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Hands-On GPU Programming with Python and CUDA

You're reading from   Hands-On GPU Programming with Python and CUDA Explore high-performance parallel computing with CUDA

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993913
Length 310 pages
Edition 1st Edition
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Author (1):
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Dr. Brian Tuomanen Dr. Brian Tuomanen
Author Profile Icon Dr. Brian Tuomanen
Dr. Brian Tuomanen
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Table of Contents (15) Chapters Close

Preface 1. Why GPU Programming? FREE CHAPTER 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 8. The CUDA Device Function Libraries and Thrust 9. Implementation of a Deep Neural Network 10. Working with Compiled GPU Code 11. Performance Optimization in CUDA 12. Where to Go from Here 13. Assessment 14. Other Books You May Enjoy

Compiling and launching pure PTX code

We have just seen how to call a pure-C function from Ctypes. In some ways, this may seem a little inelegant, as our binary file must contain both host code as well as the compiled GPU code, which may seem cumbersome. Can we just use pure, compiled GPU code and then launch it appropriately onto the GPU without writing a C wrapper each and every time? Fortunately, we can.

The NVCC compiler compiles CUDA-C into PTX (Parallel Thread Execution), which is an interpreted pseudo-assembly language that is compatible across NVIDIA 's various GPU architectures. Whenever you compile a program that uses a CUDA kernel with NVCC into an executable EXE, DLL, .so, or ELF file, there will be PTX code for that kernel contained within the file. We can also directly compile a file with the extension PTX, which will contain only the compiled GPU kernels from...

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