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)

Who this book is for

This book is aimed at one person in particular—that is, myself in the year 2014, when I was trying to develop a GPU-based simulation for my doctoral studies in math. I was poring over multiple books and manuals on GPU programming, trying to make the slightest sense of the field; most texts seemed happy to throw an endless parade of hardware schematics and buzzwords at the reader on every page, while the actual programming took a back seat.

This book is primarily aimed at those who want to actually do GPU programming, but without getting bogged down with gritty technical details and hardware schematics. We will program the GPU in proper C/C++ (CUDA C) in this text, but we will write it inline within Python code by way of the PyCUDA module. PyCUDA allows us to only write the necessary low-level GPU code that we need, while it automatically handles all of the redundancies of compiling, linking, and launching code onto a GPU for us.