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A Simple and Efficient FFT Implementation in C++:
Part I


This article describes a new efficient implementation of the Cooley-Tukey fast Fourier transform (FFT) algorithm using C++ template metaprogramming. Thank to the recursive nature of the FFT, the source code is more readable and faster than the classical implementation. The efficiency is proved by performance benchmarks on different platforms.

Introduction

Fast Fourier Transformation (FFT) is not only a fast method to compute digital Fourier transformation (DFT)—having a complexity O(Nlog(N)) (where N must be power of 2, N=2P), it is a way to linearize many kinds of real mathematical problems of nonlinear complexity using the idiom "divide and conquer."

The discrete Fourier transform fn of the N points signal xn is defined as a sum:

Example 1.

where i is the complex unity. Put simply, the formula says that an algorithm for the computing of the transform will require O(N2) operations. But the Danielson-Lanczos Lemma (1942), using properties of the complex roots of unity g, gave a wonderful idea to construct the Fourier transform recursively (Example 1).

Example 2.

where fke denotes the k-th component of the Fourier transform of length N/2 formed from the even components of the original xj, while fko is the corresponding transform formed from the odd components. Although k in the last line of Example 2 varies from 0 to N-1, the transforms fke and fko are periodic in k with length N/2. The same formula applied to the transforms fke and fke reduces the problem to the transforms of length N/4 and so on. It means, if N is a power of 2, the transform will be computed with a linear complexity O(Nlog(N)). More information on the mathematical background of the FFT and advanced algorithms, which are not limited to N=2P, can be found in many books, e.g. [3,4].

I would like to start with the simplest recursive form of the algorithm, that follows directly from the relation in Example 2:

FFT(x) {
  n=length(x);
  if (n==1) return x;
  m = n/2;
  X = (x_{2j})_{j=0}^{m-1};
  Y = (x_{2j+1})_{j=0}^{m-1};
  X = FFT(X);
  Y = FFT(Y);
  U = (X_{k mod m})_{k=0}^{n-1};
  V = (g^{-k}Y_{k mod m})_{k=0}^{n-1};
  return U+V;
}

This algorithm should give only a first impression of the FFT construction. The FFT(x) function is called twice recursively on the even and odd elements of the source data. After that some transformation on the data is performed. The recursion ends if the data length becomes 1.

This recursion form is instructive, but the overwhelming majority of FFT implementations use a loop structure first achieved by Cooley and Tukey [2] in 1965. The Cooley-Tukey algorithm uses the fact that if the elements of the original length N signal x are given a certain "bit-scrambling" permutation, then the FFT can be carried out with convenient nested loops. The scrambling intended is reverse-binary reindexing, meaning that xj gets replaced by xk, where k is the reverse-binary representation of j. For example, for data length N=25, the element x5 must be exchanged with x{20}, because the binary reversal of 5=001012 is 101002=20. The implementation of this data permutation will be considered later, since it has been a minor part of the whole FFT. A most important observation is that the Cooley-Tukey scheme actually allows the FFT to be performed in place, that is, the original data x is replaced, element by element, with the FFT values. This is an extremely memory-efficient way to proceed with large data. Listing One shows the classical implementation of the Cooley-Tukey algorithm from Numerical Recipes in C++ [5], p.513.

Listing One

void four1(double* data, unsigned long nn)
{
    unsigned long n, mmax, m, j, istep, i;
    double wtemp, wr, wpr, wpi, wi, theta;
    double tempr, tempi;

    // reverse-binary reindexing
    n = nn<<1;
    j=1;
    for (i=1; i<n; i+=2) {
        if (j>i) {
            swap(data[j-1], data[i-1]);
            swap(data[j], data[i]);
        }
        m = nn;
        while (m>=2 && j>m) {
            j -= m;
            m >>= 1;
        }
        j += m;
    };

    // here begins the Danielson-Lanczos section
    mmax=2;
    while (n>mmax) {
        istep = mmax<<1;
        theta = -(2*M_PI/mmax);
        wtemp = sin(0.5*theta);
        wpr = -2.0*wtemp*wtemp;
        wpi = sin(theta);
        wr = 1.0;
        wi = 0.0;
        for (m=1; m < mmax; m += 2) {
            for (i=m; i <= n; i += istep) {
                j=i+mmax;
                tempr = wr*data[j-1] - wi*data[j];
                tempi = wr * data[j] + wi*data[j-1];

                data[j-1] = data[i-1] - tempr;
                data[j] = data[i] - tempi;
                data[i-1] += tempr;
                data[i] += tempi;
            }
            wtemp=wr;
            wr += wr*wpr - wi*wpi;
            wi += wi*wpr + wtemp*wpi;
        }
        mmax=istep;
    }
}

The initial signal is stored in the array data of length 2*nn, where each even element corresponds to the real part and each odd element to the imaginary part of a complex number.


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