OTFFT is a high-speed FFT library using the Stockham's algorithm and AVX. In addition, C++ template metaprogramming technique is used in OTFFT. And OTFFT is a mixed-radix FFT.

Build Status

It should be able to compile/use the sources using Visual Studio on Windows and GCC on Linux.

CI Server	Status
Travis CI Ubuntu 14.04
GitHub Ubuntu 20.04
Appveyor VS2013

Reasons for the fork

OTFFT is developed by OK Ojisan (Takuya OKAHISA). It's original homepage is http://wwwa.pikara.ne.jp/okojisan/otfft-en/.

The DEWETRON fork uses the original source code with improvements:

• multi CPU ISS builds (AVX2, AVX, SSE2,...)
• unit test coverage
• Use of standard OpenMP

Status

There is a continuous effort to keep the FFT computations in sync with the upstream OTFFT version. Relevant changed from version 11.5 have been merged into this repository.

How to use

Just run cmake to get an appropriate build environment depending on your used build system and operating system.

Complex-to-Complex FFT

    #include "otfft/otfft.h"
    using OTFFT::complex_t;
    using OTFFT::simd_malloc;
    using OTFFT::simd_free;

    void f(int N)
    {
        complex_t* x = (complex_t*) simd_malloc(N*sizeof(complex_t));
        // do something
        OTFFT::FFT fft(N); // creation of FFT object. N is sequence length.
        fft.fwd(x); // execution of transformation. x is input and output
        // do something
        simd_free(x);
}

complex_t is defined as follows.

    struct complex_t
    {
        double Re, Im;

        complex_t() : Re(0), Im(0) {}
        complex_t(double x) : Re(x), Im(0) {}
        complex_t(double x, double y) : Re(x), Im(y) {}
        complex_t(const std::complex<double>& z) : Re(z.real()), Im(z.imag()) {}
        operator std::complex<double>(){ return std::complex(Re, Im); }

    // ...
    };

There are member functions, such as the following.

fwd(x)  -- DFT(with 1/N normalization) x:input/output
fwd0(x) -- DFT(non normalization) x:input/output
fwdu(x) -- DFT(unitary transformation) x:input/output
fwdn(x) -- DFT(with 1/N normalization) x:input/output

inv(x)  -- IDFT(non normalization) x:input/output
inv0(x) -- IDFT(non normalization) x:input/output
invu(x) -- IDFT(unitary transformation) x:input/output
invn(x) -- IDFT(with 1/N normalization) x:input/output

To change the FFT size, do the following.

fft.setup(2 * N);

To use in a multi-threaded environment, we do as follows.

    #include "otfft/otfft.h"
    using OTFFT::complex_t;
    using OTFFT::simd_malloc;
    using OTFFT::simd_free;

    void f(int N)
    {
        complex_t* x = (complex_t*) simd_malloc(N*sizeof(complex_t));
        complex_t* y = (complex_t*) simd_malloc(N*sizeof(complex_t));
        // do someting
        OTFFT::FFT0 fft(N);
        fft.fwd(x, y); // x is input/output. y is work area
        // do something
        fft.inv(x, y);
        // x is input/output. y is work area
        // do someting
        simd_free(y);
        simd_free(x);
    }

Please note that "OTFFT::FFT" was changed to "OTFFT::FFT0".

Real-to-Complex FFT

    #include "otfft/otfft.h"
    using OTFFT::complex_t;
    using OTFFT::simd_malloc;
    using OTFFT::simd_free;

    void f(int N)
    {
        double* x = (double*) simd_malloc(N*sizeof(double));
        complex_t* y = (complex_t*) simd_malloc(N*sizeof(complex_t));
        // do something
        OTFFT::RFFT rfft(N);
        rfft.fwd(x, y); // x is input. y is output
        // do something
        simd_free(y);
        simd_free(x);
        }

N must be an even number. There are member functions, such as the following.

fwd(x, y)  -- DFT(with 1/N normalization) x:input, y:output
fwd0(x,y)  -- DFT(non normalization) x:input, y:output
fwdu(x, y) -- DFT(unitary transformation) x:input, y:output
fwdn(x, y) -- DFT(with 1/N normalization) x:input, y:output

inv(y, x)  -- IDFT(non normalization) y:input, x:output
inv0(y, x) -- IDFT(non normalization) y:input, x:output
invu(y, x) -- IDFT(unitary transformation) y:input, x:output
invn(y, x) -- IDFT(with 1/N normalization) y:input, x:output

inv,inv0,invu,invn will destroy the input.

Discrete Cosine Transformation(DCT-II)

This transformation, orthogonalization is not executed.

    #include "otfft/otfft.h"
    using OTFFT::complex_t;
    using OTFFT::simd_malloc;
    using OTFFT::simd_free;

    void f(int N)
    {
        double* x = (double*) simd_malloc(N*sizeof(double));
        // do something
        OTFFT::DCT dct(N);
        dct.fwd(x); // execution of DCT. x is input and output
        // do something
        simd_free(x);
    }

N must be an even number. There are member functions, such as the following.

fwd(x)  -- DCT(with 1/N normalization) x:input/output
fwd0(x) -- DCT(non normalization) x:input/output
fwdn(x) -- DCT(with 1/N normalization) x:input/output

inv(x) -- IDCT(non normalization) x:input/output
inv0(x) -- IDCT(non normalization) x:input/output
invn(x) -- IDCT(with 1/N normalization) x:input/output

To use in a multi-threaded environment, we do as follows.

    #include "otfft/otfft.h"
    using OTFFT::complex_t;
    using OTFFT::simd_malloc;
    using OTFFT::simd_free;

    void f(int N)
    {
        double* x = (double*) simd_malloc(N*sizeof(double));
        double* y = (double*) simd_malloc(N*sizeof(double));
        complex_t* z = (complex_t*) simd_malloc(N*sizeof(complex_t));
        // do something
        OTFFT::DCT0 dct(N);
        dct.fwd(x, y, z); // x is input/output. y,z are work area
        // do something
        dct.inv(x, y, z); // x is input/output. y,z are work area
        // do somthing
        simd_free(z);
        simd_free(y);
        simd_free(x);
    }

Please note that "OTFFT::DCT" was changed to "OTFFT::DCT0".

Bluestein's FFT

Bluestein's FFT is the FFT of any sequence length. Even if the sequence length is a big prime number, the order of complexity is O(N log N).

    #include "otfft/otfft.h"
    using OTFFT::complex_t;
    using OTFFT::simd_malloc;
    using OTFFT::simd_free;

    void f(int N)
    {
        complex_t* x = (complex_t*) simd_malloc(N*sizeof(complex_t));
        // do something
        OTFFT::Bluestein bst(N);
        bst.fwd(x); // execution of Bluestein's FFT. x is input and output
        // do something
        simd_free(x);
    }

There are member functions, such as the following.

fwd(x)  -- DFT(with 1/N normalization) x:input/output
fwd0(x) -- DFT(non normalization) x:input/output
fwdu(x) -- DFT(unitary transformation) x:input/output
fwdn(x) -- DFT(with 1/N normalization) x:input/output

inv(x)  -- IDFT(non normalization) x:input/output
inv0(x) -- IDFT(non normalization) x:input/output
invu(x) -- IDFT(unitary transformation) x:input/output
invn(x) -- IDFT(with 1/N normalization) x:input/output

To use in a multi-threaded environment, we need to create objects of the same number as the number of threads.