This is fftw.info, produced by makeinfo version 4.2 from fftw.texi.

   This is the FFTW User's manual.

   Copyright (C) 1997-1999 Massachusetts Institute of Technology

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File: fftw.info,  Node: Top,  Next: Introduction,  Prev: (dir),  Up: (dir)

FFTW User Manual
****************

   Welcome to FFTW, the Fastest Fourier Transform in the West.  FFTW is
a collection of fast C routines to compute the discrete Fourier
transform.  This manual documents FFTW version 2.1.5.

* Menu:

* Introduction::
* Tutorial::
* FFTW Reference::
* Parallel FFTW::
* Calling FFTW from Fortran::
* Installation and Customization::
* Acknowledgments::
* License and Copyright::
* Concept Index::
* Library Index::


Tutorial

* Complex One-dimensional Transforms Tutorial::
* Complex Multi-dimensional Transforms Tutorial::
* Real One-dimensional Transforms Tutorial::
* Real Multi-dimensional Transforms Tutorial::
* Multi-dimensional Array Format::
* Words of Wisdom::

Multi-dimensional Array Format

* Row-major Format::
* Column-major Format::
* Static Arrays in C::
* Dynamic Arrays in C::
* Dynamic Arrays in C-The Wrong Way::

Words of Wisdom

* Caveats in Using Wisdom::     What you should worry about in using wisdom
* Importing and Exporting Wisdom::  I/O of wisdom to disk and other media

FFTW Reference

* Data Types::                  real, complex, and halfcomplex numbers
* One-dimensional Transforms Reference::
* Multi-dimensional Transforms Reference::
* Real One-dimensional Transforms Reference::
* Real Multi-dimensional Transforms Reference::
* Wisdom Reference::
* Memory Allocator Reference::
* Thread safety::

One-dimensional Transforms Reference

* fftw_create_plan::            Plan Creation
* Discussion on Specific Plans::
* fftw::                        Plan Execution
* fftw_destroy_plan::           Plan Destruction
* What FFTW Really Computes::   Definition of the DFT.

Multi-dimensional Transforms Reference

* fftwnd_create_plan::          Plan Creation
* fftwnd::                      Plan Execution
* fftwnd_destroy_plan::         Plan Destruction
* What FFTWND Really Computes::

Real One-dimensional Transforms Reference

* rfftw_create_plan::           Plan Creation
* rfftw::                       Plan Execution
* rfftw_destroy_plan::          Plan Destruction
* What RFFTW Really Computes::

Real Multi-dimensional Transforms Reference

* rfftwnd_create_plan::         Plan Creation
* rfftwnd::                     Plan Execution
* Array Dimensions for Real Multi-dimensional Transforms::
* Strides in In-place RFFTWND::
* rfftwnd_destroy_plan::        Plan Destruction
* What RFFTWND Really Computes::

Wisdom Reference

* fftw_export_wisdom::
* fftw_import_wisdom::
* fftw_forget_wisdom::

Parallel FFTW

* Multi-threaded FFTW::
* MPI FFTW::

Multi-threaded FFTW

* Installation and Supported Hardware/Software::
* Usage of Multi-threaded FFTW::
* How Many Threads to Use?::
* Using Multi-threaded FFTW in a Multi-threaded Program::
* Tips for Optimal Threading::

MPI FFTW

* MPI FFTW Installation::
* Usage of MPI FFTW for Complex Multi-dimensional Transforms::
* MPI Data Layout::
* Usage of MPI FFTW for Real Multi-dimensional Transforms::
* Usage of MPI FFTW for Complex One-dimensional Transforms::
* MPI Tips::

Calling FFTW from Fortran

* Wrapper Routines::
* FFTW Constants in Fortran::
* Fortran Examples::

Installation and Customization

* Installation on Unix::
* Installation on non-Unix Systems::
* Installing FFTW in both single and double precision::
* gcc and Pentium hacks::
* Customizing the timer::
* Generating your own code::


File: fftw.info,  Node: Introduction,  Next: Tutorial,  Prev: Top,  Up: Top

Introduction
************

   This manual documents version 2.1.5 of FFTW, the _Fastest Fourier
Transform in the West_.  FFTW is a comprehensive collection of fast C
routines for computing the discrete Fourier transform (DFT) in one or
more dimensions, of both real and complex data, and of arbitrary input
size.  FFTW also includes parallel transforms for both shared- and
distributed-memory systems.  We assume herein that the reader is already
familiar with the properties and uses of the DFT that are relevant to
her application.  Otherwise, see e.g. `The Fast Fourier Transform' by
E. O. Brigham (Prentice-Hall, Englewood Cliffs, NJ, 1974).  Our web
page (http://www.fftw.org) also has links to FFT-related information
online.

   FFTW is usually faster (and sometimes much faster) than all other
freely-available Fourier transform programs found on the Net.  For
transforms whose size is a power of two, it compares favorably with the
FFT codes in Sun's Performance Library and IBM's ESSL library, which are
targeted at specific machines.  Moreover, FFTW's performance is
_portable_.  Indeed, FFTW is unique in that it automatically adapts
itself to your machine, your cache, the size of your memory, the number
of registers, and all the other factors that normally make it impossible
to optimize a program for more than one machine.  An extensive
comparison of FFTW's performance with that of other Fourier transform
codes has been made. The results are available on the Web at the
benchFFT home page (http://theory.lcs.mit.edu/~benchfft).

