python - Enthought Python 中的线程化 FFT

Question

Numpy/SciPy 中的快速傅里叶变换 (FFT) 没有线程化。Enthought Python 附带英特尔 MKL 数值库，该库能够进行线程 FFT。如何访问这些例程？

Answer 1

以下代码适用于 Windows 7 Ultimate 64 位上的 Enthought 7.3-1（64 位）。我没有对它进行基准测试，但它肯定会同时使用所有内核，而不仅仅是一个。

from ctypes import *

class Mkl_Fft:
    c_double_p = POINTER(c_double)

    def __init__(self,num_threads=8):
        self.dfti = cdll.LoadLibrary("mk2_rt.dll")
        self.dfti.MKL_Set_Num_Threads(num_threads)
        self.Create = self.dfti.DftiCreateDescriptor_d_md
        self.Commit = self.dfti.DftiCommitDescriptor
        self.ComputeForward = self.dfti.DftiComputeForward

    def fft(self,a):
        Desc_Handle = c_void_p(0)
        dims = (c_int*2)(*a.shape)
        DFTI_COMPLEX = c_int(32)
        rank = 2

        self.Create(byref(Desc_Handle), DFTI_COMPLEX, rank, dims )
        self.Commit(Desc_Handle)
        self.ComputeForward(Desc_Handle, a.ctypes.data_as(self.c_double_p) )

用法：

import numpy as np
a = np.ones( (32,32), dtype = complex128 )
fft = Mkl_Fft()
fft.fft(a)

Answer 2

我的原始答案的更简洁的版本如下：

from ctypes import *

mkl = cdll.LoadLibrary("mk2_rt.dll")
c_double_p = POINTER(c_double)
DFTI_COMPLEX = c_int(32)
DFTI_DOUBLE = c_int(36)

def fft2(a):
    Desc_Handle = c_void_p(0)
    dims = (c_int*2)(*a.shape)

    mkl.DftiCreateDescriptor(byref(Desc_Handle), DFTI_DOUBLE, DFTI_COMPLEX, 2, dims )
    mkl.DftiCommitDescriptor(Desc_Handle)
    mkl.DftiComputeForward(Desc_Handle, a.ctypes.data_as(c_void_p) )
    mkl.DftiFreeDescriptor(byref(Desc_Handle))

    return a

def ifft2(a):
    Desc_Handle = c_void_p(0)
    dims = (c_int*2)(*a.shape)

    mkl.DftiCreateDescriptor(byref(Desc_Handle), DFTI_DOUBLE, DFTI_COMPLEX, 2, dims )
    mkl.DftiCommitDescriptor(Desc_Handle)
    mkl.DftiComputeBackward(Desc_Handle, a.ctypes.data_as(c_void_p) )
    mkl.DftiFreeDescriptor(byref(Desc_Handle))

    return a

Answer 3

新的和改进的版本，可以处理输入和输出数组中的任意步幅。默认情况下，这个现在不在原地并创建一个新数组。它模仿了 Numpy FFT 例程，只是它具有不同的规范化。

''' Wrapper to MKL FFT routines '''

import numpy as _np
import ctypes as _ctypes

mkl = _ctypes.cdll.LoadLibrary("mk2_rt.dll")
_DFTI_COMPLEX = _ctypes.c_int(32)
_DFTI_DOUBLE = _ctypes.c_int(36)
_DFTI_PLACEMENT = _ctypes.c_int(11)
_DFTI_NOT_INPLACE = _ctypes.c_int(44)
_DFTI_INPUT_STRIDES = _ctypes.c_int(12)
_DFTI_OUTPUT_STRIDES = _ctypes.c_int(13)

def fft2(a, out=None):
    ''' 
Forward two-dimensional double-precision complex-complex FFT.
Uses the Intel MKL libraries distributed with Enthought Python.
Normalisation is different from Numpy!
By default, allocates new memory like 'a' for output data.
Returns the array containing output data.
'''

    assert a.dtype == _np.complex128
    assert len(a.shape) == 2

    inplace = False

    if out is a:
        inplace = True

    elif out is not None:
        assert out.dtype == _np.complex128
        assert a.shape == out.shape
        assert not _np.may_share_memory(a, out)

    else:
        out = _np.empty_like(a)

