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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

Branded GPU-enabled PCs

If you are not currently looking to build a PC yourself but want to get started with GPU programming straightaway, this section will help you decide the branded system that's best for you.

Leading PC brands offer HPC workstations and GPU-enabled PCs ready for deployment. Such systems are equipped with GPUs that range from low-end to mid-range to high-end specifications and have their respective budgets. Jetson Nano, from NVIDIA, is a cost-effective System on a Chip (SoC) like Raspberry Pi, built especially for AI-based GPU computing. The price is just $99 USD, apparently, as unveiled at the GTC in 2019. It is a great gizmo for beginners getting started with AI computing.

While comparing different brands, your best way to explore is to look into each and every specification of such systems in detail. Depending on your requirements and application usage...

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