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GPU Programming with C++ and CUDA

GPU Programming with C++ and CUDA

By : Paulo Motta
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GPU Programming with C++ and CUDA

GPU Programming with C++ and CUDA

By: Paulo Motta

Overview of this book

Written by Paulo Motta, a senior researcher with decades of experience, this comprehensive GPU programming book is an essential guide for leveraging the power of parallelism to accelerate your computations. The first section introduces the concept of parallelism and provides practical advice on how to think about and utilize it effectively. Starting with a basic GPU program, you then gain hands-on experience in managing the device. This foundational knowledge is then expanded by parallelizing the program to illustrate how GPUs enhance performance. The second section explores GPU architecture and implementation strategies for parallel algorithms, and offers practical insights into optimizing resource usage for efficient execution. In the final section, you will explore advanced topics such as utilizing CUDA streams. You will also learn how to package and distribute GPU-accelerated libraries for the Python ecosystem, extending the reach and impact of your work. Combining expert insight with real-world problem solving, this book is a valuable resource for developers and researchers aiming to harness the full potential of GPU computing. The blend of theoretical foundations, practical programming techniques, and advanced optimization strategies it offers is sure to help you succeed in the fast-evolving field of GPU programming.
Table of Contents (17 chapters)
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1
Understanding Where We Are Heading
6
Bring It On!
10
Moving Forward
15
Other Books You May Enjoy
16
Index
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Index

A

Amdahl’s Law 101

asynchronous data transfers 86-89

asynchronous memory transfers 185

B

block 36, 82, 83

size 84

blocking call 68

breakpoints 173

C

C++ library 198

creating 200-203

integrating, with Python 198, 199

C++ Standard Template Library (STL) 218

CI/CD pipeline 230

CMake 67

code

GTest, using with 228-231

Pytest, using with 232-235

writing 222-225

code optimization 126

compiler directives 155

conditional breakpoints 176

context switching 7

convolution 120

sensor data, processing 120-122

CPUs 16

critical path 104

Ctypes

using 203-205

cuBLAS 218

matrices, multiplying 218-220

CUDA Capability specification 42

CUDA code

debugging, with VS Code 170-179

CUDA cores 11

CUDA Events 68

cuda-gdb 170

reference link 170

CUDA streams

limits, measuring 182, 183

matrices, multiplying 183-193

operations, overlaying...

Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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GPU Programming with C++ and CUDA
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