/usr/include/viennacl/fft.hpp is in libviennacl-dev 1.5.2-2.
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#define VIENNACL_FFT_HPP
/* =========================================================================
Copyright (c) 2010-2014, 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
============================================================================= */
/** @file viennacl/fft.hpp
@brief All routines related to the Fast Fourier Transform. Experimental.
*/
#include <viennacl/vector.hpp>
#include <viennacl/matrix.hpp>
#include "viennacl/linalg/opencl/kernels/fft.hpp"
#include <cmath>
#include <stdexcept>
namespace viennacl
{
namespace detail
{
namespace fft
{
const vcl_size_t MAX_LOCAL_POINTS_NUM = 512;
namespace FFT_DATA_ORDER {
enum DATA_ORDER {
ROW_MAJOR,
COL_MAJOR
};
}
}
}
}
/// @cond
namespace viennacl
{
namespace detail
{
namespace fft
{
inline bool is_radix2(vcl_size_t data_size) {
return !((data_size > 2) && (data_size & (data_size - 1)));
}
inline vcl_size_t next_power_2(vcl_size_t n) {
n = n - 1;
vcl_size_t power = 1;
while(power < sizeof(vcl_size_t) * 8) {
n = n | (n >> power);
power *= 2;
}
return n + 1;
}
inline vcl_size_t num_bits(vcl_size_t size)
{
vcl_size_t bits_datasize = 0;
vcl_size_t ds = 1;
while(ds < size)
{
ds = ds << 1;
bits_datasize++;
}
return bits_datasize;
}
/**
* @brief Direct algorithm for computing Fourier transformation.
*
* Works on any sizes of data.
* Serial implementation has o(n^2) complexity
*/
template<class SCALARTYPE>
void direct(const viennacl::ocl::handle<cl_mem>& in,
const viennacl::ocl::handle<cl_mem>& out,
vcl_size_t size,
vcl_size_t stride,
vcl_size_t batch_num,
SCALARTYPE sign = -1.0f,
FFT_DATA_ORDER::DATA_ORDER data_order = FFT_DATA_ORDER::ROW_MAJOR
)
{
viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(in.context());
viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::init(ctx);
std::string program_string = viennacl::linalg::opencl::kernels::matrix<SCALARTYPE, row_major>::program_name();
if (data_order == FFT_DATA_ORDER::COL_MAJOR)
{
viennacl::linalg::opencl::kernels::matrix<SCALARTYPE, column_major>::init(ctx);
program_string = viennacl::linalg::opencl::kernels::matrix<SCALARTYPE, column_major>::program_name();
}
else
viennacl::linalg::opencl::kernels::matrix<SCALARTYPE, row_major>::init(ctx);
viennacl::ocl::kernel& kernel = ctx.get_kernel(program_string, "fft_direct");
viennacl::ocl::enqueue(kernel(in, out, static_cast<cl_uint>(size), static_cast<cl_uint>(stride), static_cast<cl_uint>(batch_num), sign));
}
/*
* This function performs reorder of input data. Indexes are sorted in bit-reversal order.
* Such reordering should be done before in-place FFT.
*/
template <typename SCALARTYPE>
void reorder(const viennacl::ocl::handle<cl_mem>& in,
vcl_size_t size,
vcl_size_t stride,
vcl_size_t bits_datasize,
vcl_size_t batch_num,
FFT_DATA_ORDER::DATA_ORDER data_order = FFT_DATA_ORDER::ROW_MAJOR
)
{
viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(in.context());
viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::init(ctx);
std::string program_string = viennacl::linalg::opencl::kernels::matrix<SCALARTYPE, row_major>::program_name();
if (data_order == FFT_DATA_ORDER::COL_MAJOR)
{
viennacl::linalg::opencl::kernels::matrix<SCALARTYPE, column_major>::init(ctx);
program_string = viennacl::linalg::opencl::kernels::matrix<SCALARTYPE, column_major>::program_name();
}
else
viennacl::linalg::opencl::kernels::matrix<SCALARTYPE, row_major>::init(ctx);
viennacl::ocl::kernel& kernel = ctx.get_kernel(program_string, "fft_reorder");
viennacl::ocl::enqueue(kernel(in,
static_cast<cl_uint>(bits_datasize),
static_cast<cl_uint>(size),
static_cast<cl_uint>(stride),
static_cast<cl_uint>(batch_num)
)
);
}
/**
* @brief Radix-2 algorithm for computing Fourier transformation.
