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

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Presentation on theme: "1 Dr. Virendrakumar (Virendra) C. Bhavsar Professor Faculty of Computer Science University of New Brunswick (UNB) Fredericton, Canada Supercomputing."— Presentation transcript:

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

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

3 3 Computing Supercomputing - A supercomputer is a computer that is at the frontline of current processing capacity, particularly speed of 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 Definitions

4 4 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 Human Brain

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

6 Early Computers 1950: 5,000 operations/sec; : 1 Million Operations/sec

7 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 Advances in Microprocessor Technology

8 8 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) Applications

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

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

11 11 Compute Intensive Applications Massive Data applications Applications

12 12 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 Applications

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

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

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

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

17 17 Compilers Limited success Automatic Parallelization Application Checkpointing

18 18 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 Current Supercomputer

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

20 20 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 Current Supercomputer

21 21 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 – InfinibandInfiniband Processor - PowerXCell 8i 3200 MHz (12.8 GFlops) Current Supercomputer

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

23 23 Hardware: Architectures 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

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

25 25 University of New Brunswick

26 High Performance Computing and University of New Brunswick

27 “People, Research, Excellence” ACEnet: Atlantic Computational Excellence Network 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 UNB Biology Gary Saunders UNB Chemistry Scott Brownridge Larry Calhoun Ghislain Deslongchamps Friedrich Grein UNB Computer Science Eric Aubanel Virendra Bhavsar Brad Nickerson Ruth Shaw UNB Text Processing Centre Alan Burk David Gants UNB Geodesy Petr Vanícek Richard Langley UNB Mathematics Keith De’Bell Abraham Punnen UNB Mechanical Engineering Mohammad Bagher Ayani David Bonham Andrew Gerber Marwan Hassan Esam Hussein UNB Physics Dr. Eugene K Ho Dr. Zong-Chao Yan Dr. Li-Hong Xu UNB Forestry Evelyn Richards UNB Biomedical Kevin Englehart DAL Physics Andrew Rutenberg MTA Chemistry Stacey Wetmore MUN Computer Science Dwight Kuo Sick Kids Hospital, Toronto Regis Pomes Ching-Hsing Yu Len Zaifman StFX Computer Science Laurence Yang UofCalgary Computer Science Peter Tieleman Justin MacCallum UdeM Environmental Studies Yves Gagnon UdeM Computer Science Jalal Almhana UPEI Physics Sheldon Opps James Polson UofT Computer Science Hue Sun Chan Maria Sabaye Moghaddam Major Users

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


34 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

35 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

36 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 Exaflop (10**18 flops) by 2019 Human brain neural simulations – 10 exaflop by week Full Weather modeling – 1 zeta flops (10**21 flops) by 2030

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