   In order to use FFTW effectively, you need to understand one basic
concept of FFTW's internal structure.  FFTW does not used a fixed
algorithm for computing the transform, but it can adapt the DFT
algorithm to details of the underlying hardware in order to achieve best
performance.  Hence, the computation of the transform is split into two
phases.  First, FFTW's "planner" is called, which "learns" the fastest
way to compute the transform on your machine.  The planner produces a
data structure called a "plan" that contains this information.
Subsequently, the plan is passed to FFTW's "executor", along with an
array of input data.  The executor computes the actual transform, as
dictated by the plan.  The plan can be reused as many times as needed.
In typical high-performance applications, many transforms of the same
size are computed, and consequently a relatively-expensive
initialization of this sort is acceptable.  On the other hand, if you
need a single transform of a given size, the one-time cost of the
planner becomes significant.  For this case, FFTW provides fast
planners based on heuristics or on previously computed plans.

   The pattern of planning/execution applies to all four operation
modes of FFTW, that is, I) one-dimensional complex transforms (FFTW),
II) multi-dimensional complex transforms (FFTWND), III) one-dimensional
transforms of real data (RFFTW), IV) multi-dimensional transforms of
real data (RFFTWND).  Each mode comes with its own planner and executor.

   Besides the automatic performance adaptation performed by the
planner, it is also possible for advanced users to customize FFTW for
their special needs.  As distributed, FFTW works most efficiently for
arrays whose size can be factored into small primes (2, 3, 5, and 7),
and uses a slower general-purpose routine for other factors.  FFTW,
however, comes with a code generator that can produce fast C programs
for any particular array size you may care about.  For example, if you
need transforms of size 513 = 19 x 3^3, you can customize FFTW to
support the factor 19 efficiently.

   FFTW can exploit multiple processors if you have them.  FFTW comes
with a shared-memory implementation on top of POSIX (and similar)
threads, as well as a distributed-memory implementation based on MPI.
We also provide an experimental parallel implementation written in Cilk,
_the superior programming tool of choice for discriminating hackers_
(Olin Shivers).  (See the Cilk home page
(http://supertech.lcs.mit.edu/cilk).)

   For more information regarding FFTW, see the paper, "The Fastest
Fourier Transform in the West," by M. Frigo and S. G. Johnson, which is
the technical report MIT-LCS-TR-728 (Sep. '97).  See also, "FFTW: An
Adaptive Software Architecture for the FFT," by M. Frigo and S. G.
Johnson, which appeared in the 23rd International Conference on
Acoustics, Speech, and Signal Processing (`Proc. ICASSP 1998' 3, p.
1381).  The code generator is described in the paper "A Fast Fourier
Transform Compiler", by M. Frigo, to appear in the `Proceedings of the
1999 ACM SIGPLAN Conference on Programming Language Design and
Implementation (PLDI), Atlanta, Georgia, May 1999'.  These papers,
along with the latest version of FFTW, the FAQ, benchmarks, and other
links, are available at the FFTW home page (http://www.fftw.org).  The
current version of FFTW incorporates many good ideas from the past
thirty years of FFT literature.  In one way or another, FFTW uses the
Cooley-Tukey algorithm, the Prime Factor algorithm, Rader's algorithm
for prime sizes, and the split-radix algorithm (with a variation due to
Dan Bernstein).  Our code generator also produces new algorithms that
we do not yet completely understand.  The reader is referred to the
cited papers for the appropriate references.

   The rest of this manual is organized as follows.  We first discuss
the sequential (one-processor) implementation.  We start by describing
the basic features of FFTW in *Note Tutorial::.  This discussion
includes the storage scheme of multi-dimensional arrays (*Note
Multi-dimensional Array Format::) and FFTW's mechanisms for storing
plans on disk (*Note Words of Wisdom::).  Next, *Note FFTW Reference::
provides comprehensive documentation of all FFTW's features.  Parallel
transforms are discussed in their own chapter *Note Parallel FFTW::.
Fortran programmers can also use FFTW, as described in *Note Calling
FFTW from Fortran::.  *Note Installation and Customization:: explains
how to install FFTW in your computer system and how to adapt FFTW to
your needs.  License and copyright information is given in *Note
License and Copyright::.  Finally, we thank all the people who helped
us in *Note Acknowledgments::.


File: fftw.info,  Node: Tutorial,  Next: FFTW Reference,  Prev: Introduction,  Up: Top

Tutorial
********

   This chapter describes the basic usage of FFTW, i.e., how to compute
the Fourier transform of a single array.  This chapter tells the truth,
but not the _whole_ truth. Specifically, FFTW implements additional
routines and flags, providing extra functionality, that are not
documented here.  *Note FFTW Reference::, for more complete information.
(Note that you need to compile and install FFTW before you can use it in
a program.  *Note Installation and Customization::, for the details of
the installation.)

   Here, we assume a default installation of FFTW.  In some
installations (particulary from binary packages), the FFTW header files
and libraries are prefixed with ``d'' or ``s'' to indicate versions in
double or single precision, respectively.  The usage of FFTW in that
case is the same, except that `#include' directives and link commands
must use the appropriate prefix.  *Note Installing FFTW in both single
and double precision::, for more information.

   This tutorial chapter is structured as follows.  *Note Complex
One-dimensional Transforms Tutorial:: describes the basic usage of the
one-dimensional transform of complex data.  *Note Complex
Multi-dimensional Transforms Tutorial:: describes the basic usage of the
multi-dimensional transform of complex data.  *Note Real
One-dimensional Transforms Tutorial:: describes the one-dimensional
transform of real data and its inverse.  Finally, *Note Real
Multi-dimensional Transforms Tutorial:: describes the multi-dimensional
transform of real data and its inverse.  We recommend that you read
these sections in the order that they are presented.  We then discuss
two topics in detail.  In *Note Multi-dimensional Array Format::, we
discuss the various alternatives for storing multi-dimensional arrays
in memory.  *Note Words of Wisdom:: shows how you can save FFTW's plans
for future use.

