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

Asynchronous programming in OpenACC

In order to achieve better performance for merging parallel code, we will make use of a concept called blocking. Blocking basically means that, rather than transferring the whole input and output arrays in one shot, we can create blocks of the array which can be transferred and operated in parallel. The following diagram demonstrates creating blocks and overlapping data transfers with the kernel execution:

 

The preceding diagram shows that different blocks are transferred and the kernel execution of these blocks can be independent of each block. In order for this to happen, we need the data transfer commands and kernel calls to be fired and executed asynchronously. In order to achieve blocking, we will be introducing more directives/clauses in this section: the structured/unstructured data directive and async clause. We will showcase...