Efficiently transferring data is a critical part of building research workflows, whether working with experimental or simulated data on local or high-performance computing (HPC) systems. Here, we introduce key concepts and command-line tools for data transfer, including how to verify data integrity, use compression, and select appropriate transfer methods based on data size, location, and organization.
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Interactive high-performance computing (HPC) involves real-time user inputs that result in actions being performed on HPC compute nodes. This session presents an overview of interactive computing tools and methods.
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This tutorial introduces the National Research Platform (NRP) as a unified environment for agentic AI development and large-scale inference on GPUs and next-generation GenAI accelerators. Participants will gain hands-on experience deploying and running models using NVIDIA GPUs and Qualcomm Cloud AI 100 Ultra cards through browser-based tools like JupyterHub. The session emphasizes practical workflows for building and managing agentic AI systems, lowering barriers to advanced AI infrastructure. It is designed for educators and researchers seeking scalable, accessible AI compute for real-world applications.
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Shell scripting improves productivity and reduces errors in HPC workflows by automating tasks like data processing, backups, and system monitoring. This session builds on basic Linux command-line skills to teach Bash scripting syntax, constructs, and best practices for effective automation.
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Essentials of using regular expressions (regexes) with the Linux tools grep, awk and sed. Topics include quantifiers, wildcards, grouping, alternation, word boundaries, lazy and greedy matching and regex flavors. Attendees should at least be familiar with grep.
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The Iron Horse Vineyards Testbed has been on-line a year for teachers and researchers. Iron Horse Vineyards worked with CENIC and AT&T to bring in “fiber to the farm” and engaged UC San Diego and Sonoma State University to instrument the Testbed, collect, process, and archive the data, for access by all, via the National Data Platform.
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Many research problems on high-performance computing (HPC) systems benefit more from high-throughput computing (HTC) or many-task computing (MTC) models, which focus on completing numerous smaller tasks over time rather than using large, parallel applications. Here, we introduce how to build such structured workflows using the Slurm Workload Manager, including job arrays, dependencies, and job bundling strategies, while addressing common challenges in managing HTC/MTC workloads.
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