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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

Parallelizing with streams

Now we will talk about the second feature that helps improve performance of CUDA programs: streams.

We can think of a stream as a queue in which we enqueue kernel executions and memory transfers, which are then executed sequentially on the GPU in the order in which they were added. There is a default stream which receives index 0, used when we don’t define a specific stream for execution. One important characteristic of the default stream is that it is synchronous, meaning that each operation completes before the next one starts. This simplifies behavior but limits performance improvements.

However, we can define our own non-default streams and execute different operations on different streams so that we end up overlapping memory transfers and computations. Since streams are not tied to any streaming processor or memory channel, anything available will execute the requested operations.

We must keep in mind that the number of streams is...

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