CS 595 - Advanced Scientific Computing
Fall 2010
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Contact Information
- Hong Zhang : hzhang@mcs.anl.gov
- Office : SB 235C
- Office Hours : R 3:00-5:30
- Michael McCourt : mccomic@mcs.anl.gov (or mccomic@iit.edu)
- Office : E1 105d
- Office Hours : MW 10:00-1:00, TR 2:00-5:00
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Advanced Scientific Computing (Fall, 2010)
Thur. 6:25-9:05pm, SB 220
Instructor: Hong Zhang,
Research Professor, Department of Computer Science, IIT.
Email:
hzhang@mcs.anl.gov
Web Page: www.mcs.anl.gov/~hzhang/teach/cs595
Office Hours and Location: R 3:30 - 5:30pm, SB 235C
Instructor: Michael McCourt,
Adjunct Faculty, Department of Applied Mathematics, IIT.
Email:
mccomic@mcs.anl.gov (or mccomic@iit.edu)
Web Page: math.iit.edu/~mccomic/595
Office Hours and Location: MW 10:00-1:00, TR 2:00 - 5:00pm, E1 105d
Course Description:
This course is designed for graduate and upper-level undergraduate students in the fields of science and engineering.
The objective is to introduce the essential numerical algorithmic ideas and provide programming practice on advanced scientific
computer architecture. The course contains following subjects:
- Overview of parallel computing.
- Parallel and distributed numerical computation.
- Numerical iterative techniques for solving large sparse systems.
- Numerical software design, analysis, implementation and performance evaluation,
including discussions on the object-oriented programming techniques.
Students are expected to gain hands-on numerical programming experience on state-of-the-art parallel computers. By the end of the course,
students are required to apply the algorithms and techniques learned in the class to projects
either in their own field (particularly encouraged) or projects suggested by the instructor. Successful course project may
lead to summer internship at the Argonne National Laboratory.
Selected examples of student project:
- "Three Phase Instantaneous Time Domain Simulation of Electiric Power Systems Using PETSc", S. Abhyankar, 2008
- "Monte Carlo Simulation for High Dimensional Financial Derivatives", B. Niu and Y. Zhang, 2008
- "Build a Low Cost Parallel Computing Cluster", N. Johnston and M. McCourt, 2006
- "Parallel Multigrid Poisson Solver for Applications in Fluid Dynamics and High Energy Physics", M. Boghosian, 2006
Prerequisites:
Advanced calculus, linear algebra, background on numerical computing. Programming skill.
Grading: Homework: 40%, Class
participation: 10%, Final project: 50%.
References:
- Numerical Linear Algebra, by Lloyd N. Trefethen and David Bau, III, SIAM ISBN 0-89871-361-7
- Introduction to Parallel Computing: Design and Analysis of
Algorithms, by Vipin Kuman, Ananth Grama, Anshul Gupta, and George Karypis, 2nd Ed., 2003.
- Iterative Methods for Sparse Linear Systems, by Yousef Saad
- Advances in Software Tools for Scientific Computing, by M. Griebel et al.
- Using MPI: Portable Parallel Programming with the Message-Passing
Interface, by W. Gropp, E. Lusk, and A. Skjellum.
- PETSc Users Manual, http://www.mcs.anl.gov/petsc
ANL-95/11 - Revision 3.1, Argonne National Laboratory, 2010, by S. Balay et al.
- Unix Tools http://cs2042.thefutureofmath.com
Lecture Notes and Assignments: blackboard.iit.edu