Poster #1
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An algorithm for the infinity-norm fixed point problem
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Presenter:
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Spencer Shellman
School of Computing,
University of Utah
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E-mail:
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shellman@cs.utah.edu
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Abstract:
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A new algorithm has been developed for solving the fixed
point problem f(x)=x for functions that are Lipschitz continuous with
constant 1 with respect to the infinity norm. Computed results
satisfy the residual error criterion in the infinity norm. The
worst-case complexity is less than exponential in the dimension and
error tolerance.
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Coauthors:
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Dr. Krzyzstof Sikorski, School of Computing, University of Utah
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Poster #2
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Adaptive Wavelet Techniques for the Numerical Solution of
Nonlinear evolutionary PDE: Optimum Wavelet Selection based on Wigner
Phase Space Representations of Multiresolution Analysis
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Presenter:
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Dr. Bedros Afeyan
Polymath Research, Inc
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E-mail:
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bedros@polymath-usa.com
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Abstract:
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WRMR analysis, or the Wigner Representation of
Multiresolution analysis in phase space, is a useful technique by
which to make a choice between wavelet families for optimal
representations in certain function spaces. The WRMR technique will be
demonstrated with band limited functions, solutions to the randomly
driven Burgers equation and generalized nonlinear schrodinger
equations using Mathematica Wavelet Explorer based routines developed
at PRI. Adaptive methods of solving NL PDEs using these techniques
will be discussed.
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Poster #3
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Cancelled
Object-Oriented Framework for Large Scale Air Pollution Modeling
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Presenter:
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Anton Antonov
Wolfram Research, Inc
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E-mail:
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antonov@wolfram.com
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Abstract:
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The object-oriented methodology of patterns and pattern
languages was applied to construct the object-oriented version of the
large scale air pollution model known as the Danish Eulerian Model
(DEM). The obtained framework is amenable to resolve new computational
tasks and to be extended with new numerical methods. In the poster
will be described the general design of the object-oriented DEM and
the design of the different layers. Results from tests and experiments
will be presented.
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Poster #4
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Automatic Performance Tuning of Sparse Matrix Kernels
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Presenter:
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Richard Vuduc
UC, Berkeley
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E-mail:
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richie@cs.berkeley.edu
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Abstract:
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We consider automatic performance tuning of the sparse
matrix kernels that dominate scientific and engineering computations.
Traditionally, such kernels are tuned by hand for a specific target
architecture and matrix. We adopt an automated methodology in which we
identify and generate a space of algorithms, and then search for the
fastest implementation using both run-time experiments and performance
models. We present recent results for sparse matrix-vector multiply,
triangular solve, and AtA on various hardware platforms.
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Coauthors:
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James Demmel, Katherine Yelick, U.C. Berkeley
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Poster #5
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A survey of Levinson-type methods for Toeplitz systems
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Presenter:
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Aaron Melman
St. Mary's College
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E-mail:
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melman@stmarys-ca.edu
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Abstract:
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We present a survey of Levinson-type methods for the
solution of strongly nonsingular symmetric Toeplitz systems.
Symmetric Toeplitz matrices exhibit an even-odd structure which, until
fairly recently, was not fully exploited in Levinson-type algorithms.
We will concentrate on the new algorithms which take advantage of this
structure, leading to a decrease in complexity and an increase in
parallelism.
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Poster #6
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Parallel Bipartite Matching for Sparse Matrix Computation
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Presenter:
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Jason Riedy
UC, Berkeley
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E-mail:
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ejr@cs.berkeley.edu
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Abstract:
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Graph algorithms are notoriously difficult to parallelize,
often because of sequential walks across vertices. Re-stating graph
problems as matrix optimization problems can lead to parallel
algorithms. One example is finding a maximum weight matching in a
bipartite graph for pre-pivoting a sparse matrix. Augmenting path
algorithms are inherently sequential, while optimization-based auction
algorithms allow many parallelizations. We present distributed
implementations of the auction algorithm and discuss how variations in
implementation and problem structure affect performance.
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Poster #7
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A numerical method for solving the Bidomain equations in a
human heart
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Presenter:
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Miguel A. Dumett
Lawrence Livermore National Laboratory
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E-mail:
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dumett1@llnl.gov
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Abstract:
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The Bidomain equations (a system of an elliptic and a
parabolic PDEs) are used to model the electrophysiology
of the human heart. We present a second order stable
generalization of the immersed interface method to solve
an anisotropic elliptic PDE on an irregular domain in 2D
and 3D and discuss certain extensions. Results show
that the discretization matrix obtained is very stable,
although accuracy of these methods are affected for the
presence of anisotropy.
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Poster #8
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Towards Fermion Monte Carlo
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Presenter:
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Leonardo Colletti
Center for Applied Scientific Computing,
Lawrence Livermore National Laboratory
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E-mail:
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colletti1@llnl.gov
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Abstract:
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Among the techniques allowing the calculation of
features of a quantum system, Monte Carlo methods are particularly
interesting since their capability to lead to the solution of these
non-probabilistic problems by means of probabilistic procedures. For
bosonic systems they represent the benchmark for other methods, but
for many-fermions systems they suffer for the "sign-problem", which
arises right from the quantum nature of fermions and represents a
fundamental challenge for physics. Methods are today in progress in
order to overcome this limit.
