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Dr. Virendrakumar (Virendra) C. Bhavsar Professor Faculty of Computer Science

Supercomputing. Dr. Virendrakumar (Virendra) C. Bhavsar Professor Faculty of Computer Science University of New Brunswick (UNB) Fredericton, Canada. Definitions Applications Hardware Software Current Status University of New Brunswick Future. Outline. 2. Definitions.

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Dr. Virendrakumar (Virendra) C. Bhavsar Professor Faculty of Computer Science

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  1. Supercomputing Dr. Virendrakumar (Virendra) C. Bhavsar Professor Faculty of Computer Science University of New Brunswick (UNB) Fredericton, Canada

  2. Definitions • Applications • Hardware • Software • Current Status • University of New Brunswick • Future Outline 2

  3. Definitions • Computing • Supercomputing • - A supercomputer is a computer that is at the frontline of current processing capacity, particularly speed of calculation. • High Performance Computing (HPC)/High Productivity Computing • - supercomputing - a subset of HPC • Parallel Computing • many calculations are carried out simultaneously • 10**6 Million, 10**9 Billion, 10**12 Trillion

  4. Human Brain • 10**10 Neurons • 10**4 Fan-in • Wires much slower than chips • Millions of times more volume • 10**14 Inputs (Connection strngths • 10**12 Connection strengths can affect processing in 5 msec • Lower bound on the computational power of brain • ~ 10**10 neurons, 10 spikes/sec, 10**14 connections • ~10**15 operations/sec or 10**18 bits/sec 4

  5. Connection Machine CM-2 • 65K Processors, • 5 CM-2 = 1.8 x 10**13 bits/sec • 10**5 times slower than brain 5

  6. Early Computers 1950: 5,000 operations/sec; 1970-71: 1 Million Operations/sec

  7. Advances in Microprocessor Technology • 1974 - 1 MHz clock • 1988 – 40 MHz • 2002 – 2 GHz • 2009 – P4 3.0 GHz, Quadcore 2.66 MHz • Intel Montecito chip • 1.72 Billion transistors • NVidia 280 series GPU 1.4 Billion transistors • Circuit complexity doubles every 18 months •  Computing power at a given cost doubles every 18 months • - Processor clock rates: 40% increase/year + more instr./cycle • - DRAM Access Times: 10% increase/year  caches required 7

  8. Applications • Grand Challenge Applications • cannot be solved in a reasonable amount of time with today's computers • Environment, Ecosystems, Molecular engineering, cognition, weapon design, Artificial Intelligence, • (near) Real-Time Applications • Military/Defense Applications • Space • Financial Forecasting; Live data (e.g. online stock market data)

  9. Applications • (near) Real-Time Applications • Google • Software as a Service (SaaS) delivery model • ATMs, online banking • Data Intensive Applications • Walmart – inventory management • Data Mining 9

  10. Applications • Computational Modeling and Simulation • Science, Engineering, Social Sciences, … • Parameter sweep applications • Animation and Movies 10

  11. Applications • Compute Intensive Applications • Massive Data applications 11

  12. Applications • Capability Computing • - Using the maximum computing power to solve a large problem in the shortest amount of time • Capacity computing • Using efficient cost-effective computing power to solve • somewhat large problems • - many small problems 12

  13. Supercomputer Design Challenges Cooling Speed of Light Compute Bound Problems  I/O Bound problems 13

  14. Supercomputer Technologies Pipelining and Vector Processing Parallel and Distributed Processing Liquid Cooling Non-Uniform Memory Access Striped Disks (RAID) Parallel File System 14

  15. Parallel and Distributed Algorithms • Intrinsic parallelism • Design of parallel algorithms • Analysis of parallel algorithms 15

  16. Programming PVM and MPI – Loosely connected clusters OpenMP for Shared Memory Machines 16

  17. Automatic Parallelization Compilers Limited success Application Checkpointing 17

  18. Current Supercomputer • Roadrunner applications • National Security • Planet: Earth and Environmental Sciences • e.g. ground water modeling • Health: Biology, Chemistry, Life Sciences • Science: Engineering, Technology • Universe: Astronomy, Space, Astrophysics • - Modeling the decay of the US nuclear arsenal 18

  19. Current Supercomputer • Roadrunner • Los Alamos National Laboratory, Los Alamos, NM, USA • >1 Petaflop (Quadrilion): million billion (10**15) floating-point operations/sec (FLOPS) • 1.71 Petaflop peak • Weight - 500,000 pounds • Power - 4 Mega Watt • Space – 6000 square feet • Cabling 57 miles 19

