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)

Inline PTX assembly

We will now scratch the surface of writing PTX (Parallel Thread eXecution) Assembly language, which is a kind of a pseudo-assembly language that works across all Nvidia GPUs, which is, in turn, compiled by a Just-In-Time (JIT) compiler to the specific GPU's actual machine code. While this obviously isn't intended for day-to-day usage, it will let us work at an even a lower level than C if necessary. One particular use case is that you can easily disassemble a CUDA binary file (a host-side executable/library or a CUDA .cubin binary) and inspect its PTX code if no source code is otherwise available. This can be done with the cuobjdump.exe -ptx cuda_binary command in both Windows and Linux.

As stated previously, we will only cover some of the basic usages of PTX from within CUDA-C, which has a particular syntax and usage which is similar to that of...