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Poster #9
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Stabilizing MINOS
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Presenter:
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Michael P. Friedlander
Systems Optimization Laboratory (SOL),
Stanford University
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E-mail:
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mpf@stanford.edu
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Abstract:
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For problems with nonlinear constraints, linearly
constrained Lagrangian (LCL) methods sequentially minimize an
augmented Lagrangian subject to linearized constraints. Convergence
is rapid near aa solution (as proved by Robinson and often observed
with MINOS). To induce global convergence, we propose a modified
algorithm that also provides for infeasible subproblems and inexact
subproblem solutions.
Our Matlab LCL implementation is efficient on large problems, using
SNOPT as the subproblem solver. We present numerical results on CUTE
and COPS test problems.
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Coauthors:
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Michael Saunders, SOL, Stanford University
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Poster #10
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SUGAR: A system-level simulator for MEMS devices
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Presenter:
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David Bindel
Polymath Research, Inc
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E-mail:
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dbindel@cs.berkeley.edu
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Abstract:
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Designers of micro-electromechanical systems (MEMS) use a
wealth of integrated circuit fabrication techniques, but little of the
IC simulation technology. To fill this gap, we have designed the
SUGAR simulator, which takes its name and heritage from SPICE. We
discuss using SUGAR from Matlab or the Web, user interfaces to SUGAR,
extending of the system in C or Matlab, and comparing simulation of a
MEMS mirror to measured data.
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Coauthors:
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Jason Clark, UC Berkeley, applied physics
David Garmire, UC Berkeley, computer science
Babak Jamshidi, UC Berkeley, civil engineering
Raffi Kamalian, UC Berkeley, mechanical engineering
Shyam Lakshmin, UC Berkeley, computer science
Jiawang Nie, UC Berkeley, math
Ningning Zhou, UC Berkeley, mechanical engineering
Wayne Kao, UC Berkeley, computer science
Ernest Zhu, UC Berkeley, computer science
Andy Kuo, University of Michigan, electrical engineering
Alice Agogino, UC Berkeley, mechanical engineering
Zhaojun Bai, UC Davis, math and computer science
James Demmel, UC Berkeley, math and computer science
Sanjay Govindjee, UC Berkeley, civil engineering
Ming Gu, UC Berkeley, math
Kristofer S.J. Pister, UC Berkeley, electrical engineering
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Poster #11
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Image Segmentation Using Normalized Cut
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Presenter:
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Monika Jankun-Kelly
University of California, Davis
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E-mail:
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jankunm@cs.ucdavis.edu
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Abstract:
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Image segmentation partitions an image into self-similar
segments. The image is represented by a graph, with nodes replacing
pixels, and edge weights measuring interpixel similarity. A cut
partitions this graph into subgraphs. The normalized cut (Ncut)
balances high intersubgraph difference and high intrasubgraph
similarity. The best Ncut is found through minimization, which leads
to an eigenproblem. This poster presents interesting features of the
Ncut segmentation method, structure and properties of the associated
eigenproblem, and challenges to overcome.
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Poster #12
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Parallel Multistep Sparse Approximate Inverse Preconditioning
Strategies for General Sparse Matrices
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Presenter:
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Jun Zhang
Laboratory for High Performance Scientific Computing and Computer Simulation,
Department of Computer Science,
University of Kentucky
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E-mail:
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jzhang@cs.uky.edu
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Abstract:
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We develop new concepts and parallel algorithms of multistep
successive preconditioning strategies to enhance efficiency
and robustness of standard sparse approximate inverse preconditioning
techniques. The key idea is to compute a series of simple sparse
matrices to approximate the inverse of the original matrix. Studies
are conducted to show the advantages of such an approach in terms
of both improving preconditioning accuracy and reducing computational
cost. Numerical experiments using one prototype implementation to
solve a few general sparse matrices on a distributed memory
parallel computer are reported.
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Coauthors:
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Kai Wang, Laboratory for High Performance Scientific Computing and
Computer Simulation, Department of Computer Science, University of Kentucky
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Poster #13
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Sundance: a Rapid-Development Toolkit for Parallel PDE Simulations
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Presenter:
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Kevin Long
Computational Sciences and Mathematics Research,
Sandia National Laboratories
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E-mail:
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krlong@sandia.gov
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Abstract:
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Research in algorithms for PDE solvers and PDE-constrained optimizers
can entail significant startup costs for code development. Sundance
provides a set of high-level components allowing flexibility in
formulation, discretization, and solution of a finite-element
problem. Sundance shields the researcher from tedious bookkeeping code
and facilitates quick exploration of ideas, yet still yields high
performance and parallel capability. Sundance reduces both the startup
time for research and the transition time from research to production
code.
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