  20. Current Supercomputer • Roadrunner (Installation Year – 2008) • Los Alamos National Lab, USA • ~ 3,250 compute nodes • Compute Node: Two AMD Opteron dual-core microprocessors • Each of the Opteron core: Internally attached to one of four enhanced Cell microprocessors. • Enhanced Cell: double-precision arithmetic faster and can access more memory than can the original Cell in a PlayStation 3. The entire machine will have almost 13,000 Cells and half as many dual-core Opterons. • Interconnection Network: off-the-shelf Infiniband 20

  21. Current Supercomputer Roadrunner (Installation Year – 2008) DOE/NNSA/LANL System Family - IBM Cluster System Model - BladeCenter QS22 Cluster Computer - BladeCenter QS22/LS21 Cluster, PowerXCell 8i 3.2 Ghz / Opteron DC 1.8 GHz , Voltaire Infiniband Operating System - Linux Interconnect – Infiniband Processor - PowerXCell 8i 3200 MHz (12.8 GFlops) 21

  22. Building blocks – processors, memory, interconnection networks • Processors • Memory – main and secondary storage • Interconnection networks Hardware: Building Blocks 22

  23. Taxonomy: SISD, SIMD, MISD and MIMD • Shared Memory Processing versus Distributed • Memory Processing • Symmetric Multi-Processing (SMP) versus Non-Uniform Memory Access (NUMA) • Processors • Clusters Hardware: Architectures 23

  24. Specially Programmed FPGA chips • Custom VLSI Chips • Reconfigurable Computing • GPUs (Graphics Processing Units) Special Purpose Supercomputers 24

  25. University of New Brunswick 25

  26. High Performance Computing and Networking @ University of New Brunswick

  27. ACEnet: Atlantic Computational Excellence Network “People, Research, Excellence” Hosting sites: Member sites:

  28. ACEnet Atlantic Canada is a distributed environment $30 million initiative Waterways make networking solutions difficult (e.g. Cabot Strait)

  29. ACEnet World-class HPC facilities Behave as a single, regionally distributed “computational power grid” Create and operate sophisticated collaboration facilities to bind together geographically dispersed research communities.

  30. Advaced Computational Research Lab (ACRL) Infrastructure

  31. Major Users UNB Mechanical EngineeringMohammad Bagher AyaniDavid BonhamAndrew GerberMarwan HassanEsam Hussein UNB PhysicsDr. Eugene K HoDr. Zong-Chao YanDr. Li-Hong Xu UNB ForestryEvelyn Richards UNB BiomedicalKevin Englehart DAL PhysicsAndrew Rutenberg MTA ChemistryStacey Wetmore MUN Computer ScienceDwight Kuo Sick Kids Hospital, TorontoRegis PomesChing-Hsing YuLen Zaifman StFX Computer ScienceLaurence Yang UofCalgary Computer SciencePeter TielemanJustin MacCallum UdeM Environmental StudiesYves Gagnon UdeM Computer ScienceJalal Almhana UPEI PhysicsSheldon OppsJames Polson UofT Computer ScienceHue Sun ChanMaria Sabaye Moghaddam UNB BiologyGary Saunders UNB ChemistryScott BrownridgeLarry CalhounGhislain DeslongchampsFriedrich Grein UNB Computer ScienceEric AubanelVirendra BhavsarBrad NickersonRuth Shaw UNB Text Processing CentreAlan BurkDavid Gants UNB GeodesyPetr VanícekRichard Langley UNB MathematicsKeith De’BellAbraham Punnen

  32. ACEnet at UNB Fundy: SUN cluster, AMD Opeteron, 632 cores ACEnet: 3324 cores Internet connectivity > 2Gbps at UNB

  33. Collaboration Grid Collaboration gear across Atlantic Canada Lecture rooms equipped so ACEnet sites can share seminars and participate remotely ACEnet cafés at each site sharing continuous video feeds Desktop level collaboration equipment for personal communication Access Grid streams tens to hundreds of Mbps across the CANARIE network ACEnet

  34. My Research Work Special Purpose computers for Military Applications Design and development of MICRON and PLEXUS Parallel Monte Carlo Algorithms Graphics and Visualization PaGrid Artificial Intelligence – artificial neural networks, e-Business Bioinformatics – Canadian Potato Genome project

  35. Future IBM Cyclops64 – supercomputer on a chip C-DAC initiative for 2010 –petaflop machine NCSA, USA 2011 petaflop machine NASA, SGI and Intel Pleiades – 10 petaflop by 2012 1 Exaflop (10**18 flops) by 2019 Human brain neural simulations – 10 exaflop by 2025 2-week Full Weather modeling – 1 zeta flops (10**21 flops) by 2030

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