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Hands-On GPU Computing with Python
Hands-On GPU Computing with Python

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

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Profile Icon Avimanyu Bandyopadhyay
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$20.98 $29.99
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eBook May 2019 452 pages 1st Edition
eBook
$20.98 $29.99
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$43.99
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Arrow left icon
Profile Icon Avimanyu Bandyopadhyay
Arrow right icon
$20.98 $29.99
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2 (1 Ratings)
eBook May 2019 452 pages 1st Edition
eBook
$20.98 $29.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$20.98 $29.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m

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Hands-On GPU Computing with Python

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...

The world of GPU computing beyond PC gaming

If you are a PC gamer, you must be very familiar with the world of graphics cards. Depending on their specifications, you might also be familiar with how each of them would affect your gaming experience. Let's explore extensively what lies beyond that domain through the subsequent sections in this chapter.

What is a GPU?

A GPU, as the initialism suggests, is an electronic circuit that serves as a processor for handling graphical information to output on a display. The scope of this book is to go beyond just handling graphical information and stepping into the general purpose computing with GPUs (GPGPU) arena. GPGPU is all about the use of what is typically performed with central...

Conventional CPU computing – before the advent of GPUs

Before GPUs arrived, general-purpose computing, as we know it, was only possible with CPUs, which were the first mainstream processors manufactured for both consumers as well as advanced computing enthusiasts.

Both computational and graphical processing were handled only by them. This meant that both the tasks of processing and handling computation of input and showing its corresponding computed output on a display were all handled by a CPU.

The history of general-purpose computing goes way back to the 1950s, before GPUs arrived and revolutionized the concept. The 1970s witnessed the rise of a new era, when the first commercial CPU, the Intel 4004, was released by Intel in 1971. The first AMD CPU was also launched in the 70s with the launch of AM2900 in 1975. There was no looking back, and a new cycle of CPU manufacturing...

How the gaming industry made GPU computing affordable for individuals

Gaming is over a $100 billion USD industry. But way back in the 1950s, video games were purely made for academic purposes. Video games were a medium to demonstrate the capabilities of a newly invented technology. They were also a good application to test AI applications through tic-tac-toe or chess. But access to such platforms was still limited to computer lab environments.

Spacewar became the first purpose-built computer game in 1962.

By the 1970s, the area of gaming started to change. Arcade gaming became very popular. The PC gaming landscape took proper shape in the 80s with programmable computers in almost every household equipped with popular games such as Super Mario Bros, Donkey Kong, Prince of Persia, and more.

The 90s saw the emergence of legendary games such as Doom and Quake, which radically changed...

The emergence of full-fledged GPU computing

From the first GPUs to the most powerful GPUs seen today, GPUs continue to make a noticeable mark upon society with limitless applications, as we are going to see in the The social impact of GPUs section of this chapter. For now, let's look into how GPU specifications evolved since they became available at much reduced costs, since the rise of the gaming industry.

GPU computing has massively grown in the last two decades with the creation of GPU application programmable interfaces (APIs) such as Compute Unified Device Architecture (CUDA) and OpenCL. These APIs allow the programmer to harness the parallel computational elements within the GPU.

Let's compare these two APIs:

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

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

Python is a programming language with syntax that is very easy to grasp and understand, especially for computational analysts from backgrounds other than computer science. Due to this reason, it is adopted quite whole-heartedly throughout the entire research community in the world. When we also consider the powerful computational capabilities of GPUs, a dual advantage is clearly noticeable, when combined with the simplistic nature of Python syntax.

The C language – a short prologue

The C language was created as a procedural and structured programming language, developed between 1969 and 1973 at AT&T Bell Labs by Dennis M. Ritchie. As...

How GPUs empower science and AI in current times

NVIDIA RAPIDS is a very recent example of using an open source system to carry out research related to science, AI, and other fields. There are numerous examples of research work in science that has been empowered by GPU acceleration. Let's understand its significance through some of these amazing research examples.

Bioinformatics workflow management

The following research paper is discussed in this section: Managing Complex Workflows in Bioinformatics: An Interactive Toolkit With GPU Acceleration, A Welivita, I Perera, D Meedeniya, A Wickramarachchi, V Mallawaarachchi (2018), IEEE Transactions on NanoBioscience, 17(3), 199-208, doi:10.1109/tnb.2018.2837122.

BioWorkflow...

The social impact of GPUs

Big data is everywhere and so datasets are also everywhere, meaning that GPUs and AI can be applied to any other computational field. Since we have already discussed how GPUs can contribute in science and AI, let's read about some more examples in other fields that reveal more about how GPUs can contribute to our society.

We are going to discuss some diverse fields here, but, of course, there are no limits!

Archaeological restoration/reconstruction

The following research paper is discussed in this section: Automated GPU-Based Surface Morphology Reconstruction of Volume Data for Archaeology, D Jungblut, S Karl, H Mara, S Krömker, G Wittum (2012), Contributions in Mathematical and Computational...

Summary

In this chapter, we learned about the basic concepts behind GPU computing, the history behind its evolution, and its scope of use in diverse fields. We also learned about the simplicity of Python syntax and why this simplicity can be of great significance when harnessing GPUs for computational work. In the final section, we looked at the uses of GPU applications beyond just science and AI, and we looked at various fields, such as archaeology, weather, music, sports, art, security, agriculture, and economics.

If you are a gamer and/or a computing enthusiast, from now on, you will be able to better understand the computational aspect of GPUs and Python code. The various fields of applications that were discussed in the later sections of this chapter will now allow you to have a general idea about the limitless areas in which you can create your own GPU applications. GPU...

Further reading

You can read the following research papers and articles to gain more knowledge about the topics that were discussed in this chapter:

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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.

Who is this book for?

Data scientists, machine learning enthusiasts, or professionals who want to get started with GPU computation and perform the complex tasks with low-latency will find this book useful. Intermediate knowledge of Python programming is assumed.

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

Product Details

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Publication date : May 14, 2019
Length: 452 pages
Edition : 1st
Language : English
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Product Details

Publication date : May 14, 2019
Length: 452 pages
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Table of Contents

15 Chapters
Section 1: Computing with GPUs Introduction, Fundamental Concepts, and Hardware Chevron down icon Chevron up icon
Introducing GPU Computing Chevron down icon Chevron up icon
Designing a GPU Computing Strategy Chevron down icon Chevron up icon
Setting Up a GPU Computing Platform with NVIDIA and AMD Chevron down icon Chevron up icon
Section 2: Hands-On Development with GPU Programming Chevron down icon Chevron up icon
Fundamentals of GPU Programming Chevron down icon Chevron up icon
Setting Up Your Environment for GPU Programming Chevron down icon Chevron up icon
Working with CUDA and PyCUDA Chevron down icon Chevron up icon
Working with ROCm and PyOpenCL Chevron down icon Chevron up icon
Working with Anaconda, CuPy, and Numba for GPUs Chevron down icon Chevron up icon
Section 3: Containerization and Machine Learning with GPU-Powered Python Chevron down icon Chevron up icon
Containerization on GPU-Enabled Platforms Chevron down icon Chevron up icon
Accelerated Machine Learning on GPUs Chevron down icon Chevron up icon
GPU Acceleration for Scientific Applications Using DeepChem Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
(1 Ratings)
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1 star 0%
Arthur Lau Jun 06, 2019
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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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