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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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Author (1):
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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 FREE CHAPTER
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

GPU manufacturers

Before we go ahead with the different computing platforms and modules on AMD/NVIDIA, let's first look into how the first GPU came into being.

Though the term GPU was first coined by NVIDIA, the IBM Professional Graphics Controller (PGA) was one of the first 2D/3D video cards that was released for the PC in 1984. It used an onboard Intel 8088 microprocessor to handle the processing of all video-related tasks instead of the CPU for video processing (such as the drawing and coloring of filled polygons).

Due to a high price tag of around $5,500 and incompatibility with many programs and non-IBM systems during that time, it could not gain a significant hold on the industry. But, even then, the PGA did establish a standardized model for hardware 2D/3D acceleration as a separate on-board processor, marking an important step in GPU evolution to improve the framework...

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