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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 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

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Appendix A

Entry-level budget

Hardware requirements for beginners can be assumed to be a set of the minimum requirements to get started with the basics of GPU computing. So, in this section, we will explore different entry-level budget options that you can also access on PC Part Picker. So, let's enlist the PC configurations with their approximate prices, according to the following CPU-GPU combinations:

Intel CPU and NVIDIA GPU:

CPU	Intel Core i3-8100	$118.89
CPU Cooler	Cooler Master – Hyper 212 EVO	$24.99
Motherboard	Gigabyte H310M A	$55.14
GPU	Asus NVIDIA GeForce GTX 1050Ti 4GB	$201.12
RAM	Corsair Vengeance 2x8GB	$119.99
SSD	ADATA – Ultimate SU800 128 GB	$28.99
HDD	Western Digital – Caviar Green 1 TB	$44.89
Case	Cooler Master – MasterBox Lite 5 ATX Mid Tower	$55.98
PSU	SeaSonic ...

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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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