* Menu:

* Complex One-dimensional Transforms Tutorial::
* Complex Multi-dimensional Transforms Tutorial::
* Real One-dimensional Transforms Tutorial::
* Real Multi-dimensional Transforms Tutorial::
* Multi-dimensional Array Format::
* Words of Wisdom::


File: fftw.info,  Node: Complex One-dimensional Transforms Tutorial,  Next: Complex Multi-dimensional Transforms Tutorial,  Prev: Tutorial,  Up: Tutorial

Complex One-dimensional Transforms Tutorial
===========================================

   The basic usage of FFTW is simple.  A typical call to FFTW looks
like:

     #include <fftw.h>
     ...
     {
          fftw_complex in[N], out[N];
          fftw_plan p;
          ...
          p = fftw_create_plan(N, FFTW_FORWARD, FFTW_ESTIMATE);
          ...
          fftw_one(p, in, out);
          ...
          fftw_destroy_plan(p);
     }

   The first thing we do is to create a "plan", which is an object that
contains all the data that FFTW needs to compute the FFT, using the
following function:

     fftw_plan fftw_create_plan(int n, fftw_direction dir, int flags);

   The first argument, `n', is the size of the transform you are trying
to compute.  The size `n' can be any positive integer, but sizes that
are products of small factors are transformed most efficiently.  The
second argument, `dir', can be either `FFTW_FORWARD' or
`FFTW_BACKWARD', and indicates the direction of the transform you are
interested in.  Alternatively, you can use the sign of the exponent in
the transform, -1 or +1, which corresponds to `FFTW_FORWARD' or
`FFTW_BACKWARD' respectively.  The `flags' argument is either
`FFTW_MEASURE' or `FFTW_ESTIMATE'.  `FFTW_MEASURE' means that FFTW
actually runs and measures the execution time of several FFTs in order
to find the best way to compute the transform of size `n'.  This may
take some time, depending on your installation and on the precision of
the timer in your machine.  `FFTW_ESTIMATE', on the contrary, does not
run any computation, and just builds a reasonable plan, which may be
sub-optimal.  In other words, if your program performs many transforms
of the same size and initialization time is not important, use
`FFTW_MEASURE'; otherwise use the estimate.  (A compromise between
these two extremes exists.  *Note Words of Wisdom::.)

   Once the plan has been created, you can use it as many times as you
like for transforms on arrays of the same size.  When you are done with
the plan, you deallocate it by calling `fftw_destroy_plan(plan)'.

   The transform itself is computed by passing the plan along with the
input and output arrays to `fftw_one':

     void fftw_one(fftw_plan plan, fftw_complex *in, fftw_complex *out);

   Note that the transform is out of place: `in' and `out' must point
to distinct arrays. It operates on data of type `fftw_complex', a data
structure with real (`in[i].re') and imaginary (`in[i].im')
floating-point components.  The `in' and `out' arrays should have the
length specified when the plan was created.  An alternative function,
`fftw', allows you to efficiently perform multiple and/or strided
transforms (*note FFTW Reference::).

   The DFT results are stored in-order in the array `out', with the
zero-frequency (DC) component in `out[0]'.  The array `in' is not
modified.  Users should note that FFTW computes an unnormalized DFT,
the sign of whose exponent is given by the `dir' parameter of
`fftw_create_plan'.  Thus, computing a forward followed by a backward
transform (or vice versa) results in the original array scaled by `n'.
*Note What FFTW Really Computes::, for the definition of DFT.

   A program using FFTW should be linked with `-lfftw -lm' on Unix
systems, or with the FFTW and standard math libraries in general.


File: fftw.info,  Node: Complex Multi-dimensional Transforms Tutorial,  Next: Real One-dimensional Transforms Tutorial,  Prev: Complex One-dimensional Transforms Tutorial,  Up: Tutorial

Complex Multi-dimensional Transforms Tutorial
=============================================

   FFTW can also compute transforms of any number of dimensions
("rank").  The syntax is similar to that for the one-dimensional
transforms, with `fftw_' replaced by `fftwnd_' (which stands for
"`fftw' in `N' dimensions").

   As before, we `#include <fftw.h>' and create a plan for the
transforms, this time of type `fftwnd_plan':

     fftwnd_plan fftwnd_create_plan(int rank, const int *n,
                                    fftw_direction dir, int flags);

   `rank' is the dimensionality of the array, and can be any
non-negative integer.  The next argument, `n', is a pointer to an
integer array of length `rank' containing the (positive) sizes of each
dimension of the array.  (Note that the array will be stored in
row-major order. *Note Multi-dimensional Array Format::, for information
on row-major order.)  The last two parameters are the same as in
`fftw_create_plan'.  We now, however, have an additional possible flag,
`FFTW_IN_PLACE', since `fftwnd' supports true in-place transforms.
Multiple flags are combined using a bitwise "or" (`|').  (An "in-place"
transform is one in which the output data overwrite the input data.  It
thus requires half as much memory as--and is often faster than--its
opposite, an "out-of-place" transform.)

   For two- and three-dimensional transforms, FFTWND provides
alternative routines that accept the sizes of each dimension directly,
rather than indirectly through a rank and an array of sizes.  These are
otherwise identical to `fftwnd_create_plan', and are sometimes more
convenient:

     fftwnd_plan fftw2d_create_plan(int nx, int ny,
                                    fftw_direction dir, int flags);
     fftwnd_plan fftw3d_create_plan(int nx, int ny, int nz,
                                    fftw_direction dir, int flags);

   Once the plan has been created, you can use it any number of times
for transforms of the same size.  When you do not need a plan anymore,
you can deallocate the plan by calling `fftwnd_destroy_plan(plan)'.

