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)

Threads, blocks, and grids

So far in this book, we have been taking the term thread for granted. Let's step back for a moment and see exactly what this means—a thread is a sequence of instructions that is executed on a single core of the GPUcores and threads should not be thought of as synonymous! In fact, it is possible to launch kernels that use many more threads than there are cores on the GPU. This is because, similar to how an Intel chip may only have four cores and yet be running hundreds of processes and thousands of threads within Linux or Windows, the operating system's scheduler can switch between these tasks rapidly, giving the appearance that they are running simultaneously. The GPU handles threads in a similar way, allowing for seamless computation over tens of thousands of threads.

Multiple threads are executed on the GPU in abstract units...