics","statistical methods","partial marking","random graphs","probability factor"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["Public","Copyright","Panthulu, Pradeep","Copyright is held by the author, unless otherwise noted. All rights reserved."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["oclc: 54453341","doi: 10.12794/metadc4399","https://digital.library.unt.edu/ark:/67531/metadc4399/","ark: ark:/67531/metadc4399"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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."]},{"key":"dc:format","label":"Dc Format","values":["Text"]},{"key":"dc:title","label":"Title","values":["Intelligent Memory Management Heuristics"]}]}],"canonical_facts":{"dc:contributor":["Tarau, Paul","Jacob, Roy T.","Barrett, David","Mihalcea, Rada, 1974-"],"dc:creator":["Panthulu, Pradeep"],"dc:date":["2003-12"],"dc: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."],"dc:format":["Text"],"dc:identifier":["oclc: 54453341","doi: 10.12794/metadc4399","https://digital.library.unt.edu/ark:/67531/metadc4399/","ark: ark:/67531/metadc4399"],"dc:language":["English"],"dc:publisher":["University of North Texas"],"dc:rights":["Public","Copyright","Panthulu, Pradeep","Copyright is held by the author, unless otherwise noted. All rights reserved."],"dc:subject":["Memory management (Computer science)","heuristics","statistical methods","partial marking","random graphs","probability factor"],"dc:title":["Intelligent Memory Management Heuristics"],"dc:type":["Thesis or Dissertation"]},"updated_at":"2026-07-24T05:35:09Z"}