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)

Writing wrappers for the CUDA Driver API

We will now look at how we can write our very own wrappers for some pre-packaged binary CUDA library functions using Ctypes. In particular, we will be writing wrappers for the CUDA Driver API, which will allow us to perform all of the necessary operations needed for basic GPU usageincluding GPU initialization, memory allocation/transfers/deallocation, kernel launching, and context creation/synchronization/destruction. This is a very powerful piece of knowledge; it will allow us to use our GPU without going through PyCUDA, and also without writing any cumbersome host-side C-function wrappers.

We will now write a small module that will act as a wrapper library for the CUDA Driver API. Let's talk about what this means for a minute. The Driver API is slightly different and a little more technical than the CUDA Runtime API, the...