Python on GPU: CuPy Tutorial for Scientific Computing (Part I)
🐍⚡ Can Python really run efficiently on GPUs? With CuPy — absolutely.
In this hands-on session from Gray Scott School 2025, Jean Marc Colley introduces CuPy, a drop-in replacement for NumPy that brings GPU acceleration to Python-based numerical simulations.
Designed for researchers and developers who want to speed up Python code without rewriting everything in C++, CuPy provides familiar syntax while leveraging NVIDIA CUDA backends under the hood.
📌 What you’ll learn:
Key differences between NumPy and CuPy
Installing and running CuPy on supported GPU hardware
Transferring data between CPU and GPU memory
Benchmarking GPU vs CPU performance
Simulating the Gray-Scott reaction using CuPy arrays and ufuncs
Managing memory and ensuring performance through asynchronous execution
🎓 Perfect for scientists, engineers, and students working with Python and looking for GPU performance — without the boilerplate of C or CUDA.
Useful Link : https://cta-lapp.pages.in2p3.fr/cours...
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🌟 Join us for the Gray Scott School 2026! 🌟
Curious and want a sneak peek of what’s coming? Check out the videos from the 2025 edition on this channel and get inspired for Gray Scott School 2026!
👉 Explore the 2026 program and the agenda of the Gray Scott Thursdays here: https://cc-fr.eu/gray-scott-school-2026/
Don’t miss out — join us this year and dive into the world of high-performance computing with experts, peers, and the HPC community!
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