Muhammed Adedigba
I hold a Joint Masters Degree in Advanced Solid Mechanics from the National Technical University of Athens, Università della Calabria, École Centrale de Lille, and Université de Lille.
I currently work as an R&D Software Engineer for Synopsys and I currently contribute to multiple projects within the PyAnsys OSS ecosystem.
Session
Pre- and post-processing of large engineering simulation datasets in some scientific domains often demand performance that pure Python cannot yet deliver, and the general approach within the scientific Python ecosystem for overcoming the performance barrier is the combination of Python with compiled languages.
In this talk, I will share practical insights, experiences, and lessons drawn from the development and maintenance of PyDPF-Core, an open-source library that interacts with a C/C++ backend via a client-server architecture for the processing of large engineering simulation datasets. We will start by discussing the challenges of developing and maintaining such a library and then examine how each of these challenges were solved for PyDPF-Core.
The focus of the talk is on transferrable insights and my goal is for attendees to leave the talk with:
- Knowledge of architectural blueprints that can be applied to their own high-performance python projects, especially those involving the processing of large simulation datasets;
- Patterns for managing complexity in large scientific codebases; and
- Understanding the trade-offs that might be encountered.