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Key benefits

Understand effective synchronization strategies for faster processing using GPUs

Write parallel processing scripts with PyCuda and PyOpenCL

Learn to use CUDA libraries such as CuDNN for deep learning on GPUs

Description

GPUs are proving to be excellent general purpose-parallel computing solutions for high-performance tasks such as deep learning and scientific computing. This book will be your guide to getting started with GPU computing. It begins by introducing GPU computing and explaining the GPU architecture and programming models. You will learn, by example, how to perform GPU programming with Python, and look at using integrations such as PyCUDA, PyOpenCL, CuPy, and Numba with Anaconda for various tasks such as machine learning and data mining. In addition to this, you will get to grips with GPU workflows, management, and deployment using modern containerization solutions. Toward the end of the book, you will get familiar with the principles of distributed computing for training machine learning models and enhancing efficiency and performance. By the end of this book, you will be able to set up a GPU ecosystem for running complex applications and data models that demand great processing capabilities, and be able to efficiently manage memory to compute your application effectively and quickly.

What you will learn

Utilize Python libraries and frameworks for GPU acceleration

Set up a GPU-enabled programmable machine learning environment on your system with Anaconda

Deploy your machine learning system on cloud containers with illustrated examples

Explore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL, and ROCm.

Perform data mining tasks with machine learning models on GPUs

Extend your knowledge of GPU computing in scientific applications

What do you get with a Packt Subscription?

Free for first 7 days. $24.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

Subscribe now

View plans & pricing

Frequently bought together

Hands-On GPU Programming with Python and CUDA

AU$67.99

Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

AU$75.99

AU$60.99

Total AU$ 204.97

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Arthur Lau Jun 06, 2019

This is a book about GPU computing, NOT programming.No GPU/Python programming concepts and skills are shown.Most importantly, this book lacks in focus. Too much facts,but no much useful information, concepts or skills are introduced.The first 30% of the book is allocated to the history of CPU and GPU.Starting from 4004 CPU from early days... Although the history is clear and well written,it does not help in improving your GPU computing or programming work.Readers better skip the first 30%.Furthermore, there is no need to list different combinations of building a computer withIntel CPU, AMD CPU, Nvdia GPU, Radeon, at entry-level, at middle level and at high-end level.CUDA, PyCUDA and other modules are included but explained briefly with 2 to 4 paragraphs.Some paragraphs are dedicated to help the reader install the relevant libraries.Some reader may find it helpful. There are examples at the end of the book demonstrating Pytorchand another module. They are practical.If you are are looking for introduction to GPU computing,there are other short and good books which help you to buildup concepts about GPU computing and machine learning.If you are looking for concepts and skills in improving your programming.It is NOT the book for you.Better have a look at a sample of this book and realize what I am talking.

Amazon Verified review

Hands-On GPU Computing with Python: Explore the capabilities of GPUs for solving high performance computational problems

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