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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

Current models from both brands that are ideal for GPU computing

In the following comparative study, we will explore the capabilities of the best GPUs available from both AMD and NVIDIA to understand the scenarios for adopting a computing-based approach.

AMD Radeon VII GPU – the new people's champion

As a GPU that is affordable by both gamers and computing enthusiasts at $699 USD, the AMD Radeon VII GPU absolutely befits Radeon VII when compared to the Titan V. With superior FP64 performance compared to its competitors in a similar price bracket, this GPU is clearly a very preferable choice for scientific computing. We will explore it in more detail later, with a few benchmarks included through out this section...

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