Hands-On GPU Programming with Python and CUDA
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.
Offered by
Difficulty Level
Intermediate
Completion Time
10h20m
Language
English
About Book
Who Is This Book For?
Hands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.
Hands-On GPU Programming with Python and CUDA
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 10h20m total length
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 Draft complete
The CUDA Device Function Libraries and Thrust
Implementing a Deep Neural Network
Working with Compiled GPU Code
Performance Optimization in CUDA
Where to Go from Here
Related Resources
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