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Hands-On GPU Computing with Python

You're reading from  Hands-On GPU Computing with Python

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781789341072
Pages 452 pages
Edition 1st Edition
Languages
Author (1):
Avimanyu Bandyopadhyay Avimanyu Bandyopadhyay
Profile icon Avimanyu Bandyopadhyay

Table of Contents (17) Chapters

Preface 1. Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
2. Introducing GPU Computing 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

The significance of Python in AI – the dual advantage

In the scenarios delineated in Appendix A, GPUs are shown to be ideal for big data management, whereas Python is shown to be ideal for data science. But in fact, it is also true the other way around. Hence, Python and GPUs are equally important in both data science and big data, which can be clearly be stated as a dual advantage.

If you look closely at the research papers cited in Appendix A, you will observe that Python and GPUs complement each other very well in terms of AI computing. This fact has led to the creation of machine learning modules in existing frameworks written in Python specifically for GPUs. Before we look into them, we must understand the significance of the dual advantage.

The need for big data management...

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