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Options for Processing Big Data for the Social Sciences and Humanities
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Details
This workshop begins by covering the relevant computer terminology and concepts. We then give an overview of the computing options at Princeton with a focus on the systems provided by Research Computing. Using only a web browser, participants will learn how to work with their files and run programs on a high-performance computing (HPC) cluster called Adroit. More advanced topics will be demonstrated such as running batch jobs using the job scheduler and transferring large files.
Workshop format: Interactive presentation, with hands-on activities.
Target audience: Researchers in the social sciences and humanities with no experience using high-performance computing clusters.
Knowledge prerequisites: Basic computer concepts such as files, directories and the ability to use a web browser (e.g., Chrome, Firefox, Safari). Some knowledge of Python or R would be beneficial.
Hardware/software prerequisites: Participants must have an account on the Adroit cluster at least 48 hours in advance of this workshop. To request an account, complete this form: https://bit.ly/3wicSaH (VPN required if off-campus). Participants will also need a laptop and power cable. The entire workshop can be done using a web browser (e.g., Chrome, Firefox, Safari).
Learning objectives: Attendees will come away with an overview of their computing options at Princeton, a basic understanding of how HPC clusters work, and how to start using HPC clusters at Princeton.
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, The Graduate School
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