Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory. (Limited-time offer)
- 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
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Publisher: Packt Publishing
Published: November 2018
File size: –
Number of pages: 310
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Free as of 12/15/2020.