Transcript Parallel and Distributed Intelligent Systems

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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Past Research Work
Current Research Work
Conclusion
Past Research Work
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Parallel/Distributed Processing
- Parallel Computer Architecture
- Design and Analysis of Parallel Algorithms
- Real-time and Fault-Tolerant Systems
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Artificial Neural Networks
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Learning Machines and Evolutionary
Computation
 Computer Graphics
 Visualization
Advanced Computational Research
Laboratory
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High Performance Computational ProblemSolving 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)
Advanced Computational Research
Laboratory
www.cs.unb.ca/acrl
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Virendra Bhavsar, Director
Chris MacPhee, Scientific Computing Support
Sean Seeley, System Administrator
ACRL’s IBM SP
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– 16 processors; 24
Gigabit
Ethernet
GFLOPS
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Disk
4 Winterhawk II nodes
High Perforrnance
Switch
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
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Bidirectional multistage
interconnection networks
(MIN)
300 MB/sec bi-directional
1.2 sec latency
Past Research Work (cont.)
Multimedia for Education: Intelligent Tutoring
Systems
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Multi-Lingual Systems and Transliteration
 Web Portal with an Intelligent User Profile
Generator
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Multi-Agent Systems
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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
What is a GRID System
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Cooperative network of shared resources
- Includes computers, network links, human resources and
databases
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Supports the development of advanced R&D
applications in Science, Engineering and
Technology Development, Finance and the Arts.
March, 2000
Copyright (C) C3.ca
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GRID Applications
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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, 2000
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Current C3.ca RP Network
March, 2000
Copyright (C) C3.ca
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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.
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, an
• 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
[email protected]
[email protected]
ucsd.edu
ai.it.nrc.ca
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Café
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Café
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cs.stir.ac.uk
meto.gov.uk
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Café
Clients
[email protected]
[email protected]
[email protected]
[email protected]
[email protected]
Performance Evaluation of ACORN
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Test-bed: Several Autonomous Servers, each
serving autonomous virtual users
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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
120
Time (Seconds)
100
80
60
40
20
0
0
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20
40
60
Number of Agents
80
Mingle Tim e with cosine
Total Exection Time with cosine
Mingle Tim e for without cosine
Total Exection Time without cosine
100
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)
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
Metadata and RDF
implementation
* XML
* Resource Description Framework
(RDF) = structure
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