    Desc_Handle = _ctypes.c_void_p(0)
    dims = (_ctypes.c_int*2)(*a.shape)

    mkl.DftiCreateDescriptor(_ctypes.byref(Desc_Handle), _DFTI_DOUBLE, _DFTI_COMPLEX, _ctypes.c_int(2), dims )

    #Set input strides if necessary
    if not a.flags['C_CONTIGUOUS']:
        in_strides = (_ctypes.c_int*3)(0, a.strides[0]/16, a.strides[1]/16)
        mkl.DftiSetValue(Desc_Handle, _DFTI_INPUT_STRIDES, _ctypes.byref(in_strides))

    if inplace:
        #Inplace FFT
        mkl.DftiCommitDescriptor(Desc_Handle)
        mkl.DftiComputeForward(Desc_Handle, a.ctypes.data_as(_ctypes.c_void_p) )

    else:
        #Not-inplace FFT
        mkl.DftiSetValue(Desc_Handle, _DFTI_PLACEMENT, _DFTI_NOT_INPLACE)

        #Set output strides if necessary
        if not out.flags['C_CONTIGUOUS']:
            out_strides = (_ctypes.c_int*3)(0, out.strides[0]/16, out.strides[1]/16)
            mkl.DftiSetValue(Desc_Handle, _DFTI_OUTPUT_STRIDES, _ctypes.byref(out_strides))

        mkl.DftiCommitDescriptor(Desc_Handle)
        mkl.DftiComputeForward(Desc_Handle, a.ctypes.data_as(_ctypes.c_void_p), out.ctypes.data_as(_ctypes.c_void_p) )

    mkl.DftiFreeDescriptor(_ctypes.byref(Desc_Handle))

    return out

def ifft2(a, out=None):
    ''' 
Backward two-dimensional double-precision complex-complex FFT.
Uses the Intel MKL libraries distributed with Enthought Python.
Normalisation is different from Numpy!
By default, allocates new memory like 'a' for output data.
Returns the array containing output data.
'''

    assert a.dtype == _np.complex128
    assert len(a.shape) == 2

    inplace = False

    if out is a:
        inplace = True

    elif out is not None:
        assert out.dtype == _np.complex128
        assert a.shape == out.shape
        assert not _np.may_share_memory(a, out)

    else:
        out = _np.empty_like(a)

    Desc_Handle = _ctypes.c_void_p(0)
    dims = (_ctypes.c_int*2)(*a.shape)

    mkl.DftiCreateDescriptor(_ctypes.byref(Desc_Handle), _DFTI_DOUBLE, _DFTI_COMPLEX, _ctypes.c_int(2), dims )

    #Set input strides if necessary
    if not a.flags['C_CONTIGUOUS']:
        in_strides = (_ctypes.c_int*3)(0, a.strides[0]/16, a.strides[1]/16)
        mkl.DftiSetValue(Desc_Handle, _DFTI_INPUT_STRIDES, _ctypes.byref(in_strides))

    if inplace:
        #Inplace FFT
        mkl.DftiCommitDescriptor(Desc_Handle)
        mkl.DftiComputeBackward(Desc_Handle, a.ctypes.data_as(_ctypes.c_void_p) )

    else:
        #Not-inplace FFT
        mkl.DftiSetValue(Desc_Handle, _DFTI_PLACEMENT, _DFTI_NOT_INPLACE)

        #Set output strides if necessary
        if not out.flags['C_CONTIGUOUS']:
            out_strides = (_ctypes.c_int*3)(0, out.strides[0]/16, out.strides[1]/16)
            mkl.DftiSetValue(Desc_Handle, _DFTI_OUTPUT_STRIDES, _ctypes.byref(out_strides))

        mkl.DftiCommitDescriptor(Desc_Handle)
        mkl.DftiComputeBackward(Desc_Handle, a.ctypes.data_as(_ctypes.c_void_p), out.ctypes.data_as(_ctypes.c_void_p) )

    mkl.DftiFreeDescriptor(_ctypes.byref(Desc_Handle))

    return out