Education and Training Program
An extensive educational and training program in research computing is available on campus led by PICSciE and co-sponsored or supported by key campus partners including the Center for Statistics & Machine Learning, Program in Applied and Computational Mathematics (PACM), the Princeton Neuroscience Institute, OIT Research Computing and various academic departments. The program includes:
Information about the Python, TensorFlow & PyTorch and MATLAB user groups.
View or download materials for Spring 2020 workshops.
Scientific computation requires a working knowledge of numerical analysis, best-practices in software engineering, computer science, and statistics and data modelling. The graduate certificate in computational and information science is open to currently enrolled Princeton University graduate students. It is designed to recognize the achievements of students who have undertaken comprehensive training in these topics, both through formal course work and through research in their subject area.
Throughout the year, Research Computing presents or co-sponsors a variety of presentations by researchers and vendor technical experts.
https://princetonuniversity.github.io/PUparallel_fall2021/sessions/M1B-intro-computer-arch/Princeton Research Computing offers a number of workshops each semester on topics related to programming and commonly used research software. Programming topics focus on using Python, and writing parallel programs in C, C++ and Fortran. Other topics include the Globus data transfer tool, visualization, and version control.
Research Computing provides a variety of tutorials to help researchers get started using our systems. These cover getting access, compiling and running programs, using the SLURM scheduler, GIS software, and using Map/Reduce and Spark. Links are also provided to many other tutorials on programming and on using common software packages.