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Hands-On GPU Programming with Python and CUDA

You're reading from   Hands-On GPU Programming with Python and CUDA Explore high-performance parallel computing with CUDA

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993913
Length 310 pages
Edition 1st Edition
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Author (1):
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Dr. Brian Tuomanen Dr. Brian Tuomanen
Author Profile Icon Dr. Brian Tuomanen
Dr. Brian Tuomanen
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Table of Contents (15) Chapters Close

Preface 1. Why GPU Programming? FREE CHAPTER 2. Setting Up Your GPU Programming Environment 3. Getting Started with PyCUDA 4. Kernels, Threads, Blocks, and Grids 5. Streams, Events, Contexts, and Concurrency 6. Debugging and Profiling Your CUDA Code 7. Using the CUDA Libraries with Scikit-CUDA 8. The CUDA Device Function Libraries and Thrust 9. Implementation of a Deep Neural Network 10. Working with Compiled GPU Code 11. Performance Optimization in CUDA 12. Where to Go from Here 13. Assessment 14. Other Books You May Enjoy

Fast Fourier transforms with cuFFT

Now let's look at how we can do some basic fast Fourier transforms (FFT) with cuFFT. First, let's briefly review what exactly a Fourier transform is. If you have taken an advanced Calculus or Analysis class, you might have seen the Fourier transform defined as an integral formula, like so:

What this does is take f as a time domain function over x. This gives us a corresponding frequency domain function over "ξ". This turns out to be an incredibly useful tool that touches virtually all branches of science and engineering.

Let's remember that the integral can be thought of as a sum; likewise, there is a corresponding discrete, finite version of the Fourier Transform called the discrete Fourier transform (DFT). This operates on vectors of a finite length and allows them to be analyzed or modified in the frequency...

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