- Two examples: DNA analysis and physics simulations.
- Two examples: OpenACC, Numba.
- TPUs are only used for machine learning operations and lack the components required to render graphics.
- Ethernet.
Chapter 12, Where to Go from Here
by Dr. Brian Tuomanen
Hands-On GPU Programming with Python and CUDA
- Title Page
- Copyright and Credits
- Dedication
- About Packt
- Contributors
- Preface
- Why GPU Programming?
- Setting Up Your GPU Programming Environment
- Getting Started with PyCUDA
- Kernels, Threads, Blocks, and Grids
- Streams, Events, Contexts, and Concurrency
- Debugging and Profiling Your CUDA Code
- Using the CUDA Libraries with Scikit-CUDA
- The CUDA Device Function Libraries and Thrust
- Implementation of a Deep Neural Network
- Working with Compiled GPU Code
- Performance Optimization in CUDA
- Where to Go from Here
- Assessment
- Chapter 1, Why GPU Programming?
- Chapter 2, Setting Up Your GPU Programming Environment
- Chapter 3, Getting Started with PyCUDA
- Chapter 4, Kernels, Threads, Blocks, and Grids
- Chapter 5, Streams, Events, Contexts, and Concurrency
- Chapter 6, Debugging and Profiling Your CUDA Code
- Chapter 7, Using the CUDA Libraries with Scikit-CUDA
- Chapter 8, The CUDA Device Function Libraries and Thrust
- Chapter 9, Implementation of a Deep Neural Network
- Chapter 10, Working with Compiled GPU Code
- Chapter 11, Performance Optimization in CUDA
- Chapter 12, Where to Go from Here
- Other Books You May Enjoy
Chapter 12, Where to Go from Here
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