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
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.