### CS595: Advanced Scientific Computing, fall 2014 (Wedn. 6:25-9:05pm, SB 201)

** 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**: Wedn. 3:30 - 5:15pm, SB 235C

** 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:**

- "Implementation and parallelization of grid-matched astrometry", M. Otten, A. Seymour and M. Warren, 2012
- "Developing Efficient Linked List Operations in the PETSc Library", Surtain Han, 2012
- "Simulation of Microstructures on Parallel Machine Using PETSc", A. K. Barue, 2010
- "A Study on the Computational Scaling of LMI Constrained Optimization", B. Nicholson, 2010
- "Three Phase Instantaneous Time Domain Simulation of Electiric Power Systems Using PETSc", S. Abhyankar, 2008
- "Build a Low Cost Parallel Computing Cluster", N. Johnston and M. McCourt, 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
- Scientific Computing, An Introductory Survey, 2nd Edition, by Michael T. Heath
- Iterative Methods for Sparse Linear Systems, by Yousef Saad
- 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 3.5, Argonne National Laboratory, 2014, by S. Balay et al.
- Unix Tools http://cs2042.thefutureofmath.com
- Design Patterns, by E. Gamma, R. Helm, R. Johnson and J. Vlissides
- Introduction to Parallel Computing https://computing.llnl.gov/tutorials/parallel_comp/
- Introduction to High Performance Scientific Computing, by Victor Eijkhout http://www.tacc.utexas.edu/~eijkhout/istc/istc.html

Lecture Notes and Assignments: blackboard.iit.edu

** Advising Philosophy: click here**