*
* Works only on power-of-two sizes of data.
* Serial implementation has o(n * lg n) complexity.
* This is a Cooley-Tukey algorithm
*/
template<class SCALARTYPE>
void radix2(const viennacl::ocl::handle<cl_mem>& in,
vcl_size_t size,
vcl_size_t stride,
vcl_size_t batch_num,
SCALARTYPE sign = -1.0f,
FFT_DATA_ORDER::DATA_ORDER data_order = FFT_DATA_ORDER::ROW_MAJOR
)
{
viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(in.context());
viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::init(ctx);
assert(batch_num != 0);
assert(is_radix2(size));
std::string program_string = viennacl::linalg::opencl::kernels::matrix<SCALARTYPE, row_major>::program_name();
if (data_order == FFT_DATA_ORDER::COL_MAJOR)
{
viennacl::linalg::opencl::kernels::matrix<SCALARTYPE, column_major>::init(ctx);
program_string = viennacl::linalg::opencl::kernels::matrix<SCALARTYPE, column_major>::program_name();
}
else
viennacl::linalg::opencl::kernels::matrix<SCALARTYPE, row_major>::init(ctx);
vcl_size_t bits_datasize = num_bits(size);
if(size <= MAX_LOCAL_POINTS_NUM)
{
viennacl::ocl::kernel& kernel = ctx.get_kernel(program_string, "fft_radix2_local");
viennacl::ocl::enqueue(kernel(in,
viennacl::ocl::local_mem((size * 4) * sizeof(SCALARTYPE)),
static_cast<cl_uint>(bits_datasize),
static_cast<cl_uint>(size),
static_cast<cl_uint>(stride),
static_cast<cl_uint>(batch_num),
sign));
}
else
{
reorder<SCALARTYPE>(in, size, stride, bits_datasize, batch_num);
for(vcl_size_t step = 0; step < bits_datasize; step++)
{
viennacl::ocl::kernel& kernel = ctx.get_kernel(program_string, "fft_radix2");
viennacl::ocl::enqueue(kernel(in,
static_cast<cl_uint>(step),
static_cast<cl_uint>(bits_datasize),
static_cast<cl_uint>(size),
static_cast<cl_uint>(stride),
static_cast<cl_uint>(batch_num),
sign));
}
}
}
/**
* @brief Bluestein's algorithm for computing Fourier transformation.
*
* Currently, Works only for sizes of input data which less than 2^16.
* Uses a lot of additional memory, but should be fast for any size of data.
* Serial implementation has something about o(n * lg n) complexity
*/
template<class SCALARTYPE, unsigned int ALIGNMENT>
void bluestein(viennacl::vector<SCALARTYPE, ALIGNMENT>& in,
viennacl::vector<SCALARTYPE, ALIGNMENT>& out,
vcl_size_t /*batch_num*/)
{
viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(in).context());
viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::init(ctx);
vcl_size_t size = in.size() >> 1;
vcl_size_t ext_size = next_power_2(2 * size - 1);
viennacl::vector<SCALARTYPE, ALIGNMENT> A(ext_size << 1);
viennacl::vector<SCALARTYPE, ALIGNMENT> B(ext_size << 1);
viennacl::vector<SCALARTYPE, ALIGNMENT> Z(ext_size << 1);
{
viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::program_name(), "zero2");
viennacl::ocl::enqueue(kernel(
A,
B,
static_cast<cl_uint>(ext_size)
));
}
{
viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::program_name(), "bluestein_pre");
viennacl::ocl::enqueue(kernel(
in,
A,
B,
static_cast<cl_uint>(size),
static_cast<cl_uint>(ext_size)
));
}
viennacl::linalg::convolve_i(A, B, Z);
{
viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::program_name(), "bluestein_post");
viennacl::ocl::enqueue(kernel(
Z,
out,
static_cast<cl_uint>(size)
));
}
}
template<class SCALARTYPE, unsigned int ALIGNMENT>
void multiply(viennacl::vector<SCALARTYPE, ALIGNMENT> const & input1,
viennacl::vector<SCALARTYPE, ALIGNMENT> const & input2,
viennacl::vector<SCALARTYPE, ALIGNMENT> & output)
{
viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(input1).context());
viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::init(ctx);
vcl_size_t size = input1.size() >> 1;
viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::program_name(), "fft_mult_vec");
viennacl::ocl::enqueue(kernel(input1, input2, output, static_cast<cl_uint>(size)));
}
template<class SCALARTYPE, unsigned int ALIGNMENT>
