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)

Using the NVIDIA nvprof profiler and Visual Profiler

We will end with a brief overview of the command-line Nvidia nvprof profiler. In contrast to the Nsight IDE, we can freely use any Python code that we have written—we won't be compelled here to write full-on, pure CUDA-C test function code.

We can do a basic profiling of a binary executable program with the nvprof program command; we can likewise profile a Python script by using the python command as the first argument, and the script as the second as follows: nvprof python program.py. Let's profile the simple matrix-multiplication CUDA-C executable program that we wrote earlier, with nvprof matrix_ker:

We see that this is very similar to the output of the Python cProfiler module that we first used to analyze a Mandelbrot algorithm way back in Chapter 1, Why GPU Programming?—only now, this exclusively...