CS595-01: Advanced Scientific Computing, fall 2018 (Thursday 6:25-9:05pm, Stuart Building Room 204.)

Instructor: Hong Zhang, Research Professor, Department of Computer Science, IIT.
Email: [email protected]
Web Page: www.mcs.anl.gov/~hzhang/teach/cs595
Office Hours and Location: Thur. 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:

"Application of DMNetwork and TS in Transient Stability Simulator for Power Systems", Bikiran Guha and Jianqiao Huang, 2016
"Hydraulic Transients in Networks", Daniel Adrian Maldonado and Llus Antoni Jimenez Rugama, 2014
"Implementation and parallelization of grid-matched astrometry", M. Otten, A. Seymour and M. Warren, 2012
"Simulation of Microstructures on Parallel Machine Using PETSc", A. K. Barue, 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
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.
Using MPI and Using Advanced MPI
PETSc Users Manual , ANL-95/11 - Revision 3.7, Argonne National Laboratory, 2016, by S. Balay et al.
Design Patterns, by E. Gamma, R. Helm, R. Johnson and J. Vlissides

Lecture Notes and Assignments: blackboard.iit.edu

Advising Philosophy: click here