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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

An overview of GPU architecture

After all that cooking, it's time for a change. Let's talk about GPUs.First, let me say that I’ve decided to explain GPUs first before comparing them with CPUs. I’m doing this on the assumption that you're already somewhat familiar with the (basic) architecture of a modern CPU.GPUs were originally thought to accelerate the output of processing graphics, since modern computer usage takes place almost exclusively in graphical environments. This differs from computing in the past, where the character-based interfaces that were used weren't graphically demanding. However, a shift occurred when it was noticed that a processing unit that was capable of dealing with the computations necessary for computer graphics could also be used for anything that could be expressed in terms of matrix computations, which is what linear algebra is all about.In the next chapter, we're going to focus specifically on NVIDIA GPUs...

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GPU Programming with C++ and CUDA
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