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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

Passing numpy arrays to your library

In our first integration we passed in numpy arrays to our library using ctypes, and using numpy is essentially ubiquitous in scientific computing with Python. It provides efficient, contiguous memory storage, vectorized operations, and uses accelerated CPU-based libraries. By integrating the C API of numpy into our C extension, we will be able to use the data that has been created and preprocessed on the Python side to execute further calculations inside our GPU. This means we can put together the best of two worlds and but retain the Python feel. In many cases, the performance gains can be orders of magnitude higher than pure Python implementations, especially for operations involving large arrays.

We will change a little bit of our previous code to make the numpy API available. The first change is to import it in our vector_add_np_wrapper.c file with:

#include <numpy/arrayobject.h>

numpy as np

Python allows us...

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GPU Programming with C++ and CUDA
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