Python GPU computing (Cunumeric, cuPy, JAX, Pytorch)

🎓Webinar : Python for GPU, by Alice Faure, Nabil Garroum and Jean-Marc Colley

📅The webinar took place on Thursday, 5 June, 2025

✅How to use GPU to speed up computing with Python ? In this webinar we will study possibles senarios to guide you to the library which fit your needs. We will see basic principle of each of them, and the Gray Scott summer school will give a deeper vision of their use. We will present them by level of integration :

for high level : Nvidia CuNumeric, Intel DPNP, JAX, and PyTorch allow to keep practically the same code as the CPU version
for intermediate level : CuPy library provides access to advanced digital functionnalities, but needs specific code for the GPU
for low level : PyCUDA and Numba for GPU provide basic functionnalities on tables.

We will also present associated profiling tools.
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As we gear up for the summer, we’re hosting a special series of 17 weekly webinars covering topics such as:

⚙️ Unit testing, CPU & GPU architectures
💡 Computing precision, memory allocation
🌍 Programming with modern C++, Rust, Fortran, Python, Sycl, EVE, CUDA... and more!
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🌟 Join us for the Gray Scott School 2025! 🌟

The Gray Scott School 2025 will take place from 23 June to 4 July ! 🎉 This year, our program is going European, focusing on the exciting topic of Programming and Optimization on Heterogeneous Architectures.

🟢 Week 1: CPU
🔵 Week 2: GPU
👉 And the best part? It’s free of charge!

Replays are available on this Playlist.