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Introducing GPU Computing

Many years ago, I used to think that a graphics processing unit (GPU), more commonly known as a graphics card, was just a device dedicated to playing video games on a computer at their maximum potential. But, one day, while going through a textbook (Advanced Computer Architecture by Kai Hwang), I realized that I was unaware of a world that goes way beyond PC gaming.

Without a doubt, most consumer GPUs are manufactured to achieve those amazing graphics and visuals to enable some spell-binding gameplay. But there's a world that explores its application a whole lot further, and that is the world of GPU computing.

In this chapter, we are going to learn the basic ideas behind GPU computing, a historical recap on computing, and the rise of GPU computing. We will also read about the simplicity of Python and the power of GPUs, and learn about the scope of...

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 Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

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DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Hands-On GPU Programming with Python and CUDA

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

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Total $ 147.97

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.

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

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

What do you get with Print?

Hands-On GPU Computing with Python

Introducing GPU Computing

The world of GPU computing beyond PC gaming

What is a GPU?

Conventional CPU computing – before the advent of GPUs

How the gaming industry made GPU computing affordable for individuals

The emergence of full-fledged GPU computing

The simplicity of Python code and the power of GPUs – a dual advantage

The C language – a short prologue

How GPUs empower science and AI in current times

Bioinformatics workflow management

The social impact of GPUs

Archaeological restoration/reconstruction

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Further reading

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