PGE 383 - High Performance Computing for Engineers

Instructor: John T. Foster, Ph.D.
Office: PGE 3.108
Phone: 512-471-6972

Class Location: CPE LRC
Class Time: MW 9:00 - 10:30 AM
Office Hours: F 9:00 AM - 10:30 PM and by appointment

Course Website:

Required Text: None


As engineers we often encounter problems too large or too difficult to solve in a conventional manner; therefore, we resort to using the computer to do the hard work for us. These types of problems include problems in computational mechanics, optimization, and statistical analysis. Sometimes, these problems can become so large that the computational expense overwhelms our desktop workstations and we seek out high-performance computing (HPC) resources or HPC clusters to perform the job. These HPC machines are almost always computers that run a UNIX/Linux style operating system and include different parallelization paradigms such as MPI, OpenMPI, OpenCL, CUDA, etc. This course will introduce the student to the UNIX environment in a scientific computing context and include instruction on several import UNIX applications that will make them more productive users. We will also cover, at an introductory level, the differences between these parallelization styles of computing and develop a basic working understanding of how to utilize the application programming interfaces (APIs) in scientific applications.

Outline: Below is a general outline of what I intend to cover in the course. This is subject to change based on the needs and preparation of the students in the class. Any updates will be posted as they occur.

Week Topic Specifics
Jan. 17 Introduction to HPC and UNIX Syllabus, Codecademy, Github, Cloud9
Jan. 22,24 Git and Github
Jan. 29, 31 Introduction to UNIX File system, permissions, regular expressions
Feb. 5,7 Editors vi, emacs
Feb. 12,14 Intermediate UNIX grep, sed, awk, bash scripting, Python scripting for os, customizing environment
Feb. 19,21 Managing Projects building code, git, cmake
Feb. 26,28 Scientific Python numpy, scipy
Mar. 5,7 Scientific Python matplotlib, LaTeX integration
Mar. 12,14 Spring Break N/A
Mar. 19,21 Scientific Python Calling other languages from Python, cffi, SWIG
Mar. 26,28 Parallel Programming MPI, cluster job submission
Apr. 2,4 Parallel Programming MPI, PyTrilinos
Apr. 9,11 Parallel Programming PyTrilinos
Apr. 16,18 Parallel Programming PyTrilinos
Apr. 23,26 Parallel Programming IPython, Jupyter notebook
Apr. 30,May 2 Machine Learning scikit-learn, PyTorch

Additional topics if time allows: Other scientific languages: C/C++


  • Codecademy course completion - 10%
    • This grade will be given entirely based on completing the assigned modules on time.
  • Video lecture quizzes - 20%
  • In-class exercises - 30%
    • These will be exercises performed on the computer, in-class, with the guidance of the instructor. Additional out-of-class time may be required to finish exercises not completed in-class.
  • Assigned projects - 40%
    • These will be longer exercises that may be started or worked on in-class, but will require out-of-class time to complete.

Grade Assignment

Range Grade
>92 A
90-92 A-
88-90 B+
82-88 B
80-82 B-
78-80 C+
72-78 C
70-72 C-
60-70 D
< 60 F

Disability Statement: The University of Austin provides upon request appropriate academic accommodations for qualified students with disabilities. For more information, contact the Office of the Dean of Students at 512-471-6259 or see for more information.