TACC’s Scientific Computing Curriculum Texas Advanced Computing Center

The Outcome Advanced Computing Knowledge and Skills Number of People Novice Expert 10% User 100% 50%

The Courses Introduction to Scientific and Technical Computing Parallel Computing for Scientists & Engineers Visualization & Data Analysis for Scientists & Engineers Distributed & Grid Computing for Scientists and Engineers

Instructors Bill Barth Research Associate High Performance Computing Jay Boisseau Director Victor Eijkhout High Performance Computing Kent Milfeld Research Associate High Performance Computing Karl Schulz Assistant Director High Performance Computing Ed Walker Manager/Research Associate Distributed & Grid Computing Kelly Gaither Associate Director/Research & Development Visualization & Data Analysis

Guest Instructors Chona Guiang Research Associate High Performance Computing Victor Eijkhout High Performance Computing Jim Brown Department of Computer Sciences Warren Smith Manager Distributed & Grid Computing Eric Roberts Software Developer Distributed & Grid Computing Kelly Gaither Associate Director/Research & Development Visualization & Data Analysis Margaret Murry Research Associate Distributed & Grid Computing Robert van de Geijn Department of Computer Sciences

Why are these courses important? Need Classes that prepare students to use advanced computing resources as they are used in computational, applications-driven research and development are relatively rare in university curricula. Undergraduate Computational Science and Engineering SIAM Working Group on CSE Undergraduate Education, Sep 2006.

Why are these courses important? Career Academic and industry careers depend on a solid and practical foundation for using cyberinfrastructure in research and development.

Why are these courses important? Discovery Current grid computing technologies and the development of integrated cyberinfrastructure open new possibilities for discovery.

Energy: #1 Global Challenge

Nobelity: The Big Picture Movie trailer

Materials Syllabus Course description Instruction materials Assignments, tests, and rubrics References Dr. Kent Milfeld teaching the Parallel Computing course.

Theoretical Outcome Novice Expert User Advanced Computing Knowledge and Skills

Economics Science, Technology & Society Electrical & Computer Engineering Aeromechanics Research Chemical Engineering Center for Space Research Advanced Manufacturing Center Physics Computer Sciences Fusion Studies Mathematics Empirical Outcome

Lessons Learned 90-minute class sessions Maximize contact time and minimize instructors’ preparation time. Students crave guided problem solving Leverage contact time to guide students through computational problem solving.

Lessons Learned Courseware is handy, and can be better utilized Students seldom used the interactive features of BlackBoard. Most effective for class announcements, assignments, and archiving materials & resources. Do a pre-course student survey Don’t assume – cheat and look: what Unix, mathematics, and HPC knowledge & skills do your students have on the first class day?

Lessons Learned PowerPoint slides are not the course content Use it, but don’t abuse it. Code examples (C and Fortran) plus diagrams and logic flowcharts. Images of HPC hardware + flow of data or instructions. Nested, interrelated realms of distributed & grid computing.

Lessons Learned PowerPoint slides are not the course content Use it, but don’t abuse it. Code examples (C and Fortran) plus diagrams and logic flowcharts. Images of HPC hardware + flow of data or instructions. Nested, interrelated realms of distributed & grid computing.

Acknowledgements Thank you, EPIC!

Acknowledgements TeraGrid EOT Working Group Ongoing and prior work to produce HPC courses SUNY Computational Science Engineering Ohio Supercomputing Center EPIC Virtual Institutes: Computational Science Curriculum …many others.