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

Visual profiling against the remote machine

The NVIDIA Visual Profiler also can profile remote applications. This feature eases the profiling task when it comes to remote application development, especially when you develop your application on the server-side.

There are several ways of using visual profilers, as follows:

  • Profiling on the host with the host CUDA application
  • By collecting profile data using the nvprof CLI on the target side, copying the file to the host, and opening it using the Visual Profiler
  • Profiling the application on the target platform using the host machine

Visual profiling directly in the host machine is convenient and can save development time. Also, remote profiling provides the same user experience that profiling a GPU application on a host machine does. One exception is that we should establish a remote connection. Another benefit OS...