| Description |
1 online resource : illustrations |
| Contents |
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 -- 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 |
| Summary |
GPUs are designed for maximum throughput, but are subject to low-level subtleties. In contrast, Python is a high-level language that favours ease of use over speed. In this book, we will combine the power of both Python and CUDA to help you create high performing Python applications by using open-source libraries such as PyCUDA and SciKit-CUDA |
| Notes |
Print version record |
| Subject |
CUDA (Computer architecture)
|
|
Graphics processing units.
|
|
Python (Computer program language)
|
|
COMPUTER -- General.
|
|
CUDA (Computer architecture)
|
|
Graphics processing units
|
|
Python (Computer program language)
|
| Form |
Electronic book
|
| ISBN |
9781788995221 |
|
1788995228 |
|
