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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

Using existing libraries and frameworks

The code we have used in previous chapters helped us understand how to use the GPU, and you may wonder whether libraries already exist that offer the most optimized version of, let’s say, matrix multiplication. And this is exactly the case: there are. Thankfully, we don’t need to reinvent the wheel, because CUDA already provides a suite of mature, optimized libraries and frameworks designed to offer efficient implementations for common computing tasks, drastically reducing the development effort for our applications. The complete list of available libraries can be found at https://developer.nvidia.com/gpu-accelerated-libraries.

Those libraries target specific categories of problems, and although they are numerous, we will focus on two libraries that are similar to what we have worked on in previous chapters. This will enable us to compare our previous development efforts with the use of the libraries. The first library is cuBLAS...

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GPU Programming with C++ and CUDA
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