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

Low/mixed precision operations

Mixed precision is a technique for exploring low-precision, and obtains a high accuracy result. This technique computes core operations with low precision and generates output with high-precision operations. Low precision operation computation has the benefits of reduced memory bandwidth and higher computing throughput compared with high-precision computing. If low precision suffices to get target accuracy from an application with high precision, this technique can benefit performance with this trade-off. NVIDIA Developer Blog introduces this programmability: https://devblogs.nvidia.com/mixed-precision-programming-cuda-8.

In these circumstances, CUDA extends its supports to low-precision tools lower than 32-bit data types, such as 8/16-bit integers (INT8/INT16) and 16-bit floating points (FP16). For those low-precision data types, a GPU can use...