Applied Mathematics 483/583
High Performance Scientific Computing
Spring Quarter, 2014
- Professor Randy LeVeque
will be "flipping
the class" this year with the help of Teaching Assistants Scott Moe and
Meghana Velegar (PhD students in Applied Math).
- Students will be required to watch 3 hours of lectures a week
that will be available on the web (videos recorded in Spring, 2013).
These follow the slides
notes from last year, but some new material will also be developed.
- For on-campus students, the class meets T-Th, 2:30 - 3:20 in OUG 136.
This is an Active Learning Classroom
and class time will be used primarily for working together with other
students on exercises designed to illustrate and reinforce the material.
Some additional new material will also be presented.
Attendance will be required at these sessions.
- Undergraduates should enroll in AMath 483 and graduate students in 583. The
lectures and classroom sessions are identical,
but the 583 course will have some additional and/or more advanced assignments.
The class sizes are capped due to the size of the room and additional
students cannot be accomodated.
- Section 583-B is for online Masters degree students only and is
not available to
on-campus students. Students in this section will view the same video
lectures as on-campus students. In addition, parts of the T-Th classroom
sessions will be taped and available to watch, along with the exercises
tackled by students in these sessions.
- Homework and a final project will consist primarily of programming
assignments. There will be no exams.
Introduction to hardware, software, and programming for large-scale
scientific computing. Overview of multicore, cluster, and supercomputer
architectures; procedure and object oriented languages; parallel computing
paradigms and languages; graphics and visualization of large data sets;
validation and verification; and scientific software development.
[More about the class and syllabus]
Experience writing and debugging computer programs is required ---
preferably experience with scientific, mathematical, or statistical
computing, for example in Matlab or R. (Previous knowledge of Fortran,
Python, or parallel computing languages is not assumed.)
Students should also be comfortable with undergraduate mathematics,
particularly calculus and linear algebra, which is pervasive in scientific
computing applications. Many of the examples used in lectures and
assignments will require this background. Past exposure to numerical
analysis is a plus.