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Getting Started with the Research Computing Clusters
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Details
Participants will also learn the basic civics of working on Princeton’s shared systems, including guidance on how to request the appropriate computational resources (e.g., number of CPU-cores, CPU memory, GPUs) and rules of thumb to avoid delays or interruptions in computing jobs.
Workshop format: Interactive presentation, with hands-on activities.
Target audience: All users of the Research Computing systems should know the content of this workshop (think of it as “Driver’s Ed” for supercomputers). Some of that content is Princeton-specific, so this workshop is suitable not only for those new to working on shared Linux clusters but also for those with experience at other institutions whose systems and policies might differ from Princeton’s.
Knowledge prerequisites: A working facility with the Linux command line.
Hardware/software prerequisites: For this workshop, users must have an account on the Adroit cluster, and they should confirm that they can SSH into Adroit several hours beforehand. Request an account on Adroit: https://bit.ly/3wicSaH (VPN required if off-campus). Details on all of the above can be found in this guide (https://bit.ly/3QER9Sv).
Learning objectives: Attendees will come away with the basic skills needed to connect to a research computing cluster, navigate its environment and file system, install and manage their software environment, and run programs through the Slurm job scheduler.
Speakers
Carolina Roe-Raymond
Visualization Analyst
Princeton University
Carolina Roe-Raymond is a Visualization Analyst in the Princeton Institute of Computational Science and Engineering (PICSciE) at Princeton University. As a Visualization Analyst, Carolina helps Princeton faculty, staff, and students explore and communicate their data through graphs, charts, and other visuals. Prior to her current position, Carolina created static and interactive data visualization applications for academic research groups. Carolina has a Ph.D. in Resource Ecology and Management, where she used visualizations created in R and GIS programs to advance research in urban bee ecology.
Calla Chennault
Calla Chennault is a Research Software Engineer embedded within the Maxwell Research Group in the Civil and Environmental Engineering department at Princeton University. Here, she contributes to the software development of HydroFrame, a national hydrologic modeling platform, and HydroGEN, a national platform for machine learning-based hydrologic forecasting. Prior to Princeton, Calla worked as a Software Developer at quantPort, a quantitative hedge fund, where she contributed to the development of a quantitative trading and research simulation framework. Calla has a B.S. in Computer Science from Ramapo College of New Jersey.
Anvitha Sudhakar
Anvitha is a graduate student working with Andrej Kosmrlj in MAE.
Hosted By
Co-hosted with: GradFUTURES
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