Memorias de investigación
Artículos en revistas:
Efficient Context-Sensitive Shape Analysis with Graph Based Heap Models
Año:2008

Áreas de investigación
  • Lenguaje de programación

Datos
Descripción
The performance of heap analysis techniques has a significant impact on their utility in an optimizing compiler. Most shape analysis techniques perform interprocedural dataflow analysis in a context-sensitive manner, which can result in analyzing each procedure body many times (causing significant increases in runtime even if the analysis results are memoized). To improve the effectiveness of memoization (and thus speed up the analysis) project/extend operations are used to remove portions of the heap model that cannot be affected by the called procedure (effectively reducing the number of different contexts that a procedure needs to be analyzed with). This paper introduces project/extend operations that are capable of accurately modeling properties that are important when analyzing non-trivial programs (sharing, nullity information, destructive recursive functions, and composite data structures). The techniques we introduce are able to handle these features while significantly improving the effectiveness of memoizing analysis results (and thus improving analysis performance). Using a range of well known benchmarks (many of which have not been successfully analyzed using other existing shape analysis methods) we demonstrate that our approach results in significant improvements in both accuracy and efficiency over a baseline analysis.
Internacional
Si
JCR del ISI
No
Título de la revista
Compiler Construction
ISSN
03029743
Factor de impacto JCR
0
Información de impacto
Volumen
2008
DOI
10.1007/978-3-540-78791-4_17
Número de revista
4959
Desde la página
245
Hasta la página
259
Mes
ABRIL
Ranking

Esta actividad pertenece a memorias de investigación

Participantes

Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Computación lógica, Lenguajes, Implementación y Paralelismo (CLIP)
  • Departamento: Inteligencia Artificial