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Hands-On GPU Computing with Python

You're reading from  Hands-On GPU Computing with Python

Product type Book
Published in May 2019
Publisher Packt
ISBN-13 9781789341072
Pages 452 pages
Edition 1st Edition
Languages
Author (1):
Avimanyu Bandyopadhyay Avimanyu Bandyopadhyay
Profile icon Avimanyu Bandyopadhyay

Table of Contents (17) Chapters

Preface Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware
Introducing GPU Computing Designing a GPU Computing Strategy Setting Up a GPU Computing Platform with NVIDIA and AMD Section 2: Hands-On Development with GPU Programming
Fundamentals of GPU Programming Setting Up Your Environment for GPU Programming Working with CUDA and PyCUDA Working with ROCm and PyOpenCL Working with Anaconda, CuPy, and Numba for GPUs Section 3: Containerization and Machine Learning with GPU-Powered Python
Containerization on GPU-Enabled Platforms Accelerated Machine Learning on GPUs GPU Acceleration for Scientific Applications Using DeepChem Other Books You May Enjoy Appendix A

Conventional CPU computing – before the advent of GPUs

Before GPUs arrived, general-purpose computing, as we know it, was only possible with CPUs, which were the first mainstream processors manufactured for both consumers as well as advanced computing enthusiasts.

Both computational and graphical processing were handled only by them. This meant that both the tasks of processing and handling computation of input and showing its corresponding computed output on a display were all handled by a CPU.

The history of general-purpose computing goes way back to the 1950s, before GPUs arrived and revolutionized the concept. The 1970s witnessed the rise of a new era, when the first commercial CPU, the Intel 4004, was released by Intel in 1971. The first AMD CPU was also launched in the 70s with the launch of AM2900 in 1975. There was no looking back, and a new cycle of CPU manufacturing...

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