Funded and Unfunded Research Projects in Scientific Computing in our group

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Funded and Unfunded Research Projects in
Scientific Computingin our group
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Scientific Computing Research at UMD

• One of the strongest groups anywhere
• Distributed across
• (Applied) Mathematics
• Computer Science
• Departments (Physics, Engineering, Meteorology,
  etc.)
• Institutes (ESSIC, UMIACS, IPST, etc.)
• Because of the breadth students often are unaware
  of opportunities
• Research can be more applied (more interesting in
  elucidating the science) or more fundamental
  (exploring analysis, or algorithms)

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Applied Mathematics and Scientific Compuing
Faculty in Computer Science doing Scientific
Computing

• Ramani Duraiswami
• Howard Elman

• Dianne OLeary
• Pete Stewart

Faculty in Mathematics doing Scientific Computing

• John Osborn
• Ricardo Nochetto
• Tobias von Petersdorff
• Radu Balan

• Eitan Tadmor
• Jian-Guo Liu
• Eitan Tadmor
• Doron Levy

Other Faculty doing Scientific Computing

• Nail A. Gumerov, UMIACS
• Bill Dorland, Physics/IREAP/CSCAMM
• .

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Recommendation

• Explore research opportunities that are of
  interest to you from all areas
• Several considerations
• Interests, advisor, funding
• My goal today bring to your attention some
  projects that need graduate students
• Briefly talk about these, and invite you to meet
  me/others to discuss problems further if you are
  interested

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Research Areas

• Fast algorithms for acoustical and
  electromagnetic scattering
• Computational Machine Learning
• Parallel Algorithms on Graphical Processors
• Plasma Simulation
• Tokamak
• Space Plasma Simulation
• Numerical Weather Prediction

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Gamer Power
Sony Playstation 3 2.18 teraflops lt500 Difficult
to program
Microsoft X-Box 360 1.04 teraflops lt500 Difficul
t to program
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Multicore Intel box with 3 GPUs in Slots 1
Teraflop for lt 3000 (shown with 1 GPU)
GEFORCE 8880 GTX
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Why are GPUs fast?

• Multicore stream processing
• Successor to SIMD ? SPMD
• Single program multiple data
• Stream of data, same short kernel program runs
  on them
• Extremely large market sensitive to price. Wants
  performance
• Gaming and to a smaller extent personal computing
• Standardization
• GPU programs execute well defined tasks
  (shaders) which are in OpenGL and DirectX gt
  special purpose architecture
• Piggyback on the Moores law revolution
• Faster memory and smaller die sizes
• A generation behind Intel/AMD (e.g., 90 nm vs. 45
  nm), so they are likely to continue to speed up
  in the short term
• Distinguish GPUs from other similar technologies
• Coprocessors, FPGAs, etc.
• Purpose built for smaller markets --- so likely
  more expensive

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New parallel revolution?

• Been there, done that
• Architecture based parallel machines
• Connection Machines, BBN Butterfly, CDC, SGI,
• After a few years became impressive doorstops and
  landfill material at national labs
• So, current trend is towards cluster computing
• Use COTS processors
• But GPU is architecture based
• However it is commodity
• 3 million NVIDIA G80 series with 128 processors
  sold
• Total connection machine market for CM5 700
  machines

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General Purpose GPU Computing

• Use GPUs to do something other than
  graphics/games
• First Wave of GPGPU (till early 2006)
• Approach Fool GPU in to thinking it is doing
  graphics by converting general purpose
  calculation in to graphics metaphores
• Several successes and impressive speedups
• But programming GPUs was more curiosity
• Scientists found it hard to learn and properly
  use OpenGL, CG
• Second generation of GPGPU (2006-present)
• Lead by graphics board manufacturers who see a
  new market
• AMD/ATI NVIDIA have a graphics duopoly
• ATIs GPGPU effort is called Close-to-the-metal
• Provides assembly type instructions to be
  captured by a 3rd party compiler
• NVIDIAs Compute Unified Device Architecture

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Programming on the GPU

• GPU organized as 16 groups of multiprocessors (8
  relatively slow 100 MHz processors) with small
  amount of own memory and access to common shared
  memory
• Factor of 100s difference in speed as one goes up
  the memory hierarchy
• To achieve gains problems must fit the SPMD
  paradigm and manage memory
• Caveat single precision only till Q4-2007
• Fortunately many practically important tasks do
  map well and we are working on converting others
• Image and Audio Processing
• Some types of linear algebra cores
• Many machine learning algorithms
• Research issues
• Identifying important tasks and mapping them to
  the architecture
• Making it convenient for programmers to call GPU
  code from host code

Local memory 50kB
GPU shared memory1GB
Host memory2-32 GB
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Simulating Acoustic and Electromagnetic scattering

• Research in simulating acoustic scattering is
  related to human hearing

• Human perception of a source location is aided by
  our modification of the received sound depending
  on direction of sound

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HRTFs are very individual

• Humans have different sizes and shapes
• Ear shapes are very individual as well
• Before fingerprints, Alphonse Bertillon used a
  system of identification of criminals that
  included 11 measurements of the ear
• Even today ear shots are part of
• Mugshots INS photographs
• If ear shapes and body sizes are different
• Properties of scattered wave are different
• HRTFs will be very individual
• Need individual HRTFs for creating virtual audio

