Writing high-performance Python code doesn’t have to mean switching to C++. This book shows you how to accelerate Python applications using NVIDIA’s CUDA platform and a modern ecosystem of Python tools and libraries. Aimed at researchers, engineers, and data scientists, it offers a practical yet deep understanding of GPU programming and how to fully exploit modern GPU hardware.

You’ll begin with the fundamentals of CUDA programming in Python using Numba-CUDA, learning how GPUs work and how to write, execute, and debug custom GPU kernels. Building on this foundation, the book explores memory access optimization, asynchronous execution with CUDA streams, and multi-GPU scaling using Dask-CUDA. Performance analysis and tuning are emphasized throughout, using NVIDIA Nsight profilers.

You’ll also learn to use high-level GPU libraries such as JAX, CuPy, and RAPIDS to accelerate numerical Python workflows with minimal code changes. These techniques are applied to real-world examples, including PDE solvers, image processing, physical simulations, and transformer models.

Written by experienced GPU practitioners, this hands-on guide emphasizes reproducible workflows using Python 3.10+, CUDA 12.3+, and tools like the Pixi package manager. By the end, you’ll have future-ready skills for building scalable GPU applications in Python.

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Spis treści

GPU-Accelerated Computing with Python 3 and CUDA. From low-level kernels to real-world applications in scientific computing and machine learning eBook -- spis treści

• 1. Why GPU programming with CUDA in Python 3?
• 2. Setting up a GPU programming environment locally and in the cloud
• 3. Writing and executing a CUDA kernel with numba
• 4. Profiling and debugging CUDA code
• 5. Optimize memory access patterns and other tricks
• 6. Using CUDA Streams for Asynchronous Data Transfers
• 7. Scaling to multiple GPUs
• 8. Bringing NumPy and SciPy to the GPU with CuPy
• 9. Bringing Pandas and Scikit-learn to the GPU with Rapids
• 10. Solving Optimization Problems on the GPU with JAX
• 11. Solving the heat equation on the GPU
• 12. Image processing on the GPU
• 13. Simulating Atomic Interactions on the GPU
• 14. Implementing your own transformer based language model from scratch
• 15. Expanding and Deepening your GPU Programming Knowledge