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)

Working with Compiled GPU Code

Throughout the course of this book, we have generally been reliant on the PyCUDA library to interface our inline CUDA-C code for us automatically, using just-in-time compilation and linking with our Python code. We might recall, however, that sometimes the compilation process can take a while. In Chapter 3, Getting Started With PyCUDA, we even saw in detail how the compilation process can contribute to slowdown, and how it can even be somewhat arbitrary as to when inline code will be compiled and retained. In some cases, this may be inconvenient and cumbersome given the application, or even unacceptable in the case of a real-time system.

To this end, we will finally see how to use pre-compiled GPU code from Python. In particular, we will look at three distinct ways to do this. First, we will look at how we can do this by writing a host-side CUDA...