Learn CUDA Programming

Han Jaegeun Han

eBook (epub), 508 Nombre de pages

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Explore different GPU programming methods using libraries and directives, such as OpenACC, with extension to languages such as C,...

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Explore different GPU programming methods using libraries and directives, such as OpenACC, with extension to languages such as C, C++, and PythonKey FeaturesLearn parallel programming principles and practices and performance analysis in GPU computingGet to grips with distributed multi GPU programming and other approaches to GPU programmingUnderstand how GPU acceleration in deep learning models can improve their performanceBook DescriptionCompute 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.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).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.What you will learnUnderstand general GPU operations and programming patterns in CUDAUncover the difference between GPU programming and CPU programmingAnalyze GPU application performance and implement optimization strategiesExplore GPU programming, profiling, and debugging toolsGrasp parallel programming algorithms and how to implement themScale GPU-accelerated applications with multi-GPU and multi-nodesDelve into GPU programming platforms with accelerated libraries, Python, and OpenACCGain insights into deep learning accelerators in CNNs and RNNs using GPUsWho this book is forThis beginner-level book is for programmers who want to delve into parallel computing, become part of the high-performance computing community and build modern applications. Basic C and C++ programming experience is assumed. For deep learning enthusiasts, this book covers Python InterOps, DL libraries, and practical examples on performance estimation.