Abstracts
and Reading List
University of Kentucky
July 1-26, 2002
Parallel
Computing and Visualization
Craig
C. Douglas (Departments of Computer Science and Mechanical
Engineering, University of Kentucky, douglas@ccs.uky.edu)
and Jun Zhang (Department
of Computer Science University of Kentucky, jzhang@cs.uky.edu)
A comprehensive introduction to parallel computing with respect
to scientific computing will be given. No knowledge of parallel
computing will be assumed, but some programming language needs
to known by the student (e.g., C, C++, or Fortran). Differences
between single and multiple processor algorithms and strategies
will be given. Communication methods (MPI and OpenMP) and visualization
techniques will be described. Students will have ample access
to the largest Hewlett-Packard supercomputer on the planet.
Reading
List:
Numerical
Linear Algebra for High Performance Computers by J. Dongarra,
I. Duff, D. Sorensen, and H. van der Vorst published by SIAM
in 1998.
Numerical
Methods for Partial Differential Equations
Jerome
Jaffré (INRIA, Jerome.Jaffre@inria.fr)
and Jean Roberts (INRIA,
Jean.Roberts@inria.fr)
Partial
differential equations model a wide variety of physical and
socio-economic phenomena. Practical applications require numerical
solutions for these equations. The choice of numerical method
for an equation is crucial and depends strongly on the particular
problem. In this course we will study several methods for different
problems and will be concerned with questions of stability,
precision, and conservation, with an emphasis on criteria for
the choice of a suitable method.
Sparse
Matrix Methods
Iain
Duff (Rutherford Appleton Laboratory, I.Duff@rl.ac.uk)
Underlying
the solution of most problems in science and engineering are
sparse matrices used in either a linear or nonlinear problem
formulation. We will focus on how to solve sparse matrix problems
and will concentrate on examining the use of direct methods
although some mention will be made of how they can be used to
precondition iterative methods. The lectures can be grouped
into three parts which we detail below.
Direct
Methods I
We commence by illustrating the diversity of problems in which
sparse matrices play a crucial role and illustrate the quite
different characteristics of sparse matrices from a number of
application areas. We then discuss basic issues for direct methods
including pivoting for sparsity preservation and stability.
We describe how these can be combined in sparse direct software
and show the effect of resulting algorithms using HSL codes
on realistic examples.
Direct
Methods II
One of the most efficient kernels on any computer, whether a
standard workstation or a supercomputer, is the GEMM Level 3
BLAS for matrix-matrix multiplication. In this lecture, we show
how this kernel that is for dense matrices can be used in a
sparse direct method. In particular, we study frontal methods,
both for finite-element and non finite-element problems. Again
we illustrate our points through examining the performance of
actual codes on a range of test problems from various application
areas. These runs will also be used to illustrate the limitations
of frontal methods which we will address by generalizing the
scheme to using many fronts, resulting in a multifrontal method.
Direct
Methods III
In
this lecture we will develop the multifrontal method further
and indicate the rich variety of possible multifrontal approaches
and their applicability to a wide range of problems and matrix
types. We will also discuss at some length more recent work
on designing sparse direct codes for distributed memory computers,
in particular a parallel multifrontal code developed as part
of an EU LTR Programme.
Reading
List:
Iain
S. Duff and Albert M. Erisman and John K. Reid, "Direct Methods
for Sparse Matrices," Oxford University Press, Oxford, England,
1986, pages xiii + 341, ISBN 0-19-853408-6 (hardcover), LCCN,
QA188 .D841, Bibliography date: Tue Dec 14 22:47:43 1993, 1986,
US price: $37.50
Jack J. Dongarra and Iain S. Duff and Danny C. Sorensen and
Henk A. van der Vorst, "Numerical Linear Algebra for High-Performance
Computers," SIAM Press, Philadelphia, 1998.
RAL reports where most of my recent work (inlcuding several
review articles) can be obtained. http://www.numerical.rl.ac.uk/reports/reports.html
ACTS
Workshop
Tony
Drummond LADrummond@lbl.gov and Osni
Marques (Lawrence Berkeley National Laboratory, osni@nersc.gov)
Students will receive hands on experience using a number of
the Department of Energy's software ACTS Toolkit for parallel
computers. There will be tutorials and discussion sessions focused
on solving specific computational needs of the participants.
See http://acts.nersc.gov for information about the ACTS Toolkit.
Reading
List:
(1) LAPACK, On-line: http://www.netlib.org/lapack/lug/lapack_lug.html
or:
LAPACK Users' Guide, Third Edition, E. Anderson, Z. Bai, C.
Bischof, S. Blackford, J. Demmel, J. Dongarra, J. Du Croz, A.
Greenbaum, S. Hammaring, A. McKenney, and D. Sorensen, SIAM
Publication, 1999.
(2) ScaLAPACK
On-line: http://www.netlib.org/scalapack/slug/index.html
or:
ScaLAPACK Users' Guide, L. S. Blackford, J. Choi, A. Cleary,
E. D'Azevedo, J. Demmel, I. Dhillon, J. Dongarra, S. Hammarling,
G. Henry, A. Petitet, K. Stanley, D. Walker, and R. C. Whaley,
SIAM Publications, Philadelphia, 1997.
(3) On-line:
http://netlib2.cs.utk.edu/linalg/html_templates/Templates.html
or
R. Barrett , M. Berry , T. F. Chan, J. Demmel, J. Donato, J.
Dongarra, V. Eijkhout, R. Pozo, C. Romine, and H. Van der Vorst
, Templates for the Solution of Linear Systems: Building Blocks
for Iterative Methods, 2nd Edition,SIAM, 1994, Philadelphia,
PA
(4) On-line: http://www.cs.utk.edu/%7Edongarra/etemplates/index.html
or:
Templates for the Solution of Algebraic Eigenvalue Problems:
a Practical Guide , Zhaojun Bai, James Demmel, Jack Dongarra,
Axel Ruhe, and Henk van der Vorst, SIAM Publication, 2000.
(5) Numerical Linear Algebra for High-Performance Computers,
Jack J. Dongarra, Iain S. Duff, Danny C. Sorensen, and Henk
A. van der Vorst, SIAM Publication, Philadelphia, 1998.
(6) Computational Science Educational Project Homepage http://csep1.phy.ornl.gov/csep.html
(7) The ACTS Toolkit Information Center: http://acts.nersc.gov
Bioinformatics
and Its Relation to Scientific Computing
Toni
Kazic (University
of Missouri - Columbia, toni@athe.cecs.missouri.edu
)
A
model of a cellular metabolism involves hundreds of uniquely
defined pieces. Changing one can affect many others. The state
of the art for the design of effective alternatives is almost
a trial and error process. Predicting the metabolic fate of
a compound or the metabolic changes produced by an altered enzyme
requires the ability to identify which enzymes will react with
the compound and its successors, and determine the extent to
which other competing processes will bypass or contribute to
the desired effect. The focus here will be on the models and
the computational algorithms for the rational design of cellular
metabolism.
IMA
2002 Summer Program for Graduate Students in Scientific Computing
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