Jaegeun Han - Learn CUDA Programming: A beginners guide to GPU programming and parallel computing with CUDA 10.x and C/C++
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- Book:Learn CUDA Programming: A beginners guide to GPU programming and parallel computing with CUDA 10.x and C/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 Python
Key Features- Learn parallel programming principles and practices and performance analysis in GPU computing
- Get to grips with distributed multi GPU programming and other approaches to GPU programming
- Understand how GPU acceleration in deep learning models can improve their performance
Compute Unified Device Architecture (CUDA) is NVIDIAs GPU computing platform and application programming interface. Its designed to work with programming languages such as C, C++, and Python. With CUDA, you can leverage a GPUs 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, youll discover CUDA programming approaches for modern GPU architectures. Youll not only be guided through GPU features, tools, and APIs, youll 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, youll learn how additional computing power can be generated using multiple GPUs in a box or in multiple boxes. Finally, youll explore how CUDA accelerates deep learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
By the end of this CUDA book, youll be equipped with the skills you need to integrate the power of GPU computing in your applications.
What you will learn- Understand general GPU operations and programming patterns in CUDA
- Uncover the difference between GPU programming and CPU programming
- Analyze GPU application performance and implement optimization strategies
- Explore GPU programming, profiling, and debugging tools
- Grasp parallel programming algorithms and how to implement them
- Scale GPU-accelerated applications with multi-GPU and multi-nodes
- Delve into GPU programming platforms with accelerated libraries, Python, and OpenACC
- Gain insights into deep learning accelerators in CNNs and RNNs using GPUs
This 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.
Table of Contents- Introduction to CUDA programming
- CUDA Memory Management
- CUDA Thread Programming: Performance Indicators and Optimization Strategies
- CUDA Kernel Execution model and optimization strategies
- CUDA Application Monitoring and Debugging
- Scalable Multi-GPU programming
- Parallel Programming Patterns in CUDA
- GPU accelerated Libraries and popular programming languages
- GPU programming using OpenACC
- Deep Learning Acceleration with CUDA
- Appendix
Jaegeun Han: author's other books
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Bharatkumar Sharma
Copyright 2019 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author(s), nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Commissioning Editor: Richa Tripathi
Acquisition Editor: Alok Dhuri
Content Development Editor: Digvijay Bagul
Senior Editor: Afshaan Khan
Technical Editor: Romy Dias
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Indexer: Priyanka Dhadke
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First published: September 2019
Production reference: 1270919
Published by Packt Publishing Ltd.
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ISBN 978-1-78899-624-2
www.packt.com
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Jaegeun Han is currently working as a solutions architect at NVIDIA, Korea. He has around 9 years' experience and he supports consumer internet companies in deep learning. Before NVIDIA, he worked in system software and parallel computing developments, and application development in medical and surgical robotics fields. He obtained a master's degree in CSE from Seoul National University.
Bharatkumar Sharma obtained a master's degree in information technology from the Indian Institute of Information Technology, Bangalore. He has around 10 years of development and research experience in the domains of software architecture and distributed and parallel computing. He is currently working with NVIDIA as a senior solutions architect, South Asia.
Christian Stehno studied computer science, receiving his diploma from Oldenburg University, Germany, in 2000. Since then, he's worked in different fields of computer science, first as a researcher in theoretical computer science at an academic institution, before subsequently switching to embedded system design at a research institute. In 2010, he started his own company, CoSynth, which develops embedded systems and intelligent cameras for industrial automation. In addition, he is a long-time member of the Irrlicht 3D engine developer team.
Minseok Lee is a developer technology engineer at NVIDIA. He works on parallelizing and optimizing scientific and AI applications for CPU-GPU heterogeneous systems. Before joining NVIDIA, he was a C++-based library designer/developer and received an M.Eng in computer science by working on utilizing GPU architectures more efficiently.
Aidan Temple is a software engineer and lead developer at Nanotek. He has recently graduated with honors from Glasgow Caledonian University, where he received a BSc in computer games software development.
While at university, Aidan also undertook a research degree outlining the benefits of implementing GUI-based game frameworks by means of parallel processing through the utilization of NVIDIA's CUDA architecture. He also received an IGDA scholarship.
Prior to his time at university, he studied computer games development at James Watt College of Further and Higher Education. Due to his excellent understanding and demonstration of game development and design methodologies, Aidan graduated from James Watt College with a distinction in his field.
, Introduction to CUDA Programming , demystifies some of the myths around GPU and CUDA, and introduces the CUDA programming model with a Hello World CUDA program.
, CUDA Memory Management , introduces the GPU memory hierarchy and how to optimally utilize it with the CUDA APIs.
, CUDA Thread Programming , introduces how threads operate in the GPU, highlighting key metrics on which basis optimizations are performed.
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