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Hands-On GPU Programming with Python and CUDA
Hands-On GPU Programming with Python and CUDA

Hands-On GPU Programming with Python and CUDA: Explore high-performance parallel computing with CUDA

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Hands-On GPU Programming with Python and CUDA

Setting Up Your GPU Programming Environment

We will now see how to set up an appropriate environment for GPU programming under both Windows and Linux. In both cases, there are several steps we will have to take. We will proceed through these steps one-by-one, noting any differences between Linux and Windows as we proceed. You should, of course, feel free to skip or ignore any sections or comments that don't apply to your choice of operating system.

The reader should note that we will only cover two platforms for 64-bit Intel/AMD-based PCs in this chapter—Ubuntu LTS (long-term support) releases and Windows 10. Note that any Ubuntu LTS-based Linux operating systems (such as Xubuntu, Kubuntu, or Linux Mint) are also equally appropriate to the generic Unity/GNOME-based Ubuntu releases.

We suggest the use of Python 2.7 over Python 3.x. Python 2.7 has stable support across...

Technical requirements

Ensuring that we have the right hardware

For this book, we recommend that you have the following hardware as a minimum:

  • 64-bit Intel/AMD-based PC
  • 4 gigabytes (GB) of RAM
  • NVIDIA GeForce GTX 1050 GPU (or higher)

This configuration will ensure that you can comfortably learn GPU programming, run all of the examples in this book, and also run some of the other newer and interesting GPU-based software, such as Google's TensorFlow (a machine learning framework) or the Vulkan SDK (a cutting-edge graphics API).

Note that you must have an NVIDIA brand GPU to make use of this book! The CUDA Toolkit is proprietary for NVIDIA cards, so it won't work for programming Intel HD or Radeon GPUs.

As stated, we will be assuming that you are using either the Windows 10 or Ubuntu LTS (long-term support) release.

Ubuntu LTS releases generally have version numbers of the form 14.04, 16.04...

Installing the GPU drivers

If you already have drivers for your GPU installed, you may possibly skip this step; moreover, some versions of CUDA are pre-packaged with the latest drivers. Quite often, CUDA is very particular about which driver you have installed and may not even work with the CUDA Toolkit driver, so you may have to experiment with several different drivers before you find one that works.

Generally speaking, Windows has better CUDA driver compatibility and a more user-friendly installation than Linux. Windows users may consider skipping this step and just use the driver that is packaged with the CUDA Toolkit, which we will install a little later in this chapter. We would strongly suggest that Linux users (particularly Linux laptop users), however, closely follow all the steps in this section before proceeding.

...

Setting up a C++ programming environment

Now that we have our drivers installed, we have to set up our C/C++ programming environment; both Python and CUDA are particular about what compilers and IDEs they may integrate with, so you may have to be careful. In the case of Ubuntu Linux users, the standard repository compilers and IDEs generally work and integrate perfectly with the CUDA Toolkit, while Windows users might have to exercise a little more caution.

Setting up GCC, Eclipse IDE, and graphical dependencies (Linux)

Open up a Terminal from the Ubuntu desktop (Ctrl + Alt + T). We first update the apt repository as follows:

sudo apt-get update

Now we can install everything we need for CUDA with one additional line:

sudo...

Setting up our Python environment for GPU programming

With our compilers, IDEs, and the CUDA Toolkit properly installed on our system, we now can set up an appropriate Python environment for GPU programming. There are many options here, but we explicitly recommend that you work with the Anaconda Python Distribution. Anaconda Python is a self-contained and user-friendly distribution that can be installed directly in your user directory, and which does not require any administrator or sudo level system access to install, use, or update.

Keep in mind that Anaconda Python comes in two flavors—Python 2.7, and Python 3. Since Python 3 is currently not as well-supported for some of the libraries we will be using, we will be using Python 2.7 in this book, which still has a broad mainstream usage.

You can install Anaconda Python by going to https://www.anaconda.com/download, choosing...

Summary

Setting up your Python environment for GPU programming can be a very delicate process. The Anaconda Python 2.7 distribution is suggested for both Windows and Linux users for the purposes of this text. First, we should ensure that we have the correct hardware for GPU programming; generally speaking, a 64-bit Windows or Linux PC with 4 gigabytes of RAM and any entry-level NVIDIA GPU from 2016 or later will be sufficient for our ends. Windows users should be careful in using a version of Visual Studio that works well with both the CUDA Toolkit and Anaconda (such as VS 2015), while Linux users should be particularly careful in the installation of their GPU drivers, and set up the appropriate environment variables in their .bashrc file. Furthermore, Windows users should create an appropriate launch script that will set up their environment for GPU programming and should use...