   Given a plan, you can compute the transform of an array of data by
calling:

     void fftwnd_one(fftwnd_plan plan, fftw_complex *in, fftw_complex *out);

   Here, `in' and `out' point to multi-dimensional arrays in row-major
order, of the size specified when the plan was created.  In the case of
an in-place transform, the `out' parameter is ignored and the output
data are stored in the `in' array.  The results are stored in-order,
unnormalized, with the zero-frequency component in `out[0]'.  A forward
followed by a backward transform (or vice-versa) yields the original
data multiplied by the size of the array (i.e. the product of the
dimensions).  *Note What FFTWND Really Computes::, for a discussion of
what FFTWND computes.

   For example, code to perform an in-place FFT of a three-dimensional
array might look like:

     #include <fftw.h>
     ...
     {
          fftw_complex in[L][M][N];
          fftwnd_plan p;
          ...
          p = fftw3d_create_plan(L, M, N, FFTW_FORWARD,
                                 FFTW_MEASURE | FFTW_IN_PLACE);
          ...
          fftwnd_one(p, &in[0][0][0], NULL);
          ...
          fftwnd_destroy_plan(p);
     }

   Note that `in' is a statically-declared array, which is
automatically in row-major order, but we must take the address of the
first element in order to fit the type expected by `fftwnd_one'.
(*Note Multi-dimensional Array Format::.)


File: fftw.info,  Node: Real One-dimensional Transforms Tutorial,  Next: Real Multi-dimensional Transforms Tutorial,  Prev: Complex Multi-dimensional Transforms Tutorial,  Up: Tutorial

Real One-dimensional Transforms Tutorial
========================================

   If the input data are purely real, you can save roughly a factor of
two in both time and storage by using the "rfftw" transforms, which are
FFTs specialized for real data.  The output of a such a transform is a
"halfcomplex" array, which consists of only half of the complex DFT
amplitudes (since the negative-frequency amplitudes for real data are
the complex conjugate of the positive-frequency amplitudes).

   In exchange for these speed and space advantages, the user sacrifices
some of the simplicity of FFTW's complex transforms.  First of all, to
allow maximum performance, the output format of the one-dimensional real
transforms is different from that used by the multi-dimensional
transforms.  Second, the inverse transform (halfcomplex to real) has the
side-effect of destroying its input array.  Neither of these
inconveniences should pose a serious problem for users, but it is
important to be aware of them.  (Both the inconvenient output format
and the side-effect of the inverse transform can be ameliorated for
one-dimensional transforms, at the expense of some performance, by using
instead the multi-dimensional transform routines with a rank of one.)

   The computation of the plan is similar to that for the complex
transforms.  First, you `#include <rfftw.h>'.  Then, you create a plan
(of type `rfftw_plan') by calling:

     rfftw_plan rfftw_create_plan(int n, fftw_direction dir, int flags);

   `n' is the length of the _real_ array in the transform (even for
halfcomplex-to-real transforms), and can be any positive integer
(although sizes with small factors are transformed more efficiently).
`dir' is either `FFTW_REAL_TO_COMPLEX' or `FFTW_COMPLEX_TO_REAL'.  The
`flags' parameter is the same as in `fftw_create_plan'.

   Once created, a plan can be used for any number of transforms, and is
deallocated when you are done with it by calling
`rfftw_destroy_plan(plan)'.

   Given a plan, a real-to-complex or complex-to-real transform is
computed by calling:

     void rfftw_one(rfftw_plan plan, fftw_real *in, fftw_real *out);

   (Note that `fftw_real' is an alias for the floating-point type for
which FFTW was compiled.)  Depending upon the direction of the plan,
either the input or the output array is halfcomplex, and is stored in
the following format:

   r0, r1, r2, r(n/2), i((n+1)/2-1), ..., i2, i1

   Here, rk is the real part of the kth output, and ik is the imaginary
part.  (We follow here the C convention that integer division is
rounded down, e.g. 7 / 2 = 3.) For a halfcomplex array `hc[]', the kth
component has its real part in `hc[k]' and its imaginary part in
`hc[n-k]', with the exception of `k' `==' `0' or `n/2' (the latter only
if n is even)--in these two cases, the imaginary part is zero due to
symmetries of the real-complex transform, and is not stored.  Thus, the
transform of `n' real values is a halfcomplex array of length `n', and
vice versa.  (1)  This is actually only half of the DFT spectrum of the
data.  Although the other half can be obtained by complex conjugation,
it is not required by many applications such as convolution and
filtering.

   Like the complex transforms, the RFFTW transforms are unnormalized,
so a forward followed by a backward transform (or vice-versa) yields the
original data scaled by the length of the array, `n'.

   Let us reiterate here our warning that an `FFTW_COMPLEX_TO_REAL'
transform has the side-effect of destroying its (halfcomplex) input.
The `FFTW_REAL_TO_COMPLEX' transform, however, leaves its (real) input
untouched, just as you would hope.

   As an example, here is an outline of how you might use RFFTW to
compute the power spectrum of a real array (i.e. the squares of the
absolute values of the DFT amplitudes):

     #include <rfftw.h>
     ...
     {
          fftw_real in[N], out[N], power_spectrum[N/2+1];
          rfftw_plan p;
          int k;
          ...
          p = rfftw_create_plan(N, FFTW_REAL_TO_COMPLEX, FFTW_ESTIMATE);
          ...
          rfftw_one(p, in, out);
          power_spectrum[0] = out[0]*out[0];  /* DC component */
          for (k = 1; k < (N+1)/2; ++k)  /* (k < N/2 rounded up) */
               power_spectrum[k] = out[k]*out[k] + out[N-k]*out[N-k];
          if (N % 2 == 0) /* N is even */
               power_spectrum[N/2] = out[N/2]*out[N/2];  /* Nyquist freq. */
          ...
          rfftw_destroy_plan(p);
     }

   Programs using RFFTW should link with `-lrfftw -lfftw -lm' on Unix,
or with the FFTW, RFFTW, and math libraries in general.

