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)

Events

Events are objects that exist on the GPU, whose purpose is to act as milestones or progress markers for a stream of operations. Events are generally used to provide measure time duration on the device side to precisely time operations; the measurements we have been doing so far have been with host-based Python profilers and standard Python library functions such as time. Additionally, events they can also be used to provide a status update for the host as to the state of a stream and what operations it has already completed, as well as for explicit stream-based synchronization.

Let's start with an example that uses no explicit streams and uses events to measure only one single kernel launch. (If we don't explicitly use streams in our code, CUDA actually invisibly defines a default stream that all operations will be placed into).

Here, we will use the same useless...