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

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781789341072

Length 452 pages

Edition 1st Edition

Languages

Python

Tools

CUDA

Concepts

Data Processing

Author (1):

Avimanyu Bandyopadhyay

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Table of Contents (17) Chapters

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

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Appendix A

Official PyCUDA home page at https://mathema.tician.de/software/pycuda/
Official PyOpenCL home page at https://mathema.tician.de/software/pyopencl/
Official CuPy website at https://cupy.chainer.org/
Official Numba website at https://numba.pydata.org
Conventional CPU Computing vs GPU Computing: http://www.mediafire.com/file/dv41lvwd9klnat2/Conventional_CPU_Computing_vs_GPU_Computing.pdf

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Authors (1)

Avimanyu Bandyopadhyay

Avimanyu Bandyopadhyay is currently pursuing a PhD degree in Bioinformatics based on applied GPU computing in Computational Biology at Heritage Institute of Technology, Kolkata, India. Since 2014, he developed a keen interest in GPU computing, and used CUDA for his master's thesis. He has experience as a systems administrator as well, particularly on the Linux platform. Avimanyu is also a scientific writer, technology communicator, and a passionate gamer. He has published technical writing on open source computing and has actively participated in NVIDIA's GPU computing conferences since 2016. A big-time Linux fan, he strongly believes in the significance of Linux and an open source approach in scientific research. Deep learning with GPUs is his new passion!

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