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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

Summary

In this chapter we learnt how to use a new tool in our IDE to allow us to debug our code in a visual way. This is a very handy tool to be proficient in, for many will be the times when you must find the proverbial needle in the haystack. After that we returned to a topic first introduced in Chapter 5, but now we were able to explore many aspects of the use of CUDA streams and also the impact of data transfer size on performance. We learned about the relations between chunk size, transfer size and the number of data partitions.

By using our profiler from Chapter 7 we were able to visualize the overlapping of memory transfers and computations. This not only shows that the new technique really works, but also enables us to leverage one more use of NVIDIA Nsight Compute which we learnt about previously.

We concluded the chapter by talking about the use of multiple GPUs, even though this is a more unusual setup.

In the next chapter we will learn how to expose our code...

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GPU Programming with C++ and CUDA
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