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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

The emergence of full-fledged GPU computing

From the first GPUs to the most powerful GPUs seen today, GPUs continue to make a noticeable mark upon society with limitless applications, as we are going to see in the The social impact of GPUs section of this chapter. For now, let's look into how GPU specifications evolved since they became available at much reduced costs, since the rise of the gaming industry.

GPU computing has massively grown in the last two decades with the creation of GPU application programmable interfaces (APIs) such as Compute Unified Device Architecture (CUDA) and OpenCL. These APIs allow the programmer to harness the parallel computational elements within the GPU.

Let's compare these two APIs:

CUDA OpenCL
CUDA has been specifically written for NVIDIA GPU architecture. OpenCL is not architecture-specific and is more commonly known as a computing...
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