Developer Reference for Intel® oneAPI Math Kernel Library - C

Developer Reference

Contents

Fourier Transform Functions

The general form of the discrete Fourier transform is
for
k
l
 = 0, ...
n
l
-1 (
l
 = 1, ...,
d)
, where
σ
 is a scale factor,
δ
 = -1 for the forward transform, and
δ
 = +1 for the inverse (backward) transform. In the forward transform, the input (periodic) sequence
{
w
j
1
,
j
2
, ...,
j
d
}
belongs to the set of complex-valued sequences and real-valued sequences. Respective domains for the backward transform are represented by complex-valued sequences and complex-valued conjugate-even sequences.
The
Intel® oneAPI Math Kernel Library (oneMKL)
 provides an interface for computing a discrete Fourier transform through the fast Fourier transform algorithm. Prefixes
Dfti
 in function names and
DFTI
 in the names of configuration parameters stand for Discrete Fourier Transform Interface.
The manual
describes the following implementations of the fast Fourier transform functions available in
Intel® oneAPI Math Kernel Library
:
  • Fast Fourier transform (FFT) functions for single-processor or shared-memory systems (see FFT Functions)
  • Cluster FFT functions for distributed-memory architectures (available only for Intel® 64 and Intel® Many Integrated Core architectures)
Intel® oneAPI Math Kernel Library
 also supports the FFTW3* interfaces to the fast Fourier transform functionality for shared memory paradigm (SMP) systems.
Both FFT and Cluster FFT functions compute an FFT in five steps:
  1. Allocate a fresh descriptor for the problem with a call to the
    DftiCreateDescriptor
     or
    DftiCreateDescriptorDM
     function. The descriptor captures the configuration of the transform, such as the dimensionality (or rank), sizes, number of transforms, memory layout of the input/output data (defined by strides), and scaling factors. Many of the configuration settings are assigned default values in this call which you might need to modify in your application.
  2. Optionally adjust the descriptor configuration with a call to the
    DftiSetValue
     or
    DftiSetValueDM
     function as needed. Typically, you must carefully define the data storage layout for an FFT or the data distribution among processes for a Cluster FFT. The configuration settings of the descriptor, such as the default values, can be obtained with the
    DftiGetValue
     or
    DftiGetValueDM
     function.
  3. Commit the descriptor with a call to the
    DftiCommitDescriptor
     or
    DftiCommitDescriptorDM
     function, that is, make the descriptor ready for the transform computation. Once the descriptor is committed, the parameters of the transform, such as the type and number of transforms, strides and distances, the type and storage layout of the data, and so on, are "frozen" in the descriptor.
  4. Compute the transform with a call to the
    DftiComputeForward
    /
    DftiComputeBackward
     or
    DftiComputeForwardDM
    /
    DftiComputeBackwardDM
     functions as many times as needed. Because the descriptor is defined and committed separately, all that the compute functions do is take the input and output data and compute the transform as defined. To modify any configuration parameters for another call to a compute function, use
    DftiSetValue
     followed by
    DftiCommitDescriptor
     (
    DftiSetValueDM
     followed by
    DftiCommitDescriptorDM
    ) or create and commit another descriptor.
  5. Deallocate the descriptor with a call to the
    DftiFreeDescriptor
     or
    DftiFreeDescriptorDM
     function. This returns the memory internally consumed by the descriptor to the operating system.
All the above functions return an integer status value, which is zero upon successful completion of the operation. You can interpret a non-zero status with the help of the
DftiErrorClass
 or
DftiErrorMessage
 function.
The FFT functions support lengths with arbitrary factors. You can improve performance of the
Intel® oneAPI Math Kernel Library
 FFT if the length of your data vector permits factorization into powers of optimized radices. See the
Intel® oneAPI Math Kernel Library
 Developer Guide
 for specific radices supported efficiently.
The FFT functions assume the Cartesian representation of complex data (that is, the real and imaginary parts define a complex number). The
Intel® oneAPI Math Kernel Library
 Vector Mathematical Functions provide efficient tools for conversion to and from polar representation.
Product and Performance Information
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.
Notice revision #20201201

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.