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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

Hello Again, but in Parallel

The objective of using GPUs is to exploit parallelism to accelerate the execution of our programs. For that, we must understand the parallel model that GPUs make available. In this chapter we’ll first learn about the SIMD execution model and discuss a simple program to test number primality in parallel, but using an approach which makes it almost like a CPU application. Then we will learn more about moving data to and from the GPU, since its memory is separated from the main system. The chapter closes with a section presenting a simple program that fully benefits from the use of GPUs.

Thus in this chapter we’re going to cover the following main topics:

  • SIMD execution model
  • Primality test with a not-so-parallel program
  • Moving memory
  • Vector addition in full parallel
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GPU Programming with C++ and CUDA
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