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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

Inline PTX assembly

We will now scratch the surface of writing PTX (Parallel Thread eXecution) Assembly language, which is a kind of a pseudo-assembly language that works across all Nvidia GPUs, which is, in turn, compiled by a Just-In-Time (JIT) compiler to the specific GPU's actual machine code. While this obviously isn't intended for day-to-day usage, it will let us work at an even a lower level than C if necessary. One particular use case is that you can easily disassemble a CUDA binary file (a host-side executable/library or a CUDA .cubin binary) and inspect its PTX code if no source code is otherwise available. This can be done with the cuobjdump.exe -ptx cuda_binary command in both Windows and Linux.

As stated previously, we will only cover some of the basic usages of PTX from within CUDA-C, which has a particular syntax and usage which is similar to that of...

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