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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

Using CUDA streams to overlay operations

In Chapter 5 we had an introduction to CUDA streams, but now it’s time to learn how to use them in practice. CUDA streams are a powerful feature in NVIDIA’s CUDA programming model that allow us to execute multiple operations concurrently, increasing the throughput of GPU workloads. And this sounds confusing because we know that GPUs have a large number of processing cores and that is why they execute instructions concurrently, right? Well, that is a part of the story, but we also have another level of parallelism to explore. By properly leveraging streams, we can overlap memory transfers and kernel executions to maximize the efficiency of the hardware. This is possible because most consumer-grade GPUs have three asynchronous engines: one for kernel execution and two for memory transfers (one for host-to-device and one for device-to-host). We will discuss how to best utilize these resources given memory bandwidth limitations.

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GPU Programming with C++ and CUDA
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