Book Content
chapters • 10h20m total length
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
