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Full Description
Accelerate your Python code on the GPU using CUDA, Numba, and modern libraries to solve real-world problems faster and more efficiently.
Key Features
Build a solid foundation in CUDA with Python, from kernel design to execution and debugging
Optimize GPU performance with efficient memory access, CUDA streams, and multi-GPU scaling
Use JAX, CuPy, RAPIDS, and Numba to accelerate numerical computing and machine learning
Create practical GPU applications, from PDE solvers to image processing and transformers
Book DescriptionWriting 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.What you will learn
Understand GPU execution, parallelism, and the CUDA programming model
Write, launch, and debug custom CUDA kernels in Python with CUDA
Profile GPU code with NVIDIA Nsight and optimize memory access
Use CUDA streams and async execution to overlap compute and transfers
Apply JAX, CuPy, and RAPIDS to numerical computing and machine learning
Scale GPU workloads across devices using Dask and multi-GPU strategies
Accelerate PDE solvers, simulations, and image processing on the GPU
Build, train, and run a transformer model from scratch on the GPU
Who this book is forPython developers, (data) scientists, engineers, and researchers looking to accelerate numerical computations without switching to low-level languages. This book is ideal for those with experience in scientific Python (NumPy, Pandas, SciPy) and a basic understanding of computing fundamentals who want deeper control over performance in GPU environments.
Contents
Table of Contents
Why GPU programming with CUDA in Python 3?
Setting up a GPU programming environment locally and in the cloud
Writing and executing a CUDA kernel with numba
Profiling and debugging CUDA code
Optimize memory access patterns and other tricks
Using CUDA Streams for Asynchronous Data Transfers
Scaling to multiple GPUs
Bringing NumPy and SciPy to the GPU with CuPy
Bringing Pandas and Scikit-learn to the GPU with Rapids
Solving Optimization Problems on the GPU with JAX
Solving the heat equation on the GPU
Image processing on the GPU
Simulating Atomic Interactions on the GPU
Implementing your own transformer based language model from scratch
Expanding and Deepening your GPU Programming Knowledge
