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CS61V

CS61V. Overview of High-Performance Computing. Part II. Parallel Programming?. ENIAC, University of Pennsylvania 1946 (http://www.library.upenn.edu/special/gallery/mauchly/jwmintro.html). The Need For Power. Computational Science. Traditional scientific and engineering paradigm

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CS61V

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  1. CS61V Overview of High-Performance Computing Part II

  2. Parallel Programming? ENIAC, University of Pennsylvania 1946 (http://www.library.upenn.edu/special/gallery/mauchly/jwmintro.html)

  3. The Need For Power

  4. Computational Science • Traditional scientific and engineering paradigm • Do theory or paper design • Perform experiments or build system • Replacing both by numerical experiments • Real phenomena are too complicated to model by hand • Real experiments are: • too hard, e.g., build large wind tunnels • too expensive, e.g., build a throw-away passenger jet • too slow, e.g., wait for climate or galactic evolution • too dangerous, e.g., weapons, drug design

  5. Computational Science Examples • Astrophysical thermonuclear flashes • Nuclear weapons • Weather prediction • Climate and atmospheric modeling • Drug design • Blood flow • Fluid dynamics (CFD)

  6. Fluid Dynamics • Hairpin vortex generation • Forced convective heat transfer • Buoyant convection • Rayleigh-Taylor instability

  7. Hairpin Vortices - Transition to Turbulence • Boundary layer flow past a hemispherical roughness element • Re=200-2000 based on hemisphere height • K=512-8168 spectral elements of polynomial degree N=7-15

  8. Simulation Cost • Cost is O(Re3) • Re=1K simulation ~ 1 week on 512 processors of ASCI Red • 50GF, 64 GB • Re=10K ~ 1 year on all 8192 processors of ASCI Red • 800GF, 1TB • We’re really interested in Re=1M … • Can’t even think of doing the Re=1K problem on a uniprocessor machine let alone the 10K or 1M problems!

  9. The Necessity of Parallel Computing

  10. How fast can a serial computer be? • Consider the 1 Tflop sequential machine • data must travel some distance, r, to get from memory to CPU • to get 1 data element per cycle, this means 1012 times per second at the speed of light, c = 3e8 m/s • r < c/1012 = 0.3 mm • Now put 1 TB of storage in a .3 mm2 area • each word occupies about 3 Angstroms2, the size of a small atom 1 Tflop 1 TB sequential machine r = .3 mm

  11. Even if we could make it ... • ... it’d be too expensive • Market forces are dictating use of COTS

  12. The Solution ? • Add more workers! • Use a collection of processors and memory modules to work together to solve our problems • Supercomputers, MPPs, Clusters, Beowulfs

  13. Bad News

  14. Still Lots of Work • Decide on and implement an interconnection network for the processors and memory modules • Design and implement system software for the hardware • Devise algorithms and data structures for solving our problems • Divide the algorithms and data structures up into subproblems • Identify the communication that will be needed between the subproblems • Assign subproblems to processors and memory modules

  15. CAD Database Scientific modeling Parallel applications Multipr ogramming Shar ed Message Data Pr ogramming models addr ess passing parallel Compilation Communication abstraction or library User/system boundary Operating systems support Har dwar e/softwar e boundary Communication har dwar e Physical communication medium Modern Layered Framework

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