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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

Comparing GPU programmable platforms on NVIDIA and AMD

So far, we have explored the scope of computing on NVIDIA and AMD GPUs through two separate chapters. Now, let's specifically look into the comparisons between their respective APIs:

NVIDIA CUDA

AMD ROCm

The API is called Compute Unified Device Architecture

The API is called Radeon Open Compute platform

Proprietary

Open source

Released in 2007

Released in 2016

Wider support

Still under adoption and very actively catching up

Significant number of programmable libraries

Fewer libraries than CUDA but active ongoing development

Cannot be used with non-NVIDIA devices

Cross-platform independence due to open standards

CUDA-C language being used

HIP for cross-platform; HC for AMD GPUs

.cu extension used for files

.cpp extension used for files

Non-portable

CUDA code...

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