Book Image

Hands-On GPU Programming with Python and CUDA

By : Dr. Brian Tuomanen
Book Image

Hands-On GPU Programming with Python and CUDA

By: Dr. Brian Tuomanen

Overview of this book

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.
Table of Contents (15 chapters)

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...