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)

Filling in the gaps with CUDA-C

We will now go through the very basics of how to write a full-on CUDA-C program. We'll start small and just translate the fixed version of the little matrix multiplication test program we just debugged in the last section to a pure CUDA-C program, which we will then compile from the command line with NVIDIA's nvcc compiler into a native Windows or Linux executable file (we will see how to use the Nsight IDE in the next section, so we will just be doing this with only a text editor and the command line for now). Again, the reader is encouraged to look at the code we are translating from Python as we go along, which is available as the matrix_ker.py file in the repository.

Now, let's open our favorite text editor and create a new file entitled matrix_ker.cu. The extension will indicate that this is a CUDA-C program, which can be compiled...