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)

Machine learning and computer vision

Of course, the elephant in the room of this chapter is machine learning and its fraternal twin computer vision. It goes without saying that machine learning (particularly the subfields of deep neural networks and convolutional neural networks) is what is keeping a roof over NVIDIA CEO Jensen Huang's head these days. (Okay, we admit that was the understatement of the decade...) If you need a reminder as to why GPUs are so applicable and useful in this field, please take another look at Chapter 9, Implementation of a Deep Neural Network. A large number of parallel computations and mathematical operations, as well as the user-friendly mathematical libraries, have made NVIDIA GPUs the hardware backbone of the machine learning industry.

The basics...