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SUMMARY:Lessons from Building a Large-Scale Engineering Simulation Data Pr
 ocessing Library - Muhammed Adedigba
DTSTART;TZID=Poland:20260721T163000
DTEND;TZID=Poland:20260721T165000
DTSTAMP:20260913T121313Z
UID:pretalx-euroscipy-2026-EBDPBB@pretalx.com
DESCRIPTION:Pre- and post-processing of large engineering simulation datas
 ets in some scientific domains often demand performance that pure Python c
 annot yet deliver\, and the general approach within the scientific Python 
 ecosystem for overcoming the performance barrier is the combination of Pyt
 hon with compiled languages.\n \nIn this talk\, I will share practical ins
 ights\, experiences\, and lessons drawn from the development and maintenan
 ce of PyDPF-Core\, an open-source library that interacts with a C/C++ back
 end via a client-server architecture for the processing of large engineeri
 ng simulation datasets. We will start by discussing the challenges of deve
 loping and maintaining such a library and then examine how each of these c
 hallenges were solved for PyDPF-Core.\n \nThe focus of the talk is on tran
 sferrable insights and my goal is for attendees to leave the talk with:\n-
  Knowledge of architectural blueprints that can be applied to their own hi
 gh-performance python projects\, especially those involving the processing
  of large simulation datasets\;\n- Patterns for managing complexity in lar
 ge scientific codebases\; and\n- Understanding the trade-offs that might b
 e encountered.
LOCATION:Room 1.19 (Ground Floor\, Shannon)
URL:https://pretalx.com/euroscipy-2026/talk/EBDPBB/
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