Parallel and Distributed Intelligent Systems Virendrakumar C. Bhavsar Professor and Director, Advanced Computational Research Laboratory Faculty of Computer Science University of New Brunswick Fredericton, NB

Outline Past Research Work Current Research Work Conclusion

Past Research Work Parallel/Distributed Processing - Parallel Computer Architecture - Design and Analysis of Parallel Algorithms - Real-time and Fault-Tolerant Systems Artificial Neural Networks Learning Machines and Evolutionary Computation Computer Graphics Visualization

Advanced Computational Research Laboratory High Performance Computational Problem- Solving and Visualization Environment Computational Experiments in multiple disciplines: CS, Science and Eng. 16-Processor IBM SP3 Member of C3.ca Association, Inc. (

Advanced Computational Research Laboratory Virendra Bhavsar, Director Chris MacPhee, Scientific Computing Support Sean Seeley, System Administrator

ACRL’s IBM SP 4 Winterhawk II nodes – 16 processors; 24 GFLOPS High Perforrnance Switch Disk Gigabit Ethernet

IBM SP at ACRL: The Clustered SMP Four 4-way SMPs Each node has its own copy of the O/S Processors on the node are closer than those on different nodes

IBM Power3 SP Switch Bidirectional multistage interconnection networks (MIN) 300 MB/sec bi-directional 1.2  sec latency

Past Research Work (cont.) Multimedia for Education: Intelligent Tutoring Systems Multi-Lingual Systems and Transliteration Web Portal with an Intelligent User Profile Generator Multi-Agent Systems  Supervision/Co-supervision 50 master's theses; 4 doctoral theses 5 post-doctoral fellows/research associates

Current Research Work Parallel/Distributed Processing -PaGrid: A Mesh Partitioner for Computational Grids - Dynamic Partitioning for Efficient Processing on Parallel Computers Multi-Agent Systems (Distributed Artificial Intelligence) - Multi-Agent System for Automatic Annotation of EST Sequences (funded by ‘The Canadian Potato Genomics’) - CS6999: Multi-Agent Systems - Dynamic Clustering of Agents in the Café - Agents with Ontology-based Keyphrases and Tree-distance algorithms - Scalability studies of Multi-Agent Systems - eCommerce applications

Current Research Work eLearning (eduSorceCanada Project) - Reuse and exchange course content stored as “learning objects.’’ - Implementation and testing of learning objects using CanCore metadata -XML schema for content packaging - other projects

March, 2000Copyright (C) C3.ca15 What is a GRID System Cooperative network of shared resources - Includes computers, network links, human resources and databases Supports the development of advanced R&D applications in Science, Engineering and Technology Development, Finance and the Arts.

March, 2000Copyright (C) C3.ca16 GRID Applications Large scale and resource intensive frontier applications – R&D applications that go beyond current technological capabilities – Technology development applications in multi-media, finance, production arts, hard sciences and engineering. - Multi-media applications such as embedded video, digital video servers and video conferencing.

March, 2000Copyright (C) C3.ca17 Current C3.ca RP Network

The Canadian Potato Genomics Project 46% of national potato production $1 Billion/year ATLANTIC CANADA Solanum Genomics International Inc. Potato Research Center of AAFC Home of McCain Foods Ltd. $5.5 billion/year

Research Areas Bioinformatic Analysis Access to resources via CBR membership/node status Raw sequence processing and analysis by Fredericton bioinformatics group (Vector trimming, base calling, clustering, contig assembly, BLAST, annotations) Relational database management system of CPGP to link NRC (sequencing), CBR and researchers In silico assignment of gene function Microarray data The Canadian Potato Genomics Project

Research Areas Bioinformatic Research To Suit Project Needs (UNB): Autonomous agent development to automatically update sequence annotations Enhancement of bioinformatic algorithm performance with parallel computing Algorithm development using annotation information to enhance sequence searching The application of clustering and learning techniques to the analysis of expression data

ServerServer ServerServer ServerServer ServerServer ServerServer anwhere.else cs.stir.ac.uk meto.gov.uk ucsd.edu ai.it.nrc.ca Clients Café

Performance Evaluation of ACORN Test-bed: Several Autonomous Servers, each serving autonomous virtual users Virtual User - capable of creating agents - picks up a topic from a client core’s interest - migrates to other servers - potential destinations

Performance Evaluation of ACORN

Why learning objects? COST: 1000s of colleges have common course topics large numbers of courses are going online World does not need 1000s of similar learning topics World needs only about a dozen Expensive to develop so sharing is essential (From Downes, 2000) Design courses as a collection of learning objects NOT HTML

What is METADATA? data about data Example:January 31, janvier Metadata standards are agreed-on criteria for describing data to support interoperability

Metadata and RDF implementation * Resource Description Framework (RDF) = structure * XML Metadata = semantics & resources

Conclusion Parallel/Distributed Processing  Multi-Agent Systems (Distributed Artificial Intelligence)  NSERC Project, The Canadian Potato Genomics Project eLearning (eduSorceCanada Project) Automated and manually-driven user profile generation and update