PyHPC 2015 Keynote
Detailed computational models, massively parallelized calculations, and enormously collaborative simulation projects are increasingly integral to the advancement of science. However, the caliber of this work is limited by a workforce lacking formal training in essential software development skills. To address this unmet need, a number of initiatives (e.g. Software Carpentry, Data Carpentry) have developed online resources and led short courses addressing software development best practices such as version control and test driven code development, as well as basic skills such as UNIX mobility. With the exception of the Software Carpentry "Driver's License for High Performance Computing", however, these initiatives stop just shy of parallelization concepts and skills, and their scalability and sustainability is further limited by the volunteer power on which they run.
The challenge at hand will only be sustainably solved when best practices in research-grade scientific computing have penetrated the traditional science and engineering curriculum in universities. This talk will describe a new effort to embed best practices for reproducible, application-focused, research-grade scientific computing into traditional university curriculum. In particular, a set of open source, liberally licensed, IPython (now Jupyter) notebooks are being developed and tested to accompany a book “Effective Computation in Physics: A Field Guide to Research in Python.” These interactive lecture materials lay out in-class exercises for a project-driven university course and are accordingly intended to be forked, modified and reused by professors across universities and disciplines. With Python as a teaching language, this course prepares university students for research at scale by approaching practical scientific computing challenges such as data structures, performant simulation design, hierarchical data storage, parallelization, analysis, and visualization.
Kathryn (Katy) Huff [CV] is currently a postdoctoral scholar with the Nuclear Science and Security Consortium at the University of California Berkeley and a Data Science Fellow with the Berkeley Institute for Data Science. Katy received her Ph.D. in Nuclear Engineering from the University of Wisconsin Madison. She holds a bachelor's degree in Physics from the University of Chicago. She has participated in varied research from nonlinear granular phase dynamics to anisotropies of the cosmic microwave background. She was a founder of The Hacker Within scientific computing group in Wisconsin and has been an instructor for Software Carpentry since 2011.
Katy received numerous honors and awards, authored many publications in journals and conference proceedings, and is co-author of the book “Effective Computation in Physics: Field Guide to Research in Python”.