Events

This workshop focuses on optimizing HPC workloads with Python, helping researchers run faster, more efficiently, and at larger scale. It covers practical strategies for improving performance, including profiling code to identify bottlenecks, using efficient data structures and libraries, parallelizing work where appropriate, and aligning Python workflows with HPC resources, CPUs, memory, and storage.

The session also highlights common pitfalls that slow jobs down on clusters and demonstrates patterns that improve throughput and reduce wasted compute, giving participants a repeatable approach to tuning Python workloads for HPC environments.