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

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 Description 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. 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 for Python 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. 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 CUDA Kernels with Numba-CUDA Profiling and Debugging CUDA Code Optimizing the Performance of CUDA Code Enabling Concurrency Using CUDA Streams 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 and Computer Vision on the GPU Simulating Atomic Interactions on the GPU Implementing Your Own Transformer-Based Language Model Expanding and Deepening Your GPU Programming Knowledge