Title :
PARTES: A partitioning scheme for parallel matching
Author :
Gallucci, Stefano ; Tan, Jack ; Hwang, Kuo-Wei
Author_Institution :
Dept. of Comput. Sci. Dept., Houston Univ., TX, USA
Abstract :
Focuses on the partitioning of rules for parallel matching in a production system. The approach, called PARTES, applies the min-cut technique to a dataflow discrimination network (DDN) that represents the antecedents of the rules. The goal is to maximize the sharing of memory nodes within a partition while minimizing the duplication of nodes across partitions. The authors illustrate the technique using a cost model based on shared memory nodes defined in a DDN created by the Rete algorithm. A generalization to any other algorithm utilizing a DDN is straightforward. A performance analysis is provided to show the effectiveness of PARTES
Keywords :
generalisation (artificial intelligence); knowledge representation; minimax techniques; parallel algorithms; pattern matching; shared memory systems; PARTES; Rete algorithm; cost model; dataflow discrimination network; generalization; memory node sharing; min-cut technique; node duplication; parallel matching; performance analysis; production system; rule antecedent representation; rule partitioning scheme; Clustering algorithms; Costs; Databases; Monitoring; Partitioning algorithms; Performance analysis; Performance evaluation; Production systems; Real time systems; Software algorithms;
Conference_Titel :
Tools with Artificial Intelligence, 1993. TAI '93. Proceedings., Fifth International Conference on
Conference_Location :
Boston, MA
Print_ISBN :
0-8186-4200-9
DOI :
10.1109/TAI.1993.633983