   ---------- Footnotes ----------

   (1) The output for the multi-dimensional rfftw is a
more-conventional array of `fftw_complex' values, but the format here
permitted us greater efficiency in one dimension.


File: fftw.info,  Node: Real Multi-dimensional Transforms Tutorial,  Next: Multi-dimensional Array Format,  Prev: Real One-dimensional Transforms Tutorial,  Up: Tutorial

Real Multi-dimensional Transforms Tutorial
==========================================

   FFTW includes multi-dimensional transforms for real data of any rank.
As with the one-dimensional real transforms, they save roughly a factor
of two in time and storage over complex transforms of the same size.
Also as in one dimension, these gains come at the expense of some
increase in complexity--the output format is different from the
one-dimensional RFFTW (and is more similar to that of the complex FFTW)
and the inverse (complex to real) transforms have the side-effect of
overwriting their input data.

   To use the real multi-dimensional transforms, you first `#include
<rfftw.h>' and then create a plan for the size and direction of
transform that you are interested in:

     rfftwnd_plan rfftwnd_create_plan(int rank, const int *n,
                                      fftw_direction dir, int flags);

   The first two parameters describe the size of the real data (not the
halfcomplex data, which will have different dimensions).  The last two
parameters are the same as those for `rfftw_create_plan'.  Just as for
fftwnd, there are two alternate versions of this routine,
`rfftw2d_create_plan' and `rfftw3d_create_plan', that are sometimes
more convenient for two- and three-dimensional transforms.  Also as in
fftwnd, rfftwnd supports true in-place transforms, specified by
including `FFTW_IN_PLACE' in the flags.

   Once created, a plan can be used for any number of transforms, and is
deallocated by calling `rfftwnd_destroy_plan(plan)'.

   Given a plan, the transform is computed by calling one of the
following two routines:

     void rfftwnd_one_real_to_complex(rfftwnd_plan plan,
                                      fftw_real *in, fftw_complex *out);
     void rfftwnd_one_complex_to_real(rfftwnd_plan plan,
                                      fftw_complex *in, fftw_real *out);

   As is clear from their names and parameter types, the former
function is for `FFTW_REAL_TO_COMPLEX' transforms and the latter is for
`FFTW_COMPLEX_TO_REAL' transforms.  (We could have used only a single
routine, since the direction of the transform is encoded in the plan,
but we wanted to correctly express the datatypes of the parameters.)
The latter routine, as we discuss elsewhere, has the side-effect of
overwriting its input (except when the rank of the array is one).  In
both cases, the `out' parameter is ignored for in-place transforms.

   The format of the complex arrays deserves careful attention.
Suppose that the real data has dimensions n1 x n2 x ... x nd (in
row-major order).  Then, after a real-to-complex transform, the output
is an n1 x n2 x ... x (nd/2+1) array of `fftw_complex' values in
row-major order, corresponding to slightly over half of the output of
the corresponding complex transform.  (Note that the division is
rounded down.)  The ordering of the data is otherwise exactly the same
as in the complex case.  (In principle, the output could be exactly
half the size of the complex transform output, but in more than one
dimension this requires too complicated a format to be practical.)
Note that, unlike the one-dimensional RFFTW, the real and imaginary
parts of the DFT amplitudes are here stored together in the natural way.

   Since the complex data is slightly larger than the real data, some
complications arise for in-place transforms.  In this case, the final
dimension of the real data must be padded with extra values to
accommodate the size of the complex data--two extra if the last
dimension is even and one if it is odd.  That is, the last dimension of
the real data must physically contain 2 * (nd/2+1) `fftw_real' values
(exactly enough to hold the complex data).  This physical array size
does not, however, change the _logical_ array size--only nd values are
actually stored in the last dimension, and nd is the last dimension
passed to `rfftwnd_create_plan'.

   For example, consider the transform of a two-dimensional real array
of size `nx' by `ny'.  The output of the `rfftwnd' transform is a
two-dimensional complex array of size `nx' by `ny/2+1', where the `y'
dimension has been cut nearly in half because of redundancies in the
output.  Because `fftw_complex' is twice the size of `fftw_real', the
output array is slightly bigger than the input array.  Thus, if we want
to compute the transform in place, we must _pad_ the input array so
that it is of size `nx' by `2*(ny/2+1)'.  If `ny' is even, then there
are two padding elements at the end of each row (which need not be
initialized, as they are only used for output).

   The RFFTWND transforms are unnormalized, so a forward followed by a
backward transform will result in the original data scaled by the number
of real data elements--that is, the product of the (logical) dimensions
of the real data.

   Below, we illustrate the use of RFFTWND by showing how you might use
it to compute the (cyclic) convolution of two-dimensional real arrays
`a' and `b' (using the identity that a convolution corresponds to a
pointwise product of the Fourier transforms).  For variety, in-place
transforms are used for the forward FFTs and an out-of-place transform
is used for the inverse transform.

