How to Implement a Fast Fourier Transform (FFT) in C++

Introduction

The Fast Fourier Transform (FFT) is a highly efficient algorithm for computing the Discrete Fourier Transform (DFT) and its inverse. FFT is widely used in signal processing, image analysis, and many other fields. Understanding and implementing FFT in C++ can significantly enhance the performance of your applications that require frequency analysis.

This tutorial aims to provide a comprehensive guide on how to implement the FFT in C++. While it is targeted at non-beginners, it assumes you have a basic understanding of complex numbers, C++ syntax, and fundamental algorithms. We’ll start with a brief overview of the FFT and its mathematical foundation before diving into the implementation details.

1. Overview of Fourier Transform

The Fourier Transform is a mathematical operation that transforms a function of time (or space) into a function of frequency. The Discrete Fourier Transform (DFT) is a specific type of Fourier Transform applied to discrete data sets. The DFT is defined as:

$X(k) = \sum_{n=0}^{N-1} x(n) \cdot e^{-i2\pi kn/N}$

Where:

$X(k)$ is the DFT of the input sequence $x(n)$ .
$N$ is the number of points in the input sequence.
$k$ is the frequency index.
$i$ is the imaginary unit.

The Fast Fourier Transform (FFT) is an algorithm that efficiently computes the DFT, reducing the computational complexity from $O(N^2)$ to $O(N \log N)$ .

2. Mathematical Foundation of FFT

The FFT algorithm leverages the symmetry and periodicity properties of the DFT. The most common FFT algorithm is the Cooley-Tukey algorithm, which recursively breaks down a DFT of any composite size $N$ into many smaller DFTs.

Cooley-Tukey Algorithm

The Cooley-Tukey algorithm splits the DFT into smaller parts by separating the even and odd indexed elements of the input sequence. This divide-and-conquer approach reduces the overall complexity.

For an input sequence $x$ of length $N$ :

$X(k) = \sum_{n=0}^{N/2-1} x(2n) \cdot e^{-i2\pi kn/(N/2)} + \sum_{n=0}^{N/2-1} x(2n+1) \cdot e^{-i2\pi k(2n+1)/N}$

This can be rewritten as:

$X(k) = \text{DFT}_{N/2}(x_{\text{even}}) + e^{-i2\pi k/N} \cdot \text{DFT}_{N/2}(x_{\text{odd}})$

Where:

$x_{\text{even}}$ is the sequence of even-indexed elements.
$x_{\text{odd}}$ is the sequence of odd-indexed elements.

This decomposition continues recursively until the base case of the DFT of length 1 is reached.

3. Recursive Implementation of FFT

Let’s implement the FFT using a recursive approach in C++.

Step-by-Step Implementation

Include necessary headers:

#include <iostream>
#include <vector>
#include <complex>
#include <cmath>Code language: C++ (cpp)

Define the FFT function:

using namespace std;
using Complex = complex<double>;
using CArray = vector<Complex>;

const double PI = acos(-1);

void fft(CArray &x) {
    const size_t N = x.size();
    if (N <= 1) return;

    // Divide
    CArray even(N / 2);
    CArray odd(N / 2);
    for (size_t i = 0; i < N / 2; ++i) {
        even[i] = x[i * 2];
        odd[i] = x[i * 2 + 1];
    }

    // Conquer
    fft(even);
    fft(odd);

    // Combine
    for (size_t k = 0; k < N / 2; ++k) {
        Complex t = polar(1.0, -2 * PI * k / N) * odd[k];
        x[k] = even[k] + t;
        x[k + N / 2] = even[k] - t;
    }
}Code language: C++ (cpp)

Implement the main function to test FFT:

int main() {
    const size_t N = 8;
    CArray data(N);

    // Sample data
    for (size_t i = 0; i < N; ++i) {
        data[i] = Complex(i, 0);
    }

    // Perform FFT
    fft(data);

    // Output the results
    for (size_t i = 0; i < N; ++i) {
        cout << data[i] << endl;
    }

    return 0;
}Code language: C++ (cpp)

Explanation

Divide: Split the input sequence into even and odd indexed elements.
Conquer: Recursively apply the FFT to the smaller sequences.
Combine: Combine the results of the smaller sequences to form the final result.

4. Iterative Implementation of FFT

The recursive approach can be memory-intensive and may lead to stack overflow for large inputs. An iterative implementation of FFT can address these issues.

