Hands-On GPU Programming with Python and CUDA

Table of Contents

1. Why GPU Programming?
2. Setting Up Your GPU Programming Environment​
3. Getting Started with PyCUDA​
4. Kernels, Threads, Blocks, and Grids​
5. Streams, Events, Contexts, and Concurrency
6. Debugging and Profiling Your CUDA Code​
7. Using the CUDA Libraries with Scikit-CUDA Draft complete
8. The CUDA Device Function Libraries and Thrust
9. Implementing a Deep Neural Network
10. Working with Compiled GPU Code
11. Performance Optimization in CUDA
12. Where to Go from Here