     #include <rfftw.h>
     ...
     {
          fftw_real a[M][2*(N/2+1)], b[M][2*(N/2+1)], c[M][N];
          fftw_complex *A, *B, C[M][N/2+1];
          rfftwnd_plan p, pinv;
          fftw_real scale = 1.0 / (M * N);
          int i, j;
          ...
          p    = rfftw2d_create_plan(M, N, FFTW_REAL_TO_COMPLEX,
                                     FFTW_ESTIMATE | FFTW_IN_PLACE);
          pinv = rfftw2d_create_plan(M, N, FFTW_COMPLEX_TO_REAL,
                                     FFTW_ESTIMATE);
     
          /* aliases for accessing complex transform outputs: */
          A = (fftw_complex*) &a[0][0];
          B = (fftw_complex*) &b[0][0];
          ...
          for (i = 0; i < M; ++i)
               for (j = 0; j < N; ++j) {
                    a[i][j] = ... ;
                    b[i][j] = ... ;
               }
          ...
          rfftwnd_one_real_to_complex(p, &a[0][0], NULL);
          rfftwnd_one_real_to_complex(p, &b[0][0], NULL);
     
          for (i = 0; i < M; ++i)
               for (j = 0; j < N/2+1; ++j) {
                    int ij = i*(N/2+1) + j;
                    C[i][j].re = (A[ij].re * B[ij].re
                                  - A[ij].im * B[ij].im) * scale;
                    C[i][j].im = (A[ij].re * B[ij].im
                                  + A[ij].im * B[ij].re) * scale;
               }
     
          /* inverse transform to get c, the convolution of a and b;
             this has the side effect of overwriting C */
          rfftwnd_one_complex_to_real(pinv, &C[0][0], &c[0][0]);
          ...
          rfftwnd_destroy_plan(p);
          rfftwnd_destroy_plan(pinv);
     }

   We access the complex outputs of the in-place transforms by casting
each real array to a `fftw_complex' pointer.  Because this is a "flat"
pointer, we have to compute the row-major index `ij' explicitly in the
convolution product loop.  In order to normalize the convolution, we
must multiply by a scale factor--we can do so either before or after
the inverse transform, and choose the former because it obviates the
necessity of an additional loop.  Notice the limits of the loops and
the dimensions of the various arrays.

   As with the one-dimensional RFFTW, an out-of-place
`FFTW_COMPLEX_TO_REAL' transform has the side-effect of overwriting its
input array.  (The real-to-complex transform, on the other hand, leaves
its input array untouched.)  If you use RFFTWND for a rank-one
transform, however, this side-effect does not occur.  Because of this
fact (and the simpler output format), users may find the RFFTWND
interface more convenient than RFFTW for one-dimensional transforms.
However, RFFTWND in one dimension is slightly slower than RFFTW because
RFFTWND uses an extra buffer array internally.


File: fftw.info,  Node: Multi-dimensional Array Format,  Next: Words of Wisdom,  Prev: Real Multi-dimensional Transforms Tutorial,  Up: Tutorial

Multi-dimensional Array Format
==============================

   This section describes the format in which multi-dimensional arrays
are stored.  We felt that a detailed discussion of this topic was
necessary, since it is often a source of confusion among users and
several different formats are common.  Although the comments below
refer to `fftwnd', they are also applicable to the `rfftwnd' routines.

* Menu:

* Row-major Format::
* Column-major Format::
* Static Arrays in C::
* Dynamic Arrays in C::
* Dynamic Arrays in C-The Wrong Way::


File: fftw.info,  Node: Row-major Format,  Next: Column-major Format,  Prev: Multi-dimensional Array Format,  Up: Multi-dimensional Array Format

Row-major Format
----------------

   The multi-dimensional arrays passed to `fftwnd' are expected to be
stored as a single contiguous block in "row-major" order (sometimes
called "C order").  Basically, this means that as you step through
adjacent memory locations, the first dimension's index varies most
slowly and the last dimension's index varies most quickly.

   To be more explicit, let us consider an array of rank d whose
dimensions are n1 x n2 x n3 x ... x nd.  Now, we specify a location in
the array by a sequence of (zero-based) indices, one for each dimension:
(i1, i2, ..., id).  If the array is stored in row-major order, then
this element is located at the position id + nd * (id-1 + nd-1 * (... +
n2 * i1)).

   Note that each element of the array must be of type `fftw_complex';
i.e. a (real, imaginary) pair of (double-precision) numbers. Note also
that, in `fftwnd', the expression above is multiplied by the stride to
get the actual array index--this is useful in situations where each
element of the multi-dimensional array is actually a data structure or
another array, and you just want to transform a single field. In most
cases, however, you use a stride of 1.


File: fftw.info,  Node: Column-major Format,  Next: Static Arrays in C,  Prev: Row-major Format,  Up: Multi-dimensional Array Format

Column-major Format
-------------------

   Readers from the Fortran world are used to arrays stored in
"column-major" order (sometimes called "Fortran order").  This is
essentially the exact opposite of row-major order in that, here, the
_first_ dimension's index varies most quickly.

   If you have an array stored in column-major order and wish to
transform it using `fftwnd', it is quite easy to do.  When creating the
plan, simply pass the dimensions of the array to `fftwnd_create_plan' in
_reverse order_.  For example, if your array is a rank three `N x M x
L' matrix in column-major order, you should pass the dimensions of the
array as if it were an `L x M x N' matrix (which it is, from the
perspective of `fftwnd').  This is done for you automatically by the
FFTW Fortran wrapper routines (*note Calling FFTW from Fortran::).


File: fftw.info,  Node: Static Arrays in C,  Next: Dynamic Arrays in C,  Prev: Column-major Format,  Up: Multi-dimensional Array Format

Static Arrays in C
------------------

   Multi-dimensional arrays declared statically (that is, at compile
time, not necessarily with the `static' keyword) in C are _already_ in
row-major order.  You don't have to do anything special to transform
them.  (*Note Complex Multi-dimensional Transforms Tutorial::, for an
example of this sort of code.)


File: fftw.info,  Node: Dynamic Arrays in C,  Next: Dynamic Arrays in C-The Wrong Way,  Prev: Static Arrays in C,  Up: Multi-dimensional Array Format

Dynamic Arrays in C
-------------------

   Often, especially for large arrays, it is desirable to allocate the
arrays dynamically, at runtime.  This isn't too hard to do, although it
is not as straightforward for multi-dimensional arrays as it is for
one-dimensional arrays.

