What we’re about
PyData Chicago is a monthly meetup to discuss all things python (or R, Julia, C++, Rust, Go, ...) and data! Our meetup is now hybrid! Enjoy the event in person or virtually, hosted on Zoom. We usually meet on the last Thursday of the month.
All meetups are recorded and posted on PyData's YouTube channel 1 week after the event -- unless the speaker does not want to be recorded. Presentation decks, code and other artifacts from the meeting are also shared with all attendees subscribed to the Meetup event 1 week after the event -- with the permission of the speaker.
The PyData Code of Conduct governs this Meetup. To discuss any issues or concerns relating to the code of conduct or the behavior of anyone at a PyData meetup, please contact co-organizer Olivia Martin via Meetup's message feature.
PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.
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Upcoming events
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•HybridMadSuite: GPU solvers for large-scale optimization
Location not specified yetDetails: In recent years, the development of scalable continuous optimization solvers on GPUs has made significant progress, primarily driven by advances in GPU-based sparse linear algebra.
We first introduce MadNLP.jl, a GPU-native solver for nonlinear programming (NLP) based on interior-point methods and sparse direct solvers. It was the first solver in the suite and remains central to solving general nonlinear problems efficiently.
Building on this foundation, MadNCL.jl is a robust meta-solver that orchestrates multiple NLP solves to handle degeneracy, ill-conditioning, and to achieve high-accuracy solutions with tolerances below 1e-8.
The latest solver MadIPM.jl, targets large-scale linear and convex quadratic programs (LP / QP).
All three solvers are based on second-order algorithms, enabling robustness and accuracy compared to purely first-order methods.
We present performance results on real-world benchmark instances, demonstrating substantial speedups between CPU and GPU implementations, while maintaining high solution quality.
Audience Takeaway:
- Second-order methods scale on GPUs
- Unified GPU solver suite (NLP / LP / QP)
- Robust, high-accuracy solutions
- Demonstrated real-world CPU–GPU speedups
Zoom: This is an online event. To attend online, join us on Zoom here at 6pm:
https://numfocus-org.zoom.us/j/89399976851?pwd=UEgMUZXdYmKdK1x1dIPL6hwUYnp7NW.1
Sponsor: Adyen, PyData Chicago and NumFocus37 attendees
Past events
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