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

Profiling with NVTX

With focused profiling, we can profile a limited, specific area by using cudaProfilerStart() and cudaProfilerStop(). However, if we want to analyze functional performance in a complex application, it is limited. For this situation, the CUDA profiler provides timeline annotations via the NVIDIA Tools Extension (NVTX). 

Using NVTX, we can annotate the CUDA code. We can use the NVTX API as follows:

nvtxRangePushA("Annotation");
.. { Range of GPU operations } ..
cudaDeviceSynchronization(); // in case if the target code block is pure kernel calls
nvtxRangePop();

As you can see, we can define a range as a group of codes and annotate that range manually. Then, the CUDA profiler provides a timeline trace of the annotation so that we can measure the execution time of code blocks. One drawback of this is that the NVTX APIs are host functions...