Questions

  1. Can we run CUDA on our main processor's built-in Intel HD GPU? What about on a discrete AMD Radeon GPU?
  2. Does this book use Python 2.7 or Python 3.7 for examples?
  3. What program do we use in Windows to see what GPU hardware we have installed?
  4. What command-line program do we use in Linux to see what GPU hardware we have installed?
  5. What is the command we use in Linux to determine how much memory our system has?
  6. If we don't want to alter our Linux system's APT repository, should we use the run or deb installer for CUDA?
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Key benefits

  • Expand your background in GPU programming—PyCUDA, scikit-cuda, and Nsight
  • Effectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolver
  • Apply GPU programming to modern data science applications

Description

Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory. As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS. With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain. By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing.

Who is this book for?

Hands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.

What you will learn

  • Launch GPU code directly from Python
  • Write effective and efficient GPU kernels and device functions
  • Use libraries such as cuFFT, cuBLAS, and cuSolver
  • Debug and profile your code with Nsight and Visual Profiler
  • Apply GPU programming to datascience problems
  • Build a GPU-based deep neuralnetwork from scratch
  • Explore advanced GPU hardware features, such as warp shuffling
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Publication date : Nov 27, 2018
Length: 310 pages
Edition : 1st
Language : English
ISBN-13 : 9781788993913
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Publication date : Nov 27, 2018
Length: 310 pages
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Language : English
ISBN-13 : 9781788993913
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Table of Contents

14 Chapters
Why GPU Programming? Chevron down icon Chevron up icon
Setting Up Your GPU Programming Environment Chevron down icon Chevron up icon
Getting Started with PyCUDA Chevron down icon Chevron up icon
Kernels, Threads, Blocks, and Grids Chevron down icon Chevron up icon
Streams, Events, Contexts, and Concurrency Chevron down icon Chevron up icon
Debugging and Profiling Your CUDA Code Chevron down icon Chevron up icon
Using the CUDA Libraries with Scikit-CUDA Chevron down icon Chevron up icon
The CUDA Device Function Libraries and Thrust Chevron down icon Chevron up icon
Implementation of a Deep Neural Network Chevron down icon Chevron up icon
Working with Compiled GPU Code Chevron down icon Chevron up icon
Performance Optimization in CUDA Chevron down icon Chevron up icon
Where to Go from Here Chevron down icon Chevron up icon
Assessment Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Joseph Picone Aug 25, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is an excellent introduction on how to program a GPU. I use it in my split-level course on parallel processing and GPU programming. It explains key concepts very clearly.
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Alexander Shnaiderman Feb 27, 2023
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Good book, and came fast
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Sujeeth Bharadwaj Mar 31, 2019
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This is truly an incredible resource for beginners as well as software engineers alike. The author does an amazing job of explaining core cuda principles with concrete examples of how to implement efficient and readable code in python. I definitely recommend this book to anyone interested in diving deeper into GPU acceleration.
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Yading Yue May 06, 2019
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I followed the guides in the book and adapted the codes from the book in my own kernel which is running correctly now. The author was recommending that Python 2 is more stable than 3, which is very true -- with 3, I got many strange nvcc errors, even for the sample codes of the book when only a blank space or a blank line was added. I would recommend the book anyone who needs to save their time.
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Ahmad Junaid Nov 27, 2021
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This book has given tremendous practical value to my projects as a researcher and engineer. A few words could never do it justice, but it’s for anyone seeking 100x speed improvements without having to give up the ease and comfort of Python’s development environment. It goes step by step through implementations of highly performant heterogenous computing programs right within Python, with readily reusable kernels—but it also treats the theoretical aspects in depth, covering core concepts in both CUDA C and general massively parallelized systems design.About to start on another ML project, I waited impatiently for the second edition to implement the changes moving from Python 2.x to 3. It’s unfortunate that its release has been delayed so, but when I reached out to the author directly I was shocked to have him offer to help and share his updated materials and notes from the upcoming second edition. I’m truly honoured, forever grateful and looking forward to more titles from him.
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