Linux command line interface (CLI) skills are essential for advanced cyberinfrastructure (CI). This session covers filesystem hierarchy, permissions, links, wildcards, finding files, environment variables, modules, config files, aliases, history & Bash scripting tips.
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This talk will start with an overview of the ICICLE (Intelligent CyberInfrastructure (CI) with Computational Learning in the Environment), an NSF-AI Institute, to address these challenges.
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A brief introduction to the Linux scheduler, how to interact with it, and run your research workloads on your personal computer, a shared workstation, or even a high-performance computing system.
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Topographic differencing measures landscape change at the Earth’s surface due to natural and anthropogenic processes. OpenTopography, an NSF-funded data facility, provides access to topographic data and processing tools. OpenTopography has recently developed on-demand topographic differencing and uncertainty calculation tools that can be applied to roughly 25% of the US mainland. Our uncertainty tool, available via a cloud-based app and Jupyter notebook, quantifies the magnitude of uncertainty at multiple spatial scales, mirroring the variable scales of common error types. We demonstrate these capabilities by calculating topographic change for the 2025 Eaton and Palisades Fires in Los Angeles.
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An overview of commonly used Linux tools for searching and manipulating text. We progress from the simplest tools, such as head, tail, cut, paste, to more complex tools grep, awk and sed.
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Jupyter Notebooks are great for exploration, but terrible for reproducibility and scaling. This talk shows how to migrate notebook code into a structured Python package that can run anywhere—locally, on HPC clusters, or in automated pipelines. We’ll connect these practices to early-stage MLOps concepts, illustrating how packaging is the foundation for reproducible scientific computing.
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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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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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Large language models (LLMs) are trained on massive, publicly available text datasets containing trillions of tokens. However, these models do not necessarily possess sufficient subject matter expertise and often struggle to provide meaningful responses to specialized prompts. Therefore, fine-tuning LLMs with domain-specific datasets – extracted from documents, articles, and other sources – is crucial. In this presentation, we will demonstrate fine-tuning a few smaller LLMs with LoRA approach using instruct datasets on Gaudi hardware.
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SAVE THE DATE - This two-day virtual workshop is an extension of the COMPLECS webinar series to provide more in-depth discussion and complementary hands-on experience. This opportunity is designed to help users strengthen skills using HPC (High-Performance Computing) systems.
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High-performance computing (HPC) systems use various specialized storage and file systems, each with different performance levels and best-use scenarios. Here, we explain how to properly use these systems for your research. We also introduce common filesystems, their architectures, appropriate I/O patterns, and key Linux tools for managing storage usage, backups, and file permissions.
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This talk highlights advances in GPU-accelerated molecular simulations and the hardware that enables them. We will begin with an overview of modern GPU architectures, supercomputers, and programming models that leverage massive parallelism for scientific computing. We will then discuss GPU-optimized molecular dynamics simulations with Amber and quantum chemistry calculations with QUICK, a massively parallel Hartree-Fock and DFT program that performs all major computations on GPUs. Together, these tools enable efficient QM/MM molecular dynamics simulations across diverse GPU platforms. Advances in software and GPU technology are thus transforming the scale and accuracy of simulations for complex chemical and biological systems.
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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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This session introduces key strategies for transitioning computations to HPC systems, including using pre-installed software, compiling code with appropriate tools, and setting up Python, R, and conda environments. It also covers workflow management and introduces containerized solutions with Singularity, featuring hands-on practice on SDSC resources.
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