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Avimanyu Bandyopadhyay

Hands-On GPU Computing with Python: Explore the capabilities of GPUs for solving high performance computational problems Paperback – Import, 14 May 2019

by Avimanyu Bandyopadhyay (Author)

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Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate Key Features Understand effective synchronization strategies for faster processing using GPUs Write parallel processing scripts with PyCuda and PyOpenCL Learn to use the CUDA libraries like CuDNN for deep learning on GPUs Book DescriptionGPUs 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 will start with introducing GPU computing and explain the architecture and programming models for GPUs. You will learn, by example, how to perform GPU programming with Python, and you'll look at using integrations such as PyCUDA, PyOpenCL, CuPy and Numba with Anaconda for various tasks such as machine learning and data mining. Going further, you will get to grips with GPU work flows, 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 Who this book is forData Scientist, Machine Learning enthusiasts and professionals who wants to get started with GPU computation and perform the complex tasks with low-latency. Intermediate knowledge of Python programming is assumed.

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About the Author

Avimanyu Bandyopadhyay is currently pursuing a PhD degree in Bioinformatics based on applied GPU computing in Computational Biology at Heritage Institute of Technology, Kolkata, India. Since 2014, he developed a keen interest in GPU computing, and used CUDA for his master's thesis. He has experience as a systems administrator as well, particularly on the Linux platform. Avimanyu is also a scientific writer, technology communicator, and a passionate gamer. He has published technical writing on open source computing and has actively participated in NVIDIA's GPU computing conferences since 2016. A big-time Linux fan, he strongly believes in the significance of Linux and an open source approach in scientific research. Deep learning with GPUs is his new passion!

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Product details

Paperback: 452 pages
Publisher: Packt Publishing Limited (14 May 2019)
Language: English
ISBN-10: 1789341078
ISBN-13: 978-1789341072
Product Dimensions: 19 x 2.6 x 23.5 cm
Average Customer Review: 1 customer review
Amazon Bestsellers Rank: #2,39,665 in Books (See Top 100 in Books)
- #641 in Digital Media & Graphic Design (Books)

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Amlan Panigrahi

An Awesome Posession To Have

8 June 2019

Format: Kindle Edition

Most of the developers, data-scientists, researchers, etc. don't realize the importance of gaining knowledge about GPUs and their computational efficiencies. The author has explained everything required from ground-zero to the top level, in an optimized approach to suit the interests varying from that of a naive reader to that of a novice. I loved how the author has emphasized on educating the reader about the basics of building an optimal system fro GPU computing, in adherent to various types of budget.

Most importantly, the book consists of all the minute information required to get comfortable with Nvidia's CUDA and AMD's ROCm platform for GPU Computing. There are detailed examples of how to install and implement various types of packages and in-built libraries to get started with harnessing the power of parallel computing with GPUs. Especially relevant, there are assignments at the end of each practical chapters, which the readers can salvage to become experts in using GPUs for computational needs. The author has also introduced certain sections in the book to indulge the reader about the power and advent of GPUs in research, hence only to excite and get them the feel of it.

The elucidation of GPU Computing with TensorFlow, PyTorch, etc. in the book is something the reader will automatically grind for. Nonetheless, the use of DeepChem (Deep Learning in Chemistry), and it's basic implementation with GPU Computing, was the ultimate chapter for me. Overall, I had an amazing experience, and shall still refer it in the future, for my upcoming projects.

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Most helpful customer reviews on Amazon.com

Amazon.com: 1 reviews

Arthur Lau

Disappointed! Many facts but little useful information.

6 June 2019 - Published on Amazon.com

Format: Kindle EditionVerified Purchase

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 with
Intel 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 Pytorch
and 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 build
up 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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