Maintaining Large Scale Julia Ecosystems | Chris Rackauckas | JuliaHEP 2023

Julia is a great programming language for making fast scripts, but how do you scale it to million lines of code organizations? In this talk, Chris Rackauckas will discuss the professional software development practices which have been successful in the development of large-scale code bases. As the founding developer of the commercial Pumas project, known for its usage in the clinical trials of the Moderna Covid-19 vaccine, the lead developer of the JuliaSim project for safety-critical real-time controls applications, and the lead developer of the open source Julia SciML project (the largest organization of open source packages for the Julia programming language), Chris shares his expertise and experience as to the software infrastructures and principles that have worked, which ones have not worked, and changes being made for the future. Extra materials: SciMLStyle Guide: https://github.com/SciML/SciMLStyle ColPrac: Contributor's Guide on Collaborative Practices for Community Packages: https://github.com/SciML/COLPRAC Originally part of the JuliaHEP 2023 Workshop at the ECAP (Erlangen Centre for Astroparticle Physics), https://indico.cern.ch/event/1292759/ Bio: Bio: Dr. Chris Rackauckas is the VP of Modeling and Simulation at JuliaHub, the Director of Scientific Research at Pumas-AI, Co-PI of the Julia Lab at MIT, and the lead developer of the SciML Open Source Software Organization. For his work in mechanistic machine learning, his work is credited for the 15,000x acceleration of NASA Launch Services simulations and recently demonstrated a 60x-570x acceleration over Modelica tools in HVAC simulation, earning Chris the US Air Force Artificial Intelligence Accelerator Scientific Excellence Award. See more at https://chrisrackauckas.com/. He is the lead developer of the Pumas project and has received a top presentation award at every ACoP in the last 3 years for improving methods for uncertainty quantification, automated GPU acceleration of nonlinear mixed effects modeling (NLME), and machine learning assisted construction of NLME models with DeepNLME. For these achievements, Chris received the Emerging Scientist award from ISoP. Time Stamps: 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/JuliaCommunity/You... Interested in improving the auto generated captions? Get involved here: https://github.com/JuliaCommunity/You...