   Creating the array is simple: using a dynamic-allocation routine like
`malloc', allocate an array big enough to store N `fftw_complex'
values, where N is the product of the sizes of the array dimensions
(i.e. the total number of complex values in the array).  For example,
here is code to allocate a 5x12x27 rank 3 array:

     fftw_complex *an_array;
     
     an_array = (fftw_complex *) malloc(5 * 12 * 27 * sizeof(fftw_complex));

   Accessing the array elements, however, is more tricky--you can't
simply use multiple applications of the `[]' operator like you could for
static arrays.  Instead, you have to explicitly compute the offset into
the array using the formula given earlier for row-major arrays.  For
example, to reference the (i,j,k)-th element of the array allocated
above, you would use the expression `an_array[k + 27 * (j + 12 * i)]'.

   This pain can be alleviated somewhat by defining appropriate macros,
or, in C++, creating a class and overloading the `()' operator.


File: fftw.info,  Node: Dynamic Arrays in C-The Wrong Way,  Prev: Dynamic Arrays in C,  Up: Multi-dimensional Array Format

Dynamic Arrays in C--The Wrong Way
----------------------------------

   A different method for allocating multi-dimensional arrays in C is
often suggested that is incompatible with `fftwnd': _using it will
cause FFTW to die a painful death_.  We discuss the technique here,
however, because it is so commonly known and used.  This method is to
create arrays of pointers of arrays of pointers of ...etcetera.  For
example, the analogue in this method to the example above is:

     int i,j;
     fftw_complex ***a_bad_array;  /* another way to make a 5x12x27 array */
     
     a_bad_array = (fftw_complex ***) malloc(5 * sizeof(fftw_complex **));
     for (i = 0; i < 5; ++i) {
          a_bad_array[i] =
             (fftw_complex **) malloc(12 * sizeof(fftw_complex *));
          for (j = 0; j < 12; ++j)
               a_bad_array[i][j] =
                     (fftw_complex *) malloc(27 * sizeof(fftw_complex));
     }

   As you can see, this sort of array is inconvenient to allocate (and
deallocate).  On the other hand, it has the advantage that the
(i,j,k)-th element can be referenced simply by `a_bad_array[i][j][k]'.

   If you like this technique and want to maximize convenience in
accessing the array, but still want to pass the array to FFTW, you can
use a hybrid method.  Allocate the array as one contiguous block, but
also declare an array of arrays of pointers that point to appropriate
places in the block.  That sort of trick is beyond the scope of this
documentation; for more information on multi-dimensional arrays in C,
see the `comp.lang.c' FAQ (http://www.eskimo.com/~scs/C-faq/s6.html).


File: fftw.info,  Node: Words of Wisdom,  Prev: Multi-dimensional Array Format,  Up: Tutorial

Words of Wisdom
===============

   FFTW implements a method for saving plans to disk and restoring them.
In fact, what FFTW does is more general than just saving and loading
plans.  The mechanism is called "`wisdom'".  Here, we describe this
feature at a high level. *Note FFTW Reference::, for a less casual (but
more complete) discussion of how to use `wisdom' in FFTW.

   Plans created with the `FFTW_MEASURE' option produce near-optimal
FFT performance, but it can take a long time to compute a plan because
FFTW must actually measure the runtime of many possible plans and select
the best one.  This is designed for the situations where so many
transforms of the same size must be computed that the start-up time is
irrelevant.  For short initialization times but slightly slower
transforms, we have provided `FFTW_ESTIMATE'.  The `wisdom' mechanism
is a way to get the best of both worlds.  There are, however, certain
caveats that the user must be aware of in using `wisdom'.  For this
reason, `wisdom' is an optional feature which is not enabled by default.

   At its simplest, `wisdom' provides a way of saving plans to disk so
that they can be reused in other program runs.  You create a plan with
the flags `FFTW_MEASURE' and `FFTW_USE_WISDOM', and then save the
`wisdom' using `fftw_export_wisdom':

          plan = fftw_create_plan(..., ... | FFTW_MEASURE | FFTW_USE_WISDOM);
          fftw_export_wisdom(...);

   The next time you run the program, you can restore the `wisdom' with
`fftw_import_wisdom', and then recreate the plan using the same flags
as before.  This time, however, the same optimal plan will be created
very quickly without measurements. (FFTW still needs some time to
compute trigonometric tables, however.)  The basic outline is:

          fftw_import_wisdom(...);
          plan = fftw_create_plan(..., ... | FFTW_USE_WISDOM);

   Wisdom is more than mere rote memorization, however.  FFTW's
`wisdom' encompasses all of the knowledge and measurements that were
used to create the plan for a given size.  Therefore, existing `wisdom'
is also applied to the creation of other plans of different sizes.

   Whenever a plan is created with the `FFTW_MEASURE' and
`FFTW_USE_WISDOM' flags, `wisdom' is generated.  Thereafter, plans for
any transform with a similar factorization will be computed more
quickly, so long as they use the `FFTW_USE_WISDOM' flag.  In fact, for
transforms with the same factors and of equal or lesser size, no
measurements at all need to be made and an optimal plan can be created
with negligible delay!

   For example, suppose that you create a plan for N = 2^16.  Then, for
any equal or smaller power of two, FFTW can create a plan (with the
same direction and flags) quickly, using the precomputed `wisdom'. Even
for larger powers of two, or sizes that are a power of two times some
other prime factors, plans will be computed more quickly than they
would otherwise (although some measurements still have to be made).

   The `wisdom' is cumulative, and is stored in a global, private data
structure managed internally by FFTW.  The storage space required is
minimal, proportional to the logarithm of the sizes the `wisdom' was
generated from.  The `wisdom' can be forgotten (and its associated
memory freed) by a call to `fftw_forget_wisdom()'; otherwise, it is
remembered until the program terminates.  It can also be exported to a
file, a string, or any other medium using `fftw_export_wisdom' and
restored during a subsequent execution of the program (or a different
program) using `fftw_import_wisdom' (these functions are described
below).

