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Advanced Computational Research Laboratory: Exploring High-Performance Computing Environments

Discover the cutting-edge research at ACRL, where computational experiments across disciplines are conducted in a collaborative environment. Learn about the diverse research groups and the wide range of applications for parallel computing. Explore the innovative technologies, software tools, and future workshops offered at the lab.

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Advanced Computational Research Laboratory: Exploring High-Performance Computing Environments

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  1. Advanced Computational Research Laboratory (ACRL) Virendra C. Bhavsar Faculty of Computer Science University of New Brunswick Fredericton, NB, E3B 5A3 Canada

  2. OUTLINE •  ACRL Research Groups •  Introduction to Parallel Processing •  ACRL Research Groups •  Conclusion

  3. ARCL • Advanced Computational Research Laboratory •  High Performance Computational Problem-Solving Environment and Visualization Environment •  Computational Experiments in multiple disciplines: Computer Science, • Science and Engineering •  Located in the Information Technology Center (ITC)

  4. ACRL: Researchers and Groups • Faculty of Computer Science •  Artificial Intelligence Group • - Dr. Spencer, Dr. Nickerson •  Parallel/Distributed Processing Group • - Dr. Bhavsar, Dr. Du, Dr. Ghorbani • Dr. Kaser, Dr. Shaw •  Computational Geometry Group • - Dr. Bremner, Dr. Itturiaga •  Automated Reasoning Group • - Dr. Spencer, Dr. Horton • Bioinformatics Group

  5. ACRL: Researchers and Groups • Faculty of Science •  Physics • - Dr. Hamza (plasma physics, • ionospehere, solar corona) • Dr. Balcolm (magnetic resonance • Imaging) • Dr. Xu (methanol to gasoline • process) •  Chemistry • - Dr. Thakkar (optical computing • materials) • Dr. Grein (ozone related reactions) • Dr. Mattar (cancer drugs, fisheries) • Bioinformatics Group

  6. ACRL: Researchers and Groups • Faculty of Engineering •  Mechanical Engineering • Dr. Hussein (threat-material • detection) • Dr. Sousa ( fire propagation, CFD) • Dr. Biden (artificial limbs) •  Chemical Engineering • Dr. Bendrich (plastics • manufacturing) •  Electrical Engineering • Dr. Chang (electrical machines • Forestry and Environment Management •  New CFI Application

  7. Scientific Computation

  8. Parallel computing - simultaneous use of multiple compute resources to solve a computational problem Why Parallel Computing? - to save time (wall clock time) - to solve larger problems - to alleviate memory constraints - larger databases Parallel Computing

  9. Grand Challenge Problems” - weather and climate - mechanical devices - from prosthetics to spacecraft - electronic circuits - manufacturing processes - geological, seismic activity - biological, human genome - chemical and nuclear reactions Parallel Computing

  10. Commercial applications - parallel databases, data mining - oil exploration - computer-aided diagnosis in medicine - management of national and multi-national corporations - advanced graphics and virtual reality, particularly in the entertainment industry - networked video and multi-media technologies - collaborative work environments Parallel Computing

  11. Ultimately, parallel computing is an attempt to maximize the infinite but seemingly scarce commodity called time Parallel Computing

  12. IBM SP

  13. Quad-Processor System Shared Memory Model

  14. Distributed Memory Model

  15. Similar to IBM SP Hybrid Model

  16. ARCL • Advanced Computational Research Laboratory •  High Performance Multiprocessor • (16-processor) System with • 24 GFLOPS (peak) performance with • 72 GB internal disk storage and 109.2 GB external disk storage •  Software for Computational Studies • and Visualization •  Parallel Programming tools •  E-Commerce Software, including • datamining software

  17. ARCL • Nodes • 4 Compute Nodes: total of 16 processors. • Switch • 300 MB/sec bi-directional • 1.2 µsec latency

  18. ARCL • Node • 2 x 2-way 375 Mhz POWER3 64-bit • Winterhawk II Processor Cards • 258 MB Memory (1 GB total) • 2 x 9.1 GB Ultra-SCSI Disk Drives • 10/100 Mbit Ethernet Adapter • Gigabit Ethernet Card

  19. Multiple Instruction Stream Multiple Data Stream Model MIMD Processing

  20. Array Processing

  21. Threads

  22. Example - MPI Message Passing Model

  23. Data Parallel Model

  24. Domain Decomposition

  25. Domain Decomposition

  26. Functional Decomposition

  27. Inter-Process Communication

  28. Load Balancing

  29. Monte Carlo Method

  30. Heat Equation

  31. Heat Equation

  32. Conclusion • Future Workshops • Feb. 13, 2001: Parallel Prog. • Workshop • Feb 24, 2001: AC3 Workshop • Feb. 26-27, 2001: IBM • Workshop • - Visualization using Open DX • - Atlantic Canada High Performance Computing Workshop • HPCS’2001 at Windsor, ON • June 18-20, 2001

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