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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

Technical requirements

In this chapter, we are going to work on our computer to configure the environment in order to use our GPU.For the operating system, we will be using Linux. Although any distribution could be used, it's best to select one that is officially supported by NVIDIA. We have a lot of choice when it comes to open source distributions for Linux, but we will go with Ubuntu as it is easy to find and install, with numerous online posts addressing common issues. It is also officially supported by NVIDIA. At the time of writing, Ubuntu 24.04 LTS is available, but it is not yet listed as supported by the NVIDIA installer for CUDA 12.4 (the current version). Instead, we have Ubuntu 20.04 and 22.04, either of which is suitable for our purposes.We need to have a computer with an NVIDIA GPU: it could be a virtual machine on your preferred cloud service, a laptop, or a desktop, but to take full advantage of the information presented in this book, it is mandatory...

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