All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Macros Pages
fft.cpp

This tutorial showcasts FFT functionality.

Note
  The FFT module is experimental in ViennaCL. The API might change in future versions.

We start with including the respective headers:

// include necessary system headers
#include <iostream>
#include <vector>
#include <cmath>
#include <complex>
#include <fstream>
// include basic scalar and vector types of ViennaCL
#include "viennacl/vector.hpp"
#include "viennacl/matrix.hpp"
// include FFT routines
#include "viennacl/fft.hpp"
#include "viennacl/linalg/fft_operations.hpp"

In the main()-routine we create a few vectors and matrices and then run FFT on them.

int main()
{
// Feel free to change this type definition to double if your gpu supports that
typedef float ScalarType;
// Create vectors of eight complex values (represented as pairs of floating point values: [real_0, imag_0, real_1, imag_1, etc.])
viennacl::vector<ScalarType> input_vec(16);
viennacl::vector<ScalarType> output_vec(16);
viennacl::vector<ScalarType> input2_vec(16);
viennacl::matrix<ScalarType> m(4, 8);
viennacl::matrix<ScalarType> o(4, 8);
for (std::size_t i = 0; i < m.size1(); i++)
for (std::size_t s = 0; s < m.size2(); s++)
m(i, s) = ScalarType((i + s) / 2);

Fill the vectors and matrices with values by using operator(). Use viennacl::copy() for larger data!

for (std::size_t i = 0; i < input_vec.size(); ++i)
{
if (i % 2 == 0)
{
input_vec(i) = ScalarType(i / 2); // even indices represent real part
input2_vec(i) = ScalarType(i / 2);
} else
input_vec(i) = 0; // odd indices represent imaginary part
}

Compute the FFT and store result in 'output_vec'

std::cout << "Computing FFT Matrix" << std::endl;
std::cout << "m: " << m << std::endl;
std::cout << "o: " << o << std::endl;
viennacl::fft(m, o);
std::cout << "Done" << std::endl;
std::cout << "m: " << m << std::endl;
std::cout << "o: " << o << std::endl;
std::cout << "Transpose" << std::endl;
viennacl::linalg::transpose(m, o);
//viennacl::linalg::transpose(m);
std::cout << "m: " << m << std::endl;
std::cout << "o: " << o << std::endl;
std::cout << "---------------------" << std::endl;

Compute the FFT using the Bluestein algorithm (usually faster, but higher memory footprint)

std::cout << "Computing FFT bluestein" << std::endl;
// Print the vector
std::cout << "input_vec: " << input_vec << std::endl;
std::cout << "Done" << std::endl;
viennacl::linalg::bluestein(input_vec, output_vec, 0);
std::cout << "input_vec: " << input_vec << std::endl;
std::cout << "output_vec: " << output_vec << std::endl;
std::cout << "---------------------" << std::endl;

Computing the standard radix-FFT for a vector

std::cout << "Computing FFT " << std::endl;
// Print the vector
std::cout << "input_vec: " << input_vec << std::endl;
std::cout << "Done" << std::endl;
viennacl::fft(input_vec, output_vec);
std::cout << "input_vec: " << input_vec << std::endl;
std::cout << "output_vec: " << output_vec << std::endl;
std::cout << "---------------------" << std::endl;

Computing the standard inverse radix-FFT for a vector

std::cout << "Computing inverse FFT..." << std::endl;
//viennacl::ifft(output_vec, input_vec); // either store result into output_vec
viennacl::inplace_ifft(output_vec); // or compute in-place
std::cout << "input_vec: " << input_vec << std::endl;
std::cout << "output_vec: " << output_vec << std::endl;
std::cout << "---------------------" << std::endl;

Convert a real vector to an interleaved complex vector and back. Entries with even indices represent real parts, odd indices imaginary parts.

std::cout << "Computing real to complex..." << std::endl;
std::cout << "input_vec: " << input_vec << std::endl;
viennacl::linalg::real_to_complex(input_vec, output_vec, input_vec.size() / 2); // or compute in-place
std::cout << "output_vec: " << output_vec << std::endl;
std::cout << "---------------------" << std::endl;
std::cout << "Computing complex to real..." << std::endl;
std::cout << "input_vec: " << input_vec << std::endl;
//viennacl::ifft(output_vec, input_vec); // either store result into output_vec
viennacl::linalg::complex_to_real(input_vec, output_vec, input_vec.size() / 2); // or compute in-place
std::cout << "output_vec: " << output_vec << std::endl;
std::cout << "---------------------" << std::endl;

Point-wise multiplication of two complex vectors.

std::cout << "Computing multiply complex" << std::endl;
// Print the vector
std::cout << "input_vec: " << input_vec << std::endl;
std::cout << "input2_vec: " << input2_vec << std::endl;
viennacl::linalg::multiply_complex(input_vec, input2_vec, output_vec);
std::cout << "Done" << std::endl;
std::cout << "output_vec: " << output_vec << std::endl;
std::cout << "---------------------" << std::endl;

