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|  | <!-- This manual is for FFTW
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|  | (version 3.3.10, 10 December 2020). | ||
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|  | Copyright (C) 2003 Matteo Frigo. | ||
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|  | <span id="Multi_002ddimensional-MPI-DFTs-of-Real-Data"></span><div class="header"> | ||
|  | <p> | ||
|  | Next: <a href="Other-Multi_002ddimensional-Real_002ddata-MPI-Transforms.html" accesskey="n" rel="next">Other Multi-dimensional Real-data MPI Transforms</a>, Previous: <a href="MPI-Data-Distribution.html" accesskey="p" rel="prev">MPI Data Distribution</a>, Up: <a href="Distributed_002dmemory-FFTW-with-MPI.html" accesskey="u" rel="up">Distributed-memory FFTW with MPI</a>   [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>][<a href="Concept-Index.html" title="Index" rel="index">Index</a>]</p> | ||
|  | </div> | ||
|  | <hr> | ||
|  | <span id="Multi_002ddimensional-MPI-DFTs-of-Real-Data-1"></span><h3 class="section">6.5 Multi-dimensional MPI DFTs of Real Data</h3> | ||
|  | 
 | ||
|  | <p>FFTW’s MPI interface also supports multi-dimensional DFTs of real | ||
|  | data, similar to the serial r2c and c2r interfaces.  (Parallel | ||
|  | one-dimensional real-data DFTs are not currently supported; you must | ||
|  | use a complex transform and set the imaginary parts of the inputs to | ||
|  | zero.) | ||
|  | </p> | ||
|  | <p>The key points to understand for r2c and c2r MPI transforms (compared | ||
|  | to the MPI complex DFTs or the serial r2c/c2r transforms), are: | ||
|  | </p> | ||
|  | <ul> | ||
|  | <li> Just as for serial transforms, r2c/c2r DFTs transform n<sub>0</sub> × n<sub>1</sub> × n<sub>2</sub> × … × n<sub>d-1</sub> | ||
|  |  real | ||
|  | data to/from n<sub>0</sub> × n<sub>1</sub> × n<sub>2</sub> × … × (n<sub>d-1</sub>/2 + 1) | ||
|  |  complex data: the last dimension of the | ||
|  | complex data is cut in half (rounded down), plus one.  As for the | ||
|  | serial transforms, the sizes you pass to the ‘<samp>plan_dft_r2c</samp>’ and | ||
|  | ‘<samp>plan_dft_c2r</samp>’ are the n<sub>0</sub> × n<sub>1</sub> × n<sub>2</sub> × … × n<sub>d-1</sub> | ||
|  |  dimensions of the real data. | ||
|  | 
 | ||
|  | </li><li> <span id="index-padding-4"></span> | ||
|  | Although the real data is <em>conceptually</em> n<sub>0</sub> × n<sub>1</sub> × n<sub>2</sub> × … × n<sub>d-1</sub> | ||
|  | , it is | ||
|  | <em>physically</em> stored as an n<sub>0</sub> × n<sub>1</sub> × n<sub>2</sub> × … × [2 (n<sub>d-1</sub>/2 + 1)] | ||
|  |  array, where the last | ||
|  | dimension has been <em>padded</em> to make it the same size as the | ||
|  | complex output.  This is much like the in-place serial r2c/c2r | ||
|  | interface (see <a href="Multi_002dDimensional-DFTs-of-Real-Data.html">Multi-Dimensional DFTs of Real Data</a>), except that | ||
|  | in MPI the padding is required even for out-of-place data.  The extra | ||
|  | padding numbers are ignored by FFTW (they are <em>not</em> like | ||
|  | zero-padding the transform to a larger size); they are only used to | ||
|  | determine the data layout. | ||
|  | 
 | ||
|  | </li><li> <span id="index-data-distribution-3"></span> | ||
|  | The data distribution in MPI for <em>both</em> the real and complex data | ||
|  | is determined by the shape of the <em>complex</em> data.  That is, you | ||
|  | call the appropriate ‘<samp>local size</samp>’ function for the n<sub>0</sub> × n<sub>1</sub> × n<sub>2</sub> × … × (n<sub>d-1</sub>/2 + 1) | ||
|  | 
 | ||
|  | complex data, and then use the <em>same</em> distribution for the real | ||
|  | data except that the last complex dimension is replaced by a (padded) | ||
|  | real dimension of twice the length. | ||
|  | 
 | ||
|  | </li></ul> | ||
|  | 
 | ||
|  | <p>For example suppose we are performing an out-of-place r2c transform of | ||
|  | L × M × N | ||
|  |  real data [padded to L × M × 2(N/2+1) | ||
|  | ], | ||
|  | resulting in L × M × N/2+1 | ||
|  |  complex data.  Similar to the | ||
