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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

Moving sequential code to the GPU

We learned that moving data to and from the GPU can be costly, and we learned that we can overlay those actions with computation to decrease the time taken to transfer data. However, there are times when we need to perform an intermediate sequential step between two GPU processing phases, and we then have to decide whether to move data out of GPU memory or whether we are going to move our sequential code into the GPU, even though it will not fully utilize the available resources.

Although it may seem a little counterintuitive at first, this is a very legitimate question to ask. It is not a matter of right or wrong, but rather of what will execute fastest and what the associated cost is – even if the cost is maintainability.

One important thing to keep in mind, based on the measurements we observed in Chapter 8, is that typically we can hide the computation time by correctly partitioning the data, in that the total execution time is...

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