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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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Lock Free Chapter
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 learned how to access GPU kernels from Python, which is an important topic that opens up a whole new world for us to explore. We can mix the acceleration that GPU kernels can achieve with the ease of use of Python.

We looked at two distinct approaches to effecting the integration: by using ctypes and by creating a Python extension. The great advantage of the ctypes approach is that it is purely Python-based, and does not involve compiling anything extra. The second approach allows finer grained control, but we must compile the extension to make it available. Two variants of the Python extension approach were discussed, the first involving copying data and the second using numpy internal data pointers directly, which is much faster.

Regardless of the approach taken, we should expend some effort on reusing memory, or we lose the gains that the GPU can provide, as we saw in the performance analysis section.

In the next chapter we discuss where we...

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GPU Programming with C++ and CUDA
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