   Because `wisdom' is incorporated into FFTW at a very low level, the
same `wisdom' can be used for one-dimensional transforms,
multi-dimensional transforms, and even the parallel extensions to FFTW.
Just include `FFTW_USE_WISDOM' in the flags for whatever plans you
create (i.e., always plan wisely).

   Plans created with the `FFTW_ESTIMATE' plan can use `wisdom', but
cannot generate it;  only `FFTW_MEASURE' plans actually produce
`wisdom'.  Also, plans can only use `wisdom' generated from plans
created with the same direction and flags.  For example, a size `42'
`FFTW_BACKWARD' transform will not use `wisdom' produced by a size `42'
`FFTW_FORWARD' transform.  The only exception to this rule is that
`FFTW_ESTIMATE' plans can use `wisdom' from `FFTW_MEASURE' plans.

* Menu:

* Caveats in Using Wisdom::     What you should worry about in using wisdom
* Importing and Exporting Wisdom::  I/O of wisdom to disk and other media


File: fftw.info,  Node: Caveats in Using Wisdom,  Next: Importing and Exporting Wisdom,  Prev: Words of Wisdom,  Up: Words of Wisdom

Caveats in Using Wisdom
-----------------------

     For in much wisdom is much grief, and he that increaseth knowledge
     increaseth sorrow.  [Ecclesiastes 1:18]

   There are pitfalls to using `wisdom', in that it can negate FFTW's
ability to adapt to changing hardware and other conditions. For example,
it would be perfectly possible to export `wisdom' from a program
running on one processor and import it into a program running on another
processor.  Doing so, however, would mean that the second program would
use plans optimized for the first processor, instead of the one it is
running on.

   It should be safe to reuse `wisdom' as long as the hardware and
program binaries remain unchanged. (Actually, the optimal plan may
change even between runs of the same binary on identical hardware, due
to differences in the virtual memory environment, etcetera.  Users
seriously interested in performance should worry about this problem,
too.)  It is likely that, if the same `wisdom' is used for two
different program binaries, even running on the same machine, the plans
may be sub-optimal because of differing code alignments.  It is
therefore wise to recreate `wisdom' every time an application is
recompiled.  The more the underlying hardware and software changes
between the creation of `wisdom' and its use, the greater grows the
risk of sub-optimal plans.


File: fftw.info,  Node: Importing and Exporting Wisdom,  Prev: Caveats in Using Wisdom,  Up: Words of Wisdom

Importing and Exporting Wisdom
------------------------------

     void fftw_export_wisdom_to_file(FILE *output_file);
     fftw_status fftw_import_wisdom_from_file(FILE *input_file);

   `fftw_export_wisdom_to_file' writes the `wisdom' to `output_file',
which must be a file open for writing. `fftw_import_wisdom_from_file'
reads the `wisdom' from `input_file', which must be a file open for
reading, and returns `FFTW_SUCCESS' if successful and `FFTW_FAILURE'
otherwise. In both cases, the file is left open and must be closed by
the caller.  It is perfectly fine if other data lie before or after the
`wisdom' in the file, as long as the file is positioned at the
beginning of the `wisdom' data before import.

     char *fftw_export_wisdom_to_string(void);
     fftw_status fftw_import_wisdom_from_string(const char *input_string)

   `fftw_export_wisdom_to_string' allocates a string, exports the
`wisdom' to it in `NULL'-terminated format, and returns a pointer to
the string.  If there is an error in allocating or writing the data, it
returns `NULL'.  The caller is responsible for deallocating the string
(with `fftw_free') when she is done with it.
`fftw_import_wisdom_from_string' imports the `wisdom' from
`input_string', returning `FFTW_SUCCESS' if successful and
`FFTW_FAILURE' otherwise.

   Exporting `wisdom' does not affect the store of `wisdom'. Imported
`wisdom' supplements the current store rather than replacing it (except
when there is conflicting `wisdom', in which case the older `wisdom' is
discarded). The format of the exported `wisdom' is "nerd-readable"
LISP-like ASCII text; we will not document it here except to note that
it is insensitive to white space (interested users can contact us for
more details).

   *Note FFTW Reference::, for more information, and for a description
of how you can implement `wisdom' import/export for other media besides
files and strings.

   The following is a brief example in which the `wisdom' is read from
a file, a plan is created (possibly generating more `wisdom'), and then
the `wisdom' is exported to a string and printed to `stdout'.

     {
          fftw_plan plan;
          char *wisdom_string;
          FILE *input_file;
     
          /* open file to read wisdom from */
          input_file = fopen("sample.wisdom", "r");
          if (FFTW_FAILURE == fftw_import_wisdom_from_file(input_file))
               printf("Error reading wisdom!\n");
          fclose(input_file); /* be sure to close the file! */
     
          /* create a plan for N=64, possibly creating and/or using wisdom */
          plan = fftw_create_plan(64,FFTW_FORWARD,
                                  FFTW_MEASURE | FFTW_USE_WISDOM);
     
          /* ... do some computations with the plan ... */
     
          /* always destroy plans when you are done */
          fftw_destroy_plan(plan);
     
          /* write the wisdom to a string */
          wisdom_string = fftw_export_wisdom_to_string();
          if (wisdom_string != NULL) {
               printf("Accumulated wisdom: %s\n",wisdom_string);
     
               /* Just for fun, destroy and restore the wisdom */
               fftw_forget_wisdom(); /* all gone! */
               fftw_import_wisdom_from_string(wisdom_string);
               /* wisdom is back! */
     
               fftw_free(wisdom_string); /* deallocate it since we're done */
          }
     }

