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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

Advantages and challenges of GPU programming

So far, we've learned why parallelism matters, considered the various GPU device components, and compared GPUs with CPUs. Now it's time to understand how GPUs can enhance the performance of our solutions and how to overcome the challenges that come with these benefits.Since we've already talked about some of the benefits, let's start with the challenges that come with GPU programming.

GPU challenges

The most obvious challenge is that we can't change the device’s components, so we can't upgrade its memory, for example. Hardware limitations will directly restrict what we can do and how we'll need to break down our data for processing.We also talked about memory transfers, something that can easily become an overhead if we have to move data to and from the device constantly. Typically, we try to move data to the device and compute as much as possible before having...

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GPU Programming with C++ and CUDA
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