That's it.

std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
return EXIT_SUCCESS;
}

## Full Example Code

/* =========================================================================
Copyright (c) 2010-2016, Institute for Microelectronics,
Institute for Analysis and Scientific Computing,
TU Wien.
Portions of this software are copyright by UChicago Argonne, LLC.
-----------------
ViennaCL - The Vienna Computing Library
-----------------
Project Head: Karl Rupp rupp@iue.tuwien.ac.at
(A list of authors and contributors can be found in the PDF manual)
License: MIT (X11), see file LICENSE in the base directory
============================================================================= */
// include necessary system headers
#include <iostream>
#include <vector>
#include <cmath>
#include <complex>
#include <fstream>
// include basic scalar and vector types of ViennaCL
#include "viennacl/vector.hpp"
#include "viennacl/matrix.hpp"
// include FFT routines
#include "viennacl/fft.hpp"
#include "viennacl/linalg/fft_operations.hpp"
int main()
{
// Feel free to change this type definition to double if your gpu supports that
typedef float ScalarType;
// Create vectors of eight complex values (represented as pairs of floating point values: [real_0, imag_0, real_1, imag_1, etc.])
viennacl::vector<ScalarType> input_vec(16);
viennacl::vector<ScalarType> output_vec(16);
viennacl::vector<ScalarType> input2_vec(16);
viennacl::matrix<ScalarType> m(4, 8);
viennacl::matrix<ScalarType> o(4, 8);
for (std::size_t i = 0; i < m.size1(); i++)
for (std::size_t s = 0; s < m.size2(); s++)
m(i, s) = ScalarType((i + s) / 2);
for (std::size_t i = 0; i < input_vec.size(); ++i)
{
if (i % 2 == 0)
{
input_vec(i) = ScalarType(i / 2); // even indices represent real part
input2_vec(i) = ScalarType(i / 2);
} else
input_vec(i) = 0; // odd indices represent imaginary part
}
std::cout << "Computing FFT Matrix" << std::endl;
std::cout << "m: " << m << std::endl;
std::cout << "o: " << o << std::endl;
viennacl::fft(m, o);
std::cout << "Done" << std::endl;
std::cout << "m: " << m << std::endl;
std::cout << "o: " << o << std::endl;
std::cout << "Transpose" << std::endl;
viennacl::linalg::transpose(m, o);
//viennacl::linalg::transpose(m);
std::cout << "m: " << m << std::endl;
std::cout << "o: " << o << std::endl;
std::cout << "---------------------" << std::endl;
std::cout << "Computing FFT bluestein" << std::endl;
// Print the vector
std::cout << "input_vec: " << input_vec << std::endl;
std::cout << "Done" << std::endl;
viennacl::linalg::bluestein(input_vec, output_vec, 0);
std::cout << "input_vec: " << input_vec << std::endl;
std::cout << "output_vec: " << output_vec << std::endl;
std::cout << "---------------------" << std::endl;
std::cout << "Computing FFT " << std::endl;
// Print the vector
std::cout << "input_vec: " << input_vec << std::endl;
std::cout << "Done" << std::endl;
viennacl::fft(input_vec, output_vec);
std::cout << "input_vec: " << input_vec << std::endl;
std::cout << "output_vec: " << output_vec << std::endl;
std::cout << "---------------------" << std::endl;
std::cout << "Computing inverse FFT..." << std::endl;
//viennacl::ifft(output_vec, input_vec); // either store result into output_vec
viennacl::inplace_ifft(output_vec); // or compute in-place
std::cout << "input_vec: " << input_vec << std::endl;
std::cout << "output_vec: " << output_vec << std::endl;
std::cout << "---------------------" << std::endl;
std::cout << "Computing real to complex..." << std::endl;
std::cout << "input_vec: " << input_vec << std::endl;
viennacl::linalg::real_to_complex(input_vec, output_vec, input_vec.size() / 2); // or compute in-place
std::cout << "output_vec: " << output_vec << std::endl;
std::cout << "---------------------" << std::endl;
std::cout << "Computing complex to real..." << std::endl;
std::cout << "input_vec: " << input_vec << std::endl;
//viennacl::ifft(output_vec, input_vec); // either store result into output_vec
viennacl::linalg::complex_to_real(input_vec, output_vec, input_vec.size() / 2); // or compute in-place
std::cout << "output_vec: " << output_vec << std::endl;
std::cout << "---------------------" << std::endl;
std::cout << "Computing multiply complex" << std::endl;
// Print the vector
std::cout << "input_vec: " << input_vec << std::endl;
std::cout << "input2_vec: " << input2_vec << std::endl;
viennacl::linalg::multiply_complex(input_vec, input2_vec, output_vec);
std::cout << "Done" << std::endl;
std::cout << "output_vec: " << output_vec << std::endl;
std::cout << "---------------------" << std::endl;
std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
return EXIT_SUCCESS;
}