|  | example in <a href="2d-MPI-example.html">2d MPI example</a>, we might do something like: | ||
|  | </p> | ||
|  | <div class="example"> | ||
|  | <pre class="example">#include <fftw3-mpi.h> | ||
|  | 
 | ||
|  | int main(int argc, char **argv) | ||
|  | { | ||
|  |     const ptrdiff_t L = ..., M = ..., N = ...; | ||
|  |     fftw_plan plan; | ||
|  |     double *rin; | ||
|  |     fftw_complex *cout; | ||
|  |     ptrdiff_t alloc_local, local_n0, local_0_start, i, j, k; | ||
|  | 
 | ||
|  |     MPI_Init(&argc, &argv); | ||
|  |     fftw_mpi_init(); | ||
|  | 
 | ||
|  |     /* <span class="roman">get local data size and allocate</span> */ | ||
|  |     alloc_local = fftw_mpi_local_size_3d(L, M, N/2+1, MPI_COMM_WORLD, | ||
|  |                                          &local_n0, &local_0_start); | ||
|  |     rin = fftw_alloc_real(2 * alloc_local); | ||
|  |     cout = fftw_alloc_complex(alloc_local); | ||
|  | 
 | ||
|  |     /* <span class="roman">create plan for out-of-place r2c DFT</span> */ | ||
|  |     plan = fftw_mpi_plan_dft_r2c_3d(L, M, N, rin, cout, MPI_COMM_WORLD, | ||
|  |                                     FFTW_MEASURE); | ||
|  | 
 | ||
|  |     /* <span class="roman">initialize rin to some function</span> my_func(x,y,z) */ | ||
|  |     for (i = 0; i < local_n0; ++i) | ||
|  |        for (j = 0; j < M; ++j) | ||
|  |          for (k = 0; k < N; ++k) | ||
|  |        rin[(i*M + j) * (2*(N/2+1)) + k] = my_func(local_0_start+i, j, k); | ||
|  | 
 | ||
|  |     /* <span class="roman">compute transforms as many times as desired</span> */ | ||
|  |     fftw_execute(plan); | ||
|  | 
 | ||
|  |     fftw_destroy_plan(plan); | ||
|  | 
 | ||
|  |     MPI_Finalize(); | ||
|  | } | ||
|  | </pre></div> | ||
|  | 
 | ||
|  | <span id="index-fftw_005falloc_005freal-2"></span> | ||
|  | <span id="index-row_002dmajor-5"></span> | ||
|  | <p>Note that we allocated <code>rin</code> using <code>fftw_alloc_real</code> with an | ||
|  | argument of <code>2 * alloc_local</code>: since <code>alloc_local</code> is the | ||
|  | number of <em>complex</em> values to allocate, the number of <em>real</em> | ||
|  | values is twice as many.  The <code>rin</code> array is then | ||
|  | local_n0 × M × 2(N/2+1) | ||
|  |  in row-major order, so its | ||
|  | <code>(i,j,k)</code> element is at the index <code>(i*M + j) * (2*(N/2+1)) + | ||
|  | k</code> (see <a href="Multi_002ddimensional-Array-Format.html">Multi-dimensional Array Format</a>). | ||
|  | </p> | ||
|  | <span id="index-transpose-1"></span> | ||
|  | <span id="index-FFTW_005fTRANSPOSED_005fOUT"></span> | ||
|  | <span id="index-FFTW_005fTRANSPOSED_005fIN"></span> | ||
|  | <p>As for the complex transforms, improved performance can be obtained by | ||
|  | specifying that the output is the transpose of the input or vice versa | ||
|  | (see <a href="Transposed-distributions.html">Transposed distributions</a>).  In our L × M × N | ||
|  |  r2c | ||
|  | example, including <code>FFTW_TRANSPOSED_OUT</code> in the flags means that | ||
|  | the input would be a padded L × M × 2(N/2+1) | ||
|  |  real array | ||
|  | distributed over the <code>L</code> dimension, while the output would be a | ||
|  | M × L × N/2+1 | ||
|  |  complex array distributed over the <code>M</code> | ||
|  | dimension.  To perform the inverse c2r transform with the same data | ||
|  | distributions, you would use the <code>FFTW_TRANSPOSED_IN</code> flag. | ||
|  | </p> | ||
|  | <hr> | ||
|  | <div class="header"> | ||
|  | <p> | ||
|  | Next: <a href="Other-Multi_002ddimensional-Real_002ddata-MPI-Transforms.html" accesskey="n" rel="next">Other Multi-dimensional Real-data MPI Transforms</a>, Previous: <a href="MPI-Data-Distribution.html" accesskey="p" rel="prev">MPI Data Distribution</a>, Up: <a href="Distributed_002dmemory-FFTW-with-MPI.html" accesskey="u" rel="up">Distributed-memory FFTW with MPI</a>   [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>][<a href="Concept-Index.html" title="Index" rel="index">Index</a>]</p> | ||
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