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Parallel and Distributed Intelligent Systems Virendrakumar C. Bhavsar Professor and Director, Advanced Computational Res PowerPoint Presentation
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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 bhavsar@unb.ca www.cs.unb.ca/profs/bhavsar www.cs.unb.ca/acrl Outline

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slide1

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

bhavsar@unb.ca

www.cs.unb.ca/profs/bhavsar

www.cs.unb.ca/acrl

outline
Outline
  • Past Research Work
  • Current Research Work
  • Conclusion
slide3

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
slide4

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)
slide5

Advanced Computational Research Laboratory

www.cs.unb.ca/acrl

  • Virendra Bhavsar, Director
  • Chris MacPhee, Scientific Computing Support
  • Sean Seeley, System Administrator
acrl s ibm sp

Disk

ACRL’s IBM SP
  • 4 Winterhawk II nodes
    • 16 processors; 24 GFLOPS
  • High Perforrnance Switch

Gigabit Ethernet

slide10

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
IBM Power3 SP Switch
  • Bidirectional multistage interconnection networks (MIN)
  • 300 MB/sec bi-directional
  • 1.2 sec latency
slide12

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
slide13

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
slide14

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
what is a grid system
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

grid applications
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

current c3 ca rp network
Current C3.ca RP Network

Copyright (C) C3.ca

the canadian potato genomics project
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.
slide19

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
slide20

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
slide21

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Café

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Café

Café

tom@ucsd.edu

ucsd.edu

ymasrour@ai.it.nrc.ca

ai.it.nrc.ca

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bob@ai.it.nrc.ca

dick@ucsd.edu

steve@ai.it.nrc.ca

anwhere.else

foo@anywhere.else

cs.stir.ac.uk

meto.gov.uk

joan@unb.ca

Clients

bhavsar@unb.ca

wibble@cs.stir.ac.uk

graham@cs.stir.ac.uk

anne@cs.stir.ac.uk

performance evaluation of acorn
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

slide24

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

slide25

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

slide26

Metadata and RDF implementation

* XML

* Resource Description Framework

(RDF) = structure

Metadata =

semantics & resources

slide27

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