This advanced session focuses on improving the efficiency, scalability, and reproducibility of your research workflows on the Hoffman2 Cluster. Topics include job arrays, resource monitoring, environment management (e.g., python, conda), and best practices for data and storage usage. Ideal for users ready to streamline and scale their computational research.
This workshop introduces participants to running batch jobs on the Hoffman2 Cluster using the job scheduler. Attendees will learn how to write and submit batch scripts, request computing resources, monitor job status, and manage output files. This session is essential for researchers aiming to run larger or automated workloads efficiently.
Part of the series: From Scripts to Software: Practical Python for Reproducible Research Having learned how to write sustainable, object-oriented code and how to scale it across multiple processors, we will now explore how to dramatically accelerate your computations using Graphics Processing Units (GPUs). Modern GPUs are massively parallel processors that can perform millions of...