The book is divided into 13 chapters:

1. Introduction to GPU and CUDA Programming
2. Setting Up and Running CUDA 12.6
3. CUDA Program and Memory Hierarchy
4. Utilising GPU-Compatible Libraries
5. Tackling Computational Bottlenecks - Computer-Generated Holography
6. Conditional Branching in Simulations - Monte Carlo Method for Optical Properties
7. Overcoming Memory Access Bottlenecks - Electromagnetic Field Simulation Using FDTD Method
8. Fortran Implementation in CUDA - Numerical Solutions to Heat Conduction
9. GPU Programming with OpenCL
10. Using Nsight Compute 2024 for Performance Tuning
11. Debugging at Scale with Nsight Systems 2024
12. Appendix A: Extended Sample Programs for Numerical Calculations
13. Appendix B: Further Reading

After reading this book, you will know the following and much more:

- How to write CUDA programs for various real-world applications
- Techniques for managing memory, control flow, and parallelism in CUDA programming
- Methods for working with GPU-compatible libraries and optimizing performance
- Strategies for debugging, profiling, and maintaining CUDA applications
- Ways to integrate CUDA with other programming languages and tools like Fortran and OpenCL
- Best practices for GPU programming and optimization in modern high-performance computing environments