Memorias de investigación
Communications at congresses:
Negative Ternary Set-Sharing

Research Areas
  • Programming language

The Set-Sharing domain has been widely used to infer at compile- time interesting properties of logic programs such as occurs-check reduction, automatic parallelization, and finite-tree analysis. However, performing abstract unification in this domain requires a closure operation that increases the number of sharing groups exponentially. Much attention has been given to mitigating this key inefficiency in this otherwise very useful domain. In this paper we present a novel approach to Set-Sharing: we define a new representation that leverages the complement (or negative) sharing relationships of the original sharing set, without loss of accuracy. Intuitively, given an abstract state shV over the finite set of variables of interest V, its negative representation is ℘(V) \ shV . Using this encoding during analysis dramatically reduces the number of elements that need to be represented in the abstract states and during abstract unification as the cardinality of the original set grows toward 2|V| . To further compress the num- ber of elements, we express the set-sharing relationships through a set of ternary strings that compacts the representation by eliminating redundancies among the sharing sets. Our experiments show that our approach can compress the number of relationships, reducing significantly the memory usage and running time of all abstract operations, including abstract unification.
24th International Conference on Logic Programming
Udine, Italy
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Negative Ternary Set-Sharing
  • Participante: Elena Ackley Universidad de Nuevo Mexico
  • Participante: Jorge Navas Caldente University of Singapore
  • Autor: Manuel de Hermenegildo Salinas UPM
  • Participante: Stephanie Forrest Universidad de Nuevo Mexico
  • Participante: Eric Trias Universidad de Nuevo Mexico

Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Computación lógica, Lenguajes, Implementación y Paralelismo (CLIP)
  • Departamento: Inteligencia Artificial