void normalize(viennacl::vector<SCALARTYPE, ALIGNMENT> & input)
{
viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(input).context());
viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::init(ctx);
viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::program_name(), "fft_div_vec_scalar");
vcl_size_t size = input.size() >> 1;
SCALARTYPE norm_factor = static_cast<SCALARTYPE>(size);
viennacl::ocl::enqueue(kernel(input, static_cast<cl_uint>(size), norm_factor));
}
template<class SCALARTYPE, unsigned int ALIGNMENT>
void transpose(viennacl::matrix<SCALARTYPE, viennacl::row_major, ALIGNMENT> & input)
{
viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(input).context());
viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::init(ctx);
viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::program_name(), "transpose_inplace");
viennacl::ocl::enqueue(kernel(input,
static_cast<cl_uint>(input.internal_size1()),
static_cast<cl_uint>(input.internal_size2()) >> 1));
}
template<class SCALARTYPE, unsigned int ALIGNMENT>
void transpose(viennacl::matrix<SCALARTYPE, viennacl::row_major, ALIGNMENT> const & input,
viennacl::matrix<SCALARTYPE, viennacl::row_major, ALIGNMENT> & output)
{
viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(input).context());
viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::init(ctx);
viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::program_name(), "transpose");
viennacl::ocl::enqueue(kernel(input,
output,
static_cast<cl_uint>(input.internal_size1()),
static_cast<cl_uint>(input.internal_size2() >> 1))
);
}
template<class SCALARTYPE>
void real_to_complex(viennacl::vector_base<SCALARTYPE> const & in,
viennacl::vector_base<SCALARTYPE> & out,
vcl_size_t size)
{
viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(in).context());
viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::init(ctx);
viennacl::ocl::kernel & kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::program_name(), "real_to_complex");
viennacl::ocl::enqueue(kernel(in, out, static_cast<cl_uint>(size)));
}
template<class SCALARTYPE>
void complex_to_real(viennacl::vector_base<SCALARTYPE> const & in,
viennacl::vector_base<SCALARTYPE>& out,
vcl_size_t size)
{
viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(in).context());
viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::init(ctx);
viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::program_name(), "complex_to_real");
viennacl::ocl::enqueue(kernel(in, out, static_cast<cl_uint>(size)));
}
template<class SCALARTYPE>
void reverse(viennacl::vector_base<SCALARTYPE>& in)
{
viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(in).context());
viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::init(ctx);
vcl_size_t size = in.size();
viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::fft<SCALARTYPE>::program_name(), "reverse_inplace");
viennacl::ocl::enqueue(kernel(in, static_cast<cl_uint>(size)));
}
} //namespace fft
} //namespace detail
/**
* @brief Generic inplace version of 1-D Fourier transformation.
*
* @param input Input vector, result will be stored here.
* @param batch_num Number of items in batch
* @param sign Sign of exponent, default is -1.0
*/
template<class SCALARTYPE, unsigned int ALIGNMENT>
void inplace_fft(viennacl::vector<SCALARTYPE, ALIGNMENT>& input,
vcl_size_t batch_num = 1,
SCALARTYPE sign = -1.0)
{
vcl_size_t size = (input.size() >> 1) / batch_num;
if(!viennacl::detail::fft::is_radix2(size))
{
viennacl::vector<SCALARTYPE, ALIGNMENT> output(input.size());
viennacl::detail::fft::direct(viennacl::traits::opencl_handle(input),
viennacl::traits::opencl_handle(output),
size,
size,
batch_num,
sign);
viennacl::copy(output, input);
} else {
viennacl::detail::fft::radix2(viennacl::traits::opencl_handle(input), size, size, batch_num, sign);
}
}
/**
* @brief Generic version of 1-D Fourier transformation.
