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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

Writing your own code

Creating GPU-accelerated programs is different from sequential CPU code, but there is an interesting software engineering aspect that is constant in both worlds: we must consider when we should write our own code and when to use an existing library. In large, enterprise-level, projects we try to avoid reinventing the wheel as much as possible, for many reasons. One of them we already discussed on the previous section: a library tends to have a dedicated team that is specialized in the library’s domain. This means that its business is the library, while most of the time our business is an application for a specific case that will make use of the library to achieve our results. Another reason is that when we decide to create code that competes with libraries we may take on responsibility for maintaining that code, and our ability to discharge that responsibility effectively can be affected by many factors. We may have less time to correct a specific bug...

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GPU Programming with C++ and CUDA
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