Course Syllabus

Abstract

This course is the graduate level introductory course in the discipline of Computational Science (the computer simulation of natural systems). This course is designed to teach the basic tools from mathematics and computer science that are needed to give computational solutions to scientific and engineering problems. Topics may include matrix methods, random numbers and Monte Carlo methods, numerical methods for ordinary and partial differential equations, and optimization techniques.

Prerequites: Computer programming, calculus and basic linear algebra. Also, having some background in computer architecture would be helpful.

Modules:

• Introduction to High Performance Computing
• Parallel Computer Architectures
• Parallel Programming Models and Software
  • SPMD (Single Program Multiple Data) using the Message Passing Interface (MPI) with the languages Fortran, C, and Java
  • Data Parallel Programming using Fortran90 and High Performance Fortran (HPF)
  • Scientific Visualization using Java applets
• Algorithms and Software for Mini-applications
  • Numerical integration including adapative methods
  • Floating point arithmetic
  • Full matrix algebra
  • Random numbers and Monte Carlo methods
  • Ordinary Differential Equations
  • Partial Differential Equations

For this course, students will gain experience programming using the MPI parallel programming packages. This will be done first in either Fortran or C, and then also explored in Java.

Northeast Parallel Architectures Center