Book Image

Learn CUDA Programming

By : Jaegeun Han, Bharatkumar Sharma
Book Image

Learn CUDA Programming

By: Jaegeun Han, Bharatkumar Sharma

Overview of this book

<p>Compute Unified Device Architecture (CUDA) is NVIDIA's GPU computing platform and application programming interface. It's designed to work with programming languages such as C, C++, and Python. With CUDA, you can leverage a GPU's parallel computing power for a range of high-performance computing applications in the fields of science, healthcare, and deep learning. </p><p> </p><p>Learn CUDA Programming will help you learn GPU parallel programming and understand its modern applications. In this book, you'll discover CUDA programming approaches for modern GPU architectures. You'll not only be guided through GPU features, tools, and APIs, you'll also learn how to analyze performance with sample parallel programming algorithms. This book will help you optimize the performance of your apps by giving insights into CUDA programming platforms with various libraries, compiler directives (OpenACC), and other languages. As you progress, you'll learn how additional computing power can be generated using multiple GPUs in a box or in multiple boxes. Finally, you'll explore how CUDA accelerates deep learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). </p><p> </p><p>By the end of this CUDA book, you'll be equipped with the skills you need to integrate the power of GPU computing in your applications.</p>
Table of Contents (18 chapters)
Title Page
Dedication

Technical requirements

This chapter recommends using an NVIDIA GPU card later than Pascal architecture. In other words, your GPU's compute capability should be equal to or greater than 60. If you are unsure of your GPU's architecture, please visit NVIDIA's GPU site at https://developer.nvidia.com/cuda-gpus, and confirm your GPU's compute capability.

Sample code was developed and tested with 10.1 when we wrote this book. In general, it is recommended to use the latest CUDA version if applicable.

In this chapter, we'll perform CUDA programming by profiling the code. If your GPU architecture is Turing, it is recommended to install Nsight Compute to profile the code. It is free, and you can download it from https://developer.nvidia.com/nsight-compute. When we wrote this book, it was a transition moment of the profiler. You can learn about its basic usage in...