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Supercomputing and Sciences Rong Ge Marquette University.

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Presentation on theme: "Supercomputing and Sciences Rong Ge Marquette University."— Presentation transcript:

1 Supercomputing and Sciences Rong Ge Marquette University

2  Supercomputing in plain English  Personal computers and limited capability  Supercomputers for solving scientific problems  Supercomputing and speed  Supercomputing for high school students  Why should HS students care  Supercomputing for HS in the country Roadmap

3 Personal Computer Output device Input device Network cable

4 Opening the Box

5  Processor: control and ALU  Memory  Input  Output  Like human organs Five Classic Components

6  Processor: number cruncher  Speed: 2GHz-4GHz?  Duo core or quad core?  Memory: data storage  8GB?  These hardware parameters largely determine how fast a computer is. Typical PC Configurations

7  Are Long to compute  Need large quantity of memory  large quantity of runs  Are Time Critical Not All Programs can Run on PC

8 Slide 8 Example 2: Fluid dynamics calculations (1000  1000  1000 lattice) 10 9 lattice points  1000 FLOP/point  time steps = FLOP Example 3: Monte Carlo simulation of nuclear reactor particles to track (for 1000 escapes)  10 4 FLOP/particle = FLOP Decentralized supercomputing ( from Mathworld News, 2006/4/7 ): Grid of tens of thousands networked computers discovers – 1, the 43 rd Mersenne prime, as the largest known prime ( digits ) Example 1: Southern oceans heat Modeling (10-minute iterations) 300 GFLOP per iteration  iterations per 6 yrs = FLOP 4096 E-W regions 1024 N-S regions 12 layers in depth Exemplar Programs

9  Physics and Astrophysics  Biophysics  Geophysics and Earth imaging  Medical Physics and Medicine  Chemistry and Biochemistry  Chemical and nuclear reactions  Weather and climate  Mechanical devices - from prosthetics to spacecraft  Manufacturing processes Traditional Scientific and Engineering Problems

10  Top 1 in June 2012  Speed: operations per second today  Big: 4500 square feet Supercomputers

11 Supercomputers in the Past Source: Jack Dongarra

12 Source: Supercomputing in Plain English: Overview by Neeman at OU 12 Parallelism for Speed Less fish … More fish! Parallelism means doing multiple things at the same time: you can get more work done in the same time.

13  Jigsaw analogy  Person: CPU  Jigsaw pieces: data in memory  One person  Serial computing, one hour  Two persons  Parallel computing, about a half hour  Four persons  A little more than a quarter hour  Eight persons  ? 13 Diminishing Returns Source: Supercomputing in Plain English: Overview by Neeman at OU 1000 jigsaw pieces

14  Two person, each having on his own table with half of the puzzle pieces  Two persons can work completely independently, without any contention for a shared resource.  BUT, they need Same number of pieces first – workload decomposition and balance Communication, which is costly Supercomputing in Plain English: Overview Tue Jan Distributed Parallelism & Overhead

15  Supercomputing in plain English  Personal computers and limited capability  Supercomputers for solving scientific problems  Supercomputing and speed  Supercomputing for high school students  Why should HS students care  Supercomputing for HS in the country Roadmap

16  Tomorrow’s PCs may be today’s supercomputers During the past 10 years, the trends indicated by ever faster networks, distributed systems, and multi- processor computer architectures (even at the desktop level) clearly show that parallelism is the future of computing. Why Should We or Our Students Care Reason I

17 Slide 17 CPU Performance The exponential growth of microprocessor performance, known as Moore’s Law, shown over the past two decades (extrapolated).

18 Slide 18 CPU Speed Projection in 2001 From the 2001 edition of the roadmap [Alla02] Calendar year  Halfpitch (nm) Clock freq. (GHz) Wiring levels78910 Power supply (V) Max. power (W)

19 The Truth Microprocessor speed stops increasing around 2003 due to physical difficulties

20  Multiple, slow cores on a chip  Intel  Up to 80 cores  AMD  Integrated CPU and GPU cores (50+ cores)  nVidia  Hundreds of GPU cores  Parallel computing is required to achieve fast execution for a single program 20 The Resulting Multicore Processors

21 1.Thousand years ago – experimental Science  Description of natural phenomena 2.Last few hundred years – Theoretical Science  Newton’s Laws, Maxwell’s Equation 3.Last few decades – Computational Science  Simulation of complex phenomena 4.Today – Data intensive Science  Scientists overwhelmed with data sets Reason II – Scientific Approaches

22  Need to solve grand challenge problems with supercomputing  Disaster preparedness  Climate change  Clean energy  National security and defense Reason III: The Burden of Next Generation Scientists

23 Particle Physics

24 Swine Flu – Pandemic Flu Simulation

25  NSF and DOE  National supercomputing centers  NCSA at UIUC  San Diego supercomputer center  the National Center for Supercomputing Applications  Technical supercomputing conferences  IEEE/ACM Supercomputing  XSEDE conference  Industry  Intel Brings Parallel Computing to High School Supercomputing for HS Programs

26 Supercomputing Organizations

27  Marquette University  Several computer clusters  Guest accounts available  Condor pool  Technical help  SeWhip: Southeast Wisconsin high performance computing Local Resources

28  https://www.xsede.org/web/xup/online-training https://www.xsede.org/web/xup/online-training    https://www.xsede.org/web/xsede12/students https://www.xsede.org/web/xsede12/students   Online Training Opportunities

29 Thank you


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