Identification of logically related heap regions

Marron, Mark; Kapur, Deepak y Hermenegildo, Manuel V. (2009). Identification of logically related heap regions. En: "The 2009 International Symposium on Memory Management", June 19-20, 2009, Trinity College Dublin, Ireland. ISBN 978-1-60558-347-1.

Descripción

Título: Identification of logically related heap regions
Autor/es:
  • Marron, Mark
  • Kapur, Deepak
  • Hermenegildo, Manuel V.
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: The 2009 International Symposium on Memory Management
Fechas del Evento: June 19-20, 2009
Lugar del Evento: Trinity College Dublin, Ireland
Título del Libro: ISMM '09 Proceedings of the 2009 international symposium on Memory management
Fecha: 2009
ISBN: 978-1-60558-347-1
Materias:
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

This paper introduces a novel technique for identifying logically related sections of the heap such as recursive data structures, objects that are part of the same multi-component structure, and related groups of objects stored in the same collection/array. When combined withthe lifetime properties of these structures, this information can be used to drive a range of program optimizations including pool allocation, object co-location, static deallocation, and region-based garbage collection. The technique outlined in this paper also improves the efficiency of the static analysis by providing a normal form for the abstract models (speeding the convergence of the static analysis). We focus on two techniques for grouping parts of the heap. The first is a technique for precisely identifying recursive data structures in object-oriented programs based on the types declared in the program. The second technique is a novel method for grouping objects that make up the same composite structure and that allows us to partition the objects stored in a collection/array into groups based on a similarity relation. We provide a parametric component in the similarity relation in order to support specific analysis applications (such as a numeric analysis which would need to partition the objects based on numeric properties of the fields). Using the Barnes-Hut benchmark from the JOlden suite we show how these grouping methods can be used to identify various types of logical structures allowing the application of many region-based program optimizations.

Más información

ID de Registro: 14305
Identificador DC: http://oa.upm.es/14305/
Identificador OAI: oai:oa.upm.es:14305
URL Oficial: http://dl.acm.org/citation.cfm?doid=1542431.1542445
Depositado por: Biblioteca Facultad de Informatica
Depositado el: 18 Ene 2013 08:48
Ultima Modificación: 21 Abr 2016 13:54
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