*
* @param input Input vector.
* @param output Output vector.
* @param batch_num Number of items in batch.
* @param sign Sign of exponent, default is -1.0
*/
template<class SCALARTYPE, unsigned int ALIGNMENT>
void fft(viennacl::vector<SCALARTYPE, ALIGNMENT>& input,
viennacl::vector<SCALARTYPE, ALIGNMENT>& output,
vcl_size_t batch_num = 1,
SCALARTYPE sign = -1.0
)
{
vcl_size_t size = (input.size() >> 1) / batch_num;
if(viennacl::detail::fft::is_radix2(size))
{
viennacl::copy(input, output);
viennacl::detail::fft::radix2(viennacl::traits::opencl_handle(output), size, size, batch_num, sign);
} else {
viennacl::detail::fft::direct(viennacl::traits::opencl_handle(input),
viennacl::traits::opencl_handle(output),
size,
size,
batch_num,
sign);
}
}
/**
* @brief Generic inplace version of 2-D Fourier transformation.
*
* @param input Input matrix, result will be stored here.
* @param sign Sign of exponent, default is -1.0
*/
template<class SCALARTYPE, unsigned int ALIGNMENT>
void inplace_fft(viennacl::matrix<SCALARTYPE, viennacl::row_major, ALIGNMENT>& input,
SCALARTYPE sign = -1.0)
{
vcl_size_t rows_num = input.size1();
vcl_size_t cols_num = input.size2() >> 1;
vcl_size_t cols_int = input.internal_size2() >> 1;
// batch with rows
if(viennacl::detail::fft::is_radix2(cols_num))
{
viennacl::detail::fft::radix2(viennacl::traits::opencl_handle(input), cols_num, cols_int, rows_num, sign, viennacl::detail::fft::FFT_DATA_ORDER::ROW_MAJOR);
}
else
{
viennacl::matrix<SCALARTYPE, viennacl::row_major, ALIGNMENT> output(input.size1(), input.size2());
viennacl::detail::fft::direct(viennacl::traits::opencl_handle(input),
viennacl::traits::opencl_handle(output),
cols_num,
cols_int,
rows_num,
sign,
viennacl::detail::fft::FFT_DATA_ORDER::ROW_MAJOR
);
input = output;
}
// batch with cols
if (viennacl::detail::fft::is_radix2(rows_num)) {
viennacl::detail::fft::radix2(viennacl::traits::opencl_handle(input), rows_num, cols_int, cols_num, sign, viennacl::detail::fft::FFT_DATA_ORDER::COL_MAJOR);
} else {
viennacl::matrix<SCALARTYPE, viennacl::row_major, ALIGNMENT> output(input.size1(), input.size2());
viennacl::detail::fft::direct(viennacl::traits::opencl_handle(input),
viennacl::traits::opencl_handle(output),
rows_num,
cols_int,
cols_num,
sign,
viennacl::detail::fft::FFT_DATA_ORDER::COL_MAJOR);
input = output;
}
}
/**
* @brief Generic version of 2-D Fourier transformation.
*
* @param input Input vector.
* @param output Output vector.
* @param sign Sign of exponent, default is -1.0
*/
template<class SCALARTYPE, unsigned int ALIGNMENT>
void fft(viennacl::matrix<SCALARTYPE, viennacl::row_major, ALIGNMENT>& input,
viennacl::matrix<SCALARTYPE, viennacl::row_major, ALIGNMENT>& output,
SCALARTYPE sign = -1.0)
{
vcl_size_t rows_num = input.size1();
vcl_size_t cols_num = input.size2() >> 1;
vcl_size_t cols_int = input.internal_size2() >> 1;
// batch with rows
if(viennacl::detail::fft::is_radix2(cols_num))
{
output = input;
viennacl::detail::fft::radix2(viennacl::traits::opencl_handle(output), cols_num, cols_int, rows_num, sign, viennacl::detail::fft::FFT_DATA_ORDER::ROW_MAJOR);
}
else
{
viennacl::detail::fft::direct(viennacl::traits::opencl_handle(input),
viennacl::traits::opencl_handle(output),
cols_num,
cols_int,
rows_num,
sign,
viennacl::detail::fft::FFT_DATA_ORDER::ROW_MAJOR
);
}
// batch with cols
if(viennacl::detail::fft::is_radix2(rows_num))
{
viennacl::detail::fft::radix2(viennacl::traits::opencl_handle(output), rows_num, cols_int, cols_num, sign, viennacl::detail::fft::FFT_DATA_ORDER::COL_MAJOR);
}
else
{
viennacl::matrix<SCALARTYPE, viennacl::row_major, ALIGNMENT> tmp(output.size1(), output.size2());
tmp = output;
viennacl::detail::fft::direct(viennacl::traits::opencl_handle(tmp),
viennacl::traits::opencl_handle(output),
rows_num,
cols_int,
cols_num,
sign,
viennacl::detail::fft::FFT_DATA_ORDER::COL_MAJOR);
}
}
/**
* @brief Generic inplace version of inverse 1-D Fourier transformation.
