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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 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

Computing on NVIDIA GPUs

NVIDIA has three notable GPU platforms, namely GeForce, Quadro, and Tesla, that support general- purpose computation. At the higher end of the GeForce series are the consumer-level GPUs that GPU computing enthusiasts are usually interested in for running GPU-accelerated applications at a lower budget range (with the exception of the Titan series). On the higher budget perspective, the Quadro and Tesla lineup are specifically targeted toward GPU-accelerated computational applications. A lot of features for such applications are available only on Quadro and Tesla GPUs. All GPUs belonging to these three platforms differ in performance and features. They have transitioned to different micro-architectures through the years since their inception.

GeForce platforms...

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