Step-by-Step Implementation

Include necessary headers:

#include <iostream>
#include <vector>
#include <complex>
#include <cmath>Code language: C++ (cpp)

Define the FFT function:

using namespace std;
using Complex = complex<double>;
using CArray = vector<Complex>;

const double PI = acos(-1);

void fft(CArray &x) {
    const size_t N = x.size();
    if (N <= 1) return;

    // Bit-reversed addressing permutation
    size_t j = 0;
    for (size_t i = 1; i < N; ++i) {
        size_t bit = N >> 1;
        while (j & bit) {
            j ^= bit;
            bit >>= 1;
        }
        j ^= bit;

        if (i < j) {
            swap(x[i], x[j]);
        }
    }

    // Iterative FFT
    for (size_t len = 2; len <= N; len <<= 1) {
        double angle = -2 * PI / len;
        Complex wlen(cos(angle), sin(angle));
        for (size_t i = 0; i < N; i += len) {
            Complex w(1);
            for (size_t j = 0; j < len / 2; ++j) {
                Complex u = x[i + j];
                Complex v = x[i + j + len / 2] * w;
                x[i + j] = u + v;
                x[i + j + len / 2] = u - v;
                w *= wlen;
            }
        }
    }
}Code language: C++ (cpp)

Implement the main function to test FFT:

int main() {
    const size_t N = 8;
    CArray data(N);

    // Sample data
    for (size_t i = 0; i < N; ++i) {
        data[i] = Complex(i, 0);
    }

    // Perform FFT
    fft(data);

    // Output the results
    for (size_t i = 0; i < N; ++i) {
        cout << data[i] << endl;
    }

    return 0;
}Code language: C++ (cpp)

Explanation

Bit-reversed addressing permutation: This step reorders the input sequence to ensure the FFT computation follows the correct order.
Iterative FFT: This loop iteratively applies the FFT algorithm, reducing the complexity by computing the results in place.

5. Optimization Techniques

To further optimize the FFT implementation, consider the following techniques:

In-Place Computation – Perform the FFT computation in place to reduce memory usage. The iterative implementation already achieves this by modifying the input sequence directly.
Loop Unrolling – Unroll loops to reduce the overhead of loop control and increase performance. However, this may increase the code size and complexity.
SIMD Instructions – Utilize Single Instruction, Multiple Data (SIMD) instructions to parallelize operations. Libraries like Intel’s Math Kernel Library (MKL) can provide highly optimized FFT implementations.
Cache Optimization – Optimize data access patterns to reduce cache misses. Ensure the data is accessed sequentially to take advantage of cache locality.
Multithreading – Parallelize the computation using multithreading. This can be achieved using libraries like OpenMP or C++’s <thread> library.

6. Example Application

Let’s apply our FFT implementation to a real-world example: analyzing the frequency components of a signal.

Step-by-Step Implementation

Generate a sample signal:

#include <iostream>
#include <vector>
#include <complex>
#include <cmath>

using namespace std;
using Complex = complex<double>;
using CArray = vector<Complex>;

const double PI = acos(-1);

void fft(CArray &x) {
    const size_t N = x.size();
    if (N <= 1) return;

    // Bit

-reversed addressing permutation
    size_t j = 0;
    for (size_t i = 1; i < N; ++i) {
        size_t bit = N >> 1;
        while (j & bit) {
            j ^= bit;
            bit >>= 1;
        }
        j ^= bit;

        if (i < j) {
            swap(x[i], x[j]);
        }
    }

    // Iterative FFT
    for (size_t len = 2; len <= N; len <<= 1) {
        double angle = -2 * PI / len;
        Complex wlen(cos(angle), sin(angle));
        for (size_t i = 0; i < N; i += len) {
            Complex w(1);
            for (size_t j = 0; j < len / 2; ++j) {
                Complex u = x[i + j];
                Complex v = x[i + j + len / 2] * w;
                x[i + j] = u + v;
                x[i + j + len / 2] = u - v;
                w *= wlen;
            }
        }
    }
}

int main() {
    const size_t N = 1024;
    CArray signal(N);

    // Generate a sample signal: a sum of two sine waves
    for (size_t i = 0; i < N; ++i) {
        signal[i] = Complex(sin(2 * PI * 50 * i / N) + 0.5 * sin(2 * PI * 120 * i / N), 0);
    }

    // Perform FFT
    fft(signal);

    // Output the results (magnitude)
    for (size_t i = 0; i < N; ++i) {
        cout << abs(signal[i]) << endl;
    }

    return 0;
}Code language: PHP (php)

Explanation

Generate a sample signal: Create a signal that is the sum of two sine waves with different frequencies.
Perform FFT: Apply the FFT to the signal.
Output the results: Print the magnitude of the FFT results to analyze the frequency components.

7. Conclusion

In this tutorial, we covered the implementation of the Fast Fourier Transform (FFT) in C++. We started with the mathematical foundation of the FFT, followed by recursive and iterative implementations. We also discussed optimization techniques to enhance the performance of the FFT.

Implementing the FFT in C++ provides a deep understanding of the algorithm and its performance characteristics. This knowledge is essential for optimizing applications that require frequency analysis.

By applying these techniques, you can efficiently analyze and process signals, images, and other data in your applications.

Introduction

1. Overview of Fourier Transform

2. Mathematical Foundation of FFT

Cooley-Tukey Algorithm

3. Recursive Implementation of FFT

Step-by-Step Implementation

Explanation

4. Iterative Implementation of FFT

Step-by-Step Implementation

Explanation

5. Optimization Techniques

6. Example Application

Step-by-Step Implementation

Explanation

7. Conclusion

Further Reading

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