*
* Shorthand function for fft(sign = 1.0)
*
* @param input Input vector.
* @param batch_num Number of items in batch.
* @param sign Sign of exponent, default is -1.0
*/
template<class SCALARTYPE, unsigned int ALIGNMENT>
void inplace_ifft(viennacl::vector<SCALARTYPE, ALIGNMENT>& input,
vcl_size_t batch_num = 1)
{
viennacl::inplace_fft(input, batch_num, SCALARTYPE(1.0));
viennacl::detail::fft::normalize(input);
}
/**
* @brief Generic version of inverse 1-D Fourier transformation.
*
* Shorthand function for fft(sign = 1.0)
*
* @param input Input vector.
* @param output Output vector.
* @param batch_num Number of items in batch.
* @param sign Sign of exponent, default is -1.0
*/
template<class SCALARTYPE, unsigned int ALIGNMENT>
void ifft(viennacl::vector<SCALARTYPE, ALIGNMENT>& input,
viennacl::vector<SCALARTYPE, ALIGNMENT>& output,
vcl_size_t batch_num = 1
)
{
viennacl::fft(input, output, batch_num, SCALARTYPE(1.0));
viennacl::detail::fft::normalize(output);
}
namespace linalg
{
/**
* @brief 1-D convolution of two vectors.
*
* This function does not make any changes to input vectors
*
* @param input1 Input vector #1.
* @param input2 Input vector #2.
* @param output Output vector.
*/
template<class SCALARTYPE, unsigned int ALIGNMENT>
void convolve(viennacl::vector<SCALARTYPE, ALIGNMENT>& input1,
viennacl::vector<SCALARTYPE, ALIGNMENT>& input2,
viennacl::vector<SCALARTYPE, ALIGNMENT>& output
)
{
assert(input1.size() == input2.size());
assert(input1.size() == output.size());
//temporal arrays
viennacl::vector<SCALARTYPE, ALIGNMENT> tmp1(input1.size());
viennacl::vector<SCALARTYPE, ALIGNMENT> tmp2(input2.size());
viennacl::vector<SCALARTYPE, ALIGNMENT> tmp3(output.size());
// align input arrays to equal size
// FFT of input data
viennacl::fft(input1, tmp1);
viennacl::fft(input2, tmp2);
// multiplication of input data
viennacl::detail::fft::multiply(tmp1, tmp2, tmp3);
// inverse FFT of input data
viennacl::ifft(tmp3, output);
}
/**
* @brief 1-D convolution of two vectors.
*
* This function can make changes to input vectors to avoid additional memory allocations.
*
* @param input1 Input vector #1.
* @param input2 Input vector #2.
* @param output Output vector.
*/
template<class SCALARTYPE, unsigned int ALIGNMENT>
void convolve_i(viennacl::vector<SCALARTYPE, ALIGNMENT>& input1,
viennacl::vector<SCALARTYPE, ALIGNMENT>& input2,
viennacl::vector<SCALARTYPE, ALIGNMENT>& output
)
{
assert(input1.size() == input2.size());
assert(input1.size() == output.size());
viennacl::inplace_fft(input1);
viennacl::inplace_fft(input2);
viennacl::detail::fft::multiply(input1, input2, output);
viennacl::inplace_ifft(output);
}
}
} //namespace linalg
/// @endcond
#endif
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