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

Debugging a CUDA application with Nsight Visual Studio Edition

For Windows application developers, the CUDA Toolkit provides Nsight Visual Studio Edition, which enables GPU computing in Visual Studio. This tool works as an extension of Visual Studio, but you can build, debug, profile, and trace GPU applications along with the host. If your working platform is not Windows, the contents in this section won't be applicable, so you can skip it.

The CUDA debugger allows us to monitor the local values on a GPU kernel for each CUDA thread. Like normal host debugging, you can set breakpoints in the kernel code and trigger them. You can also place conditions such as other normal breakpoints. With this feature, you can trigger breakpoints for a specific CUDA thread index and review their local variables.

This tool can be installed along with the CUDA Toolkit. You can obtain the...