Parallel and Distributed Intelligent Systems
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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 [email protected] www.cs.unb.ca/profs/bhavsar www.cs.unb.ca/acrl Outline

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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

[email protected]

www.cs.unb.ca/profs/bhavsar

www.cs.unb.ca/acrl


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Outline

  • Past Research Work

  • Current Research Work

  • Conclusion


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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


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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. (http://www.c3.ca)


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Advanced Computational Research Laboratory

www.cs.unb.ca/acrl

  • Virendra Bhavsar, Director

  • Chris MacPhee, Scientific Computing Support

  • Sean Seeley, System Administrator


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Disk

ACRL’s IBM SP

  • 4 Winterhawk II nodes

    • 16 processors; 24 GFLOPS

  • High Perforrnance Switch

Gigabit Ethernet


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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


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IBM Power3 SP Switch

  • Bidirectional multistage interconnection networks (MIN)

  • 300 MB/sec bi-directional

  • 1.2 sec latency


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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


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    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


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    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


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    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.

    Copyright (C) C3.ca


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    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.

    Copyright (C) C3.ca


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    Current C3.ca RP Network

    Copyright (C) C3.ca


    The canadian potato genomics project l.jpg
    The Canadian Potato Genomics Project

    ATLANTIC

    CANADA

    • 46% of national

    • potato production

    • $1 Billion/year

    • Home of McCain

    • Foods Ltd.

    • $5.5 billion/year

    • Potato Research

    • Center of AAFC

    • Solanum Genomics

    • International Inc.


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    The Canadian Potato Genomics Project

    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


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    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


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    S

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    Café

    Café

    [email protected]

    ucsd.edu

    [email protected]

    ai.it.nrc.ca

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    [email protected]

    [email protected]

    [email protected]

    anwhere.else

    [email protected]

    cs.stir.ac.uk

    meto.gov.uk

    [email protected]

    Clients

    [email protected]

    [email protected]

    [email protected]

    [email protected]


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    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



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    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


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    What is METADATA?

    data about data

    Metadata standards are agreed-on criteria for describing data to support interoperability

    Example: January 31, 2001

    31 janvier 2001

    2001-01-31

    01-31-2000

    31012000


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    Metadata and RDF implementation

    * XML

    * Resource Description Framework

    (RDF) = structure

    Metadata =

    semantics & resources


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    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


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