Other Resources
High Performance Computing Infrastructure Beyond Stanford
Campus resources are varied and are growing. They may well meet your needs. But what if you need more compute, need to run larger jobs, need to manipulate more data than local systems can accommodate? The Research Computing team can help link you to national compute resources, such as the OpenScience Grid. Or help you decide whether cloud computing could/should be in your portfolio of computing platforms. Contact us at srcc-support@stanford.edu and let's start the conversation.
Supplemental Training
In addition to providing our own classes and workshops, Stanford Research Computing partners with other Stanford and external organizations to offer a variety of training opportunities around HPC and data technologies, methods, and tools. Some of the areas covered in previous training are CUDA, GPU basics, Python, Introduction to Stanford HPC resources, Introduction to Sherlock, BigData, OpenMP, MPI, and Intel HPC Tools. For more information, contact us at srcc-support@stanford.edu.
You can also try teaching yourself relevant skills with these many resources we think are great:
Learning Linux
Codecademy: Learn the Command Line: Now requires a monthly membership, but a good, interactive introduction to the command line. More "academic" than the games below. Also less fun.
Software Carpentry: The Unix Shell: One of many tutorials offered by the Carpentries, this one covers the basics (and advanced principals) of the Unix shell.
Hacknet: A $10 game with a simplified Unix syntax. Feel like you're in Sneakers! Makes learning the command line fun!
The Bandit Wargame: Free whitehat hacking tutorial that takes place on real servers in your actual terminal. They link to relevant documentation, but it might be a bit much for beginners.
Learning HPC
HPC Carpentry: An Introduction to High Performance Computing: A Carpentry lesson adapted for SRC infrastructure by our own Christina Gancayco!
Higher-level Language Resources
Python
Software Carpentries: Programming with Python
R
Software Carpentries: Programming with R
Software Carpentries: R for Reproducible Scientific Analysis