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)

Vectorized data types and memory access

We will now look at CUDA's Vectorized Data Types. These are vectorized versions of the standard datatypes, such as int or double, in that they can store multiple values. There are vectorized versions of the 32-bit types of up to size 4 (for example, int2, int3, int4, and float4), while 64-bit variables can only be vectorized to be twice their original size (for example, double2 and long2). For a size 4 vectorized variable, we access each individual element using the C "struct" notation for the members x, y, z, and w, while we use x,y, and z for a 3-member variable and just x and y for a 2-member variable.

These may seem pointless right now, but these datatypes can be used to improve the performance of loading arrays from the global memory. Now, let's do a small test to see how we can load some int4 variables from an...