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)

Streams, Events, Contexts, and Concurrency

In the prior chapters, we saw that there are two primary operations we perform from the host when interacting with the GPU:

  • Copying memory data to and from the GPU
  • Launching kernel functions

We know that within a single kernel, there is one level of concurrency among its many threads; however, there is another level of concurrency over multiple kernels and GPU memory operations that are also available to us. This means that we can launch multiple memory and kernel operations at once, without waiting for each operation to finish. However, on the other hand, we will have to be somewhat organized to ensure that all inter-dependent operations are synchronized; this means that we shouldn't launch a particular kernel until its input data is fully copied to the device memory, or we shouldn't copy the output data of a launched kernel...