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Hands-On GPU Computing with Python

You're reading from   Hands-On GPU Computing with Python Explore the capabilities of GPUs for solving high performance computational problems

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781789341072
Length 452 pages
Edition 1st Edition
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Avimanyu Bandyopadhyay Avimanyu Bandyopadhyay
Author Profile Icon Avimanyu Bandyopadhyay
Avimanyu Bandyopadhyay
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
2. Introducing GPU Computing FREE CHAPTER 3. Designing a GPU Computing Strategy 4. Setting Up a GPU Computing Platform with NVIDIA and AMD 5. Section 2: Hands-On Development with GPU Programming
6. Fundamentals of GPU Programming 7. Setting Up Your Environment for GPU Programming 8. Working with CUDA and PyCUDA 9. Working with ROCm and PyOpenCL 10. Working with Anaconda, CuPy, and Numba for GPUs 11. Section 3: Containerization and Machine Learning with GPU-Powered Python
12. Containerization on GPU-Enabled Platforms 13. Accelerated Machine Learning on GPUs 14. GPU Acceleration for Scientific Applications Using DeepChem 15. Other Books You May Enjoy Appendix A

Understanding how ROCm-C/C++ works with hipify, HIP, and OpenCL

In this section, we will learn how CUDA code is converted into cross-platform HIP code and how to use the HIP compiler to compile the ported code. Finally, we will explore an OpenCL example by comparing it to CUDA through its documentation, so as to understand the open computing language in an easier manner.

Converting CUDA code into cross-platform HIP code with hipify

As we begin understanding ROCm for both AMD and NVIDIA GPUs, what can be more practical than a hands-on approach to converting our first CUDA program in this book into an ROCm HIP version? Follow these steps to achieve that:

  1. Make sure you have the Terminal open at the location where you have the...
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