GPU Parallel Program Development Using CUDA

✍ Scribed by Tolga Soyata

• Year: 2018
• Tongue: English
• Leaves: 476
• Edition: 1
• Category: Library

✦ Synopsis

This book provides a hands-on, class-tested introduction to CUDA and GPU programming. It begins by introducing CPU programming and the concepts of P-threads, thread programming, multi-tasking, and parallelism, and then interweaves those concepts into an introduction of GPU programming. Using Nvidia's new platform, based on Amazon EC2 and Web GPU, the book uses a standardized architecture, while also exploring other architectures and their differences. The book also covers GPU multi-threading and Global Memory, CUDA atomics and the use of libraries on GPUs. Example applications in image processing, face detection, and tumor detection are also included.

✦ Table of Contents

Series Page......Page 3
Title Page......Page 6
Copyright......Page 7
Dedication......Page 8
Contents......Page 10
List of Figures......Page 20
List of Tables......Page 30
Preface......Page 34
About the Author......Page 36
Part I: Understanding CPU Parallelism......Page 38
1 Introduction to CPU Parallel Programming......Page 40
2 Developing Our First Parallel CPU Program......Page 64
3 Improving Our First Parallel CPU Program......Page 90
4 Understanding the Cores and Memory......Page 116
5 Thread Management and Synchronization......Page 144
Part II: GPU Programming Using CUDA......Page 172
6 Introduction to GPU Parallelism and CUDA......Page 174
7 CUDA Host/Device Programming Model......Page 222
8 Understanding GPU Hardware Architecture......Page 262
9 Understanding GPU Cores......Page 300
10 Understanding GPU Memory......Page 340
11 CUDA Streams......Page 382
Part III: More To Know......Page 418
12 CUDA Libraries......Page 420
13 Introduction to OpenCL......Page 434
14 Other GPU Programming Languages......Page 450
15 Deep Learning Using CUDA......Page 462
Bibliography......Page 472
Index......Page 476