Research Computing (full-day) Advanced Bootcamp

The Office of Research Cyberinfrastructure is hosting a one-day Research Computing Advanced Bootcamp for users interested in specialized topics in research computing such as strategies for leveraging multi-GPU architectures in parallel workflows, GPU profiling, limitations of pandas for large DataFrames, other high-performance tools for DataFrames, querying large language models via Python APIs, reproducibility practices, and automated plotting techniques. The workshop will include three sessions featuring hands-on exercises, followed by an open discussion and Q&A. 

Session 1: Distributed GPU Architecture for LLMs
This session introduces GPU computing fundamentals and memory considerations in machine learning workflows.
It also examines multi-GPU strategies—including model parallelism, Distributed Data Parallel (DDP), and Fully Sharded Data Parallel (FSDP)—through practical examples and hands-on exercises.

Session 2: Handling Large DataFrames in Python
This session explores the performance and memory limitations of pandas when working with large-scale datasets.
It presents modern alternatives such as Polars and covers efficient data handling techniques, including optimized storage formats, chunked processing, and extensions to distributed and GPU-enabled frameworks.

Session 3: Python and DataFrames for Sensible Experiment Management
This session focuses on developing structured and reproducible workflows for computational research.
Participants will build a benchmarking framework for LLM inference while learning best practices in data aggregation, API integration, and automated visualization.

For further information on sessions and tentative agenda please visit: 
https://rci.research.ucf.edu/events/research-computing-full-day-advanced-bootcamp-june-2026/

Please note: All the sessions have a hands-on component. To participate in the hands-on exercises during the session, you will need to bring your own laptop equipped with a web browser as well as install any software specific to that lesson. Refer to each lesson's description for specific instruction.