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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...

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

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

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

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Alexander Shnaiderman Feb 27, 2023

Good book, and came fast

Amazon Verified review

Joseph Picone Aug 25, 2022

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.

Ahmad Junaid Nov 27, 2021

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.

Mark Ettinger Jan 09, 2020

This book is clear, thorough, and comprehensive. Because it leverages PyCUDA, a python interface to the NVIDIA compiler, you can work your way through the book using free Colab notebooks with GPU runtimes. This is helpful if you don't already have a NVIDIA GPU, for example if you own a Mac as I do. The code uses Python 2 which is being phased out on Colab so you may need to convert the code to Python 3. There are websites and scripts that do this automatically. This book also makes a good predecessor to another good book "Professional CUDA C Programming" or the two can be read in parallel (pun intended). Highly recommended!

Yading Yue May 06, 2019

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.

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

Technical requirements

Ensuring that we have the right hardware

Installing the GPU drivers

Setting up a C++ programming environment

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

Setting up our Python environment for GPU programming

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

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