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HRTFs can be computed
Wave equation
Fourier Transform from Time to Frequency Domain
Helmholtz equation
Boundary conditions
Sound-hard boundaries
Sound-soft boundaries
Impedance conditions
Sommerfeld radiation condition
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Idea for rapidly obtaining individual HRTFs

• Discretize equation using surface meshes of
  individuals
• Obtain these via computer vision
• Basis for an NSF ITR award in 2000

Boundary Integral Formulations
Discretization
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Papers

• Nail A. Gumerov and Ramani Duraiswami. Fast
  Multipole Methods for the Helmholtz Equation in
  Three Dimensions. The Elsevier Electromagnetism
  Series. Elsevier Science, Amsterdam, 2005. ISBN
  0080443710.
• Nail A. Gumerov and Ramani Duraiswami. Fast
  multipole methods on graphical processors.
  Submitted, 2008.
• Nail A. Gumerov and Ramani Duraiswami. Fast
  radial basis function interpolation via
  preconditioned Krylov iteration. SIAM Journal on
  Scientific Computing, 2918761899, 2007.
• Zhenyu Zhang, Isaak D. Mayergoyz, Nail A.
  Gumerov, and Ramani Duraiswami. Numerical
  analysis of plasmon resonances in nanoparticles
  based on fast multipole method. IEEE Transactions
  on Magnetics, 4314651468, April 2007.
• Ramani Duraiswami, Dmitry N. Zotkin, and Nail A.
  Gumerov. Fast evaluation of the room transfer
  function using multipole expansion. IEEE
  Transactions on Speech and Audio Processing,
  15565 576, 2007.

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• Nail A. Gumerov and Ramani Duraiswami. A scalar
  potential formulation and translation theory for
  the time-harmonic Maxwell equations. Journal of
  Computational Physics, 225206236, 2007.
• Nail A. Gumerov and Ramani Duraiswami. Fast
  multipole method for the biharmonic equation in
  three dimensions. Journal of Computational
  Physics, 215(1)363383, Jun 2006.
• Nail A. Gumerov and Ramani Duraiswami.
  Computation of scattering from clusters of
  spheres using the fast multipole method. The
  Journal of the Acoustical Society of America,
  117(4)17441761, 2005.
• Nail A. Gumerov and Ramani Duraiswami. Recursions
  for the computation of multipole translation and
  rotation coefficients for the 3-D Helmholtz
  equation. SIAM Journal on Scientific Computing,
  25(4)13441381, 2003.
• Nail A. Gumerov and Ramani Duraiswami.
  Computation of scattering from N spheres using
  multipole reexpansion. The Journal of the
  Acoustical Society of America, 112(6)26882701,
  2002.

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CURRENT RESEARCH ISSUES

• Creation of good meshes for scattering problems
• Use of graphical processors
• Redesigning algorithms for data-parallel and
  cluster architectures
• High frequency acoustic/electromagnetic
  simulations
• Funding several proposals applied for

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Numerical Weather/Disease Forecasting

• University is a center for Earth Systems
  Science
• National Oceanic and Atmospheric Administration
  is moving on campus
• ESSIC, Geography, Applied Math, Computer Science,
  Physics, etc. all have faculty working on such
  problems
• Climate Change is one of the biggest challenges
  facing humanity

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Goals

• Develop/Use local models of climate
• Predict behavior of associated quantities
• Cholera, other disease pathogens
• Sea Nettles,
• Predict extreme events and their effects
• Storm Surges, Cyclones, etc

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Approach

• Develop validate models
• Models are a collection of
• equations (Navier-Stokes, Energy conservation)
• Historical data (observations)
• current observations
• Forecasts and Predictions need to assimilate data
• Model Uncertainty in the predictions

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Faculty team

• Raghu Murtugudde, ESSIC and Meteorology
• Rita Colwell, CBCB and UMIACS
• Ramani Duraiswami, CS
• Nail Gumerov, UMIACS

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Goals

• Use GPUs to aid forecasting
• Employ methods for modeling uncertainty that are
  being developed in machine learning for problems
  in weather (and vice versa)
• Gaussian process regression
• Ensemble Kalman filters
• Funding available for the next 18 months, and
  likely in the future

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Simulating plasma

• Fusion limitless cheap and clean power
• Problem very hard to confine and compress
  hydrogen and cause it to fuse and release energy
• Lots of fluid mechanical instabilities
• Confine plasma
• Big business in Physics around the world
• Problem whose solution is always 50 years in the
  future )

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Simulations Experiments

• UMD again is a leader
• Numerical simulation folks include Prof. Bill
  Dorland
• Collaborations between his group and mine
• Fast and accurate simulation of plasma
• Use GPUs/FMM/ GPU clusters
• Funding several proposals pending, and some
  funding available over the next 4 years.

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Space plasmas

• Work with Prof. Papadapoulos of Astronomy and
  Prof. Gumerov
• Space is almost entirely plasma
• Satellites float in space in this plasma
• If plasma is disrupted so is communication, GPS
• Large five year project to simulate what happens
  when there is a disturbance in plasma (e.g. via
  natural means or nuclear explosions)
• Physics and Numerical simulation