A scalable distributed heterogeneous associative logic programming system

Ryan, Stephen W.; Bansal, Arvind K.

doi:10.1109/tai.1997.632234

Cited by 2 publications

(1 citation statement)

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“…There are four new abstract instructions in the coordinating abstract machine [23]: get_servers, broadcast_goal, receive_binding, and repeat_else_try that facilitate distributed processing.…”

Section: Distributed Abstract Instructionsmentioning

confidence: 99%

A Scalable Distributed Multimedia Knowledge Retrieval System on a Cluster of Heterogeneous High Performance Architectures

Ryan

Bansal

2000

Int. J. Artif. Intell. Tools

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This paper describes a system to distribute and retrieve multimedia knowledge on a cluster of heterogeneous high performance architectures distributed over the Internet. The knowledge is represented using facts and rules in an associative logic-programming model. Associative computation facilitates distribution of facts and rules, and exploits coarse grain data parallel computation. Associative logic programming uses a flat data model that can be easily mapped onto heterogeneous architectures. The paper describes an abstract instruction set for the distributed version of the associative logic programming and the corresponding implementation.The implementation uses a message-passing library for architecture independence within a cluster, uses object oriented programming for modularity and portability, and uses Java as a front-end interface to provide a graphical user interface and multimedia capability and remote access via the Internet. The performance results on a cluster of IBM RS 6000 workstations are presented. The results show that distribution of data improves the performance almost linearly for small number of processors in a cluster.

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Section: Distributed Abstract Instructionsmentioning

confidence: 99%