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<span id="Transposed-distributions"></span><div class="header">
<p>
Next: <a href="One_002ddimensional-distributions.html" accesskey="n" rel="next">One-dimensional distributions</a>, Previous: <a href="Load-balancing.html" accesskey="p" rel="prev">Load balancing</a>, Up: <a href="MPI-Data-Distribution.html" accesskey="u" rel="up">MPI Data Distribution</a> &nbsp; [<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="Transposed-distributions-1"></span><h4 class="subsection">6.4.3 Transposed distributions</h4>
<p>Internally, FFTW&rsquo;s MPI transform algorithms work by first computing
transforms of the data local to each process, then by globally
<em>transposing</em> the data in some fashion to redistribute the data
among the processes, transforming the new data local to each process,
and transposing back. For example, a two-dimensional <code>n0</code> by
<code>n1</code> array, distributed across the <code>n0</code> dimension, is
transformd by: (i) transforming the <code>n1</code> dimension, which are
local to each process; (ii) transposing to an <code>n1</code> by <code>n0</code>
array, distributed across the <code>n1</code> dimension; (iii) transforming
the <code>n0</code> dimension, which is now local to each process; (iv)
transposing back.
<span id="index-transpose"></span>
</p>
<p>However, in many applications it is acceptable to compute a
multidimensional DFT whose results are produced in transposed order
(e.g., <code>n1</code> by <code>n0</code> in two dimensions). This provides a
significant performance advantage, because it means that the final
transposition step can be omitted. FFTW supports this optimization,
which you specify by passing the flag <code>FFTW_MPI_TRANSPOSED_OUT</code>
to the planner routines. To compute the inverse transform of
transposed output, you specify <code>FFTW_MPI_TRANSPOSED_IN</code> to tell
it that the input is transposed. In this section, we explain how to
interpret the output format of such a transform.
<span id="index-FFTW_005fMPI_005fTRANSPOSED_005fOUT"></span>
<span id="index-FFTW_005fMPI_005fTRANSPOSED_005fIN"></span>
</p>
<p>Suppose you have are transforming multi-dimensional data with (at
least two) dimensions n<sub>0</sub>&nbsp;&times;&nbsp;n<sub>1</sub>&nbsp;&times;&nbsp;n<sub>2</sub>&nbsp;&times;&nbsp;&hellip;&nbsp;&times;&nbsp;n<sub>d-1</sub>
. As always, it is distributed along
the first dimension n<sub>0</sub>
. Now, if we compute its DFT with the
<code>FFTW_MPI_TRANSPOSED_OUT</code> flag, the resulting output data are stored
with the first <em>two</em> dimensions transposed: n<sub>1</sub>&nbsp;&times;&nbsp;n<sub>0</sub>&nbsp;&times;&nbsp;n<sub>2</sub>&nbsp;&times;&hellip;&times;&nbsp;n<sub>d-1</sub>
,
distributed along the n<sub>1</sub>
dimension. Conversely, if we take the
n<sub>1</sub>&nbsp;&times;&nbsp;n<sub>0</sub>&nbsp;&times;&nbsp;n<sub>2</sub>&nbsp;&times;&hellip;&times;&nbsp;n<sub>d-1</sub>
data and transform it with the
<code>FFTW_MPI_TRANSPOSED_IN</code> flag, then the format goes back to the
original n<sub>0</sub>&nbsp;&times;&nbsp;n<sub>1</sub>&nbsp;&times;&nbsp;n<sub>2</sub>&nbsp;&times;&nbsp;&hellip;&nbsp;&times;&nbsp;n<sub>d-1</sub>
array.
</p>
<p>There are two ways to find the portion of the transposed array that
resides on the current process. First, you can simply call the
appropriate &lsquo;<samp>local_size</samp>&rsquo; function, passing n<sub>1</sub>&nbsp;&times;&nbsp;n<sub>0</sub>&nbsp;&times;&nbsp;n<sub>2</sub>&nbsp;&times;&hellip;&times;&nbsp;n<sub>d-1</sub>
(the
transposed dimensions). This would mean calling the &lsquo;<samp>local_size</samp>&rsquo;
function twice, once for the transposed and once for the
non-transposed dimensions. Alternatively, you can call one of the
&lsquo;<samp>local_size_transposed</samp>&rsquo; functions, which returns both the
non-transposed and transposed data distribution from a single call.
For example, for a 3d transform with transposed output (or input), you
might call:
</p>
<div class="example">
<pre class="example">ptrdiff_t fftw_mpi_local_size_3d_transposed(
ptrdiff_t n0, ptrdiff_t n1, ptrdiff_t n2, MPI_Comm comm,
ptrdiff_t *local_n0, ptrdiff_t *local_0_start,
ptrdiff_t *local_n1, ptrdiff_t *local_1_start);
</pre></div>
<span id="index-fftw_005fmpi_005flocal_005fsize_005f3d_005ftransposed"></span>
<p>Here, <code>local_n0</code> and <code>local_0_start</code> give the size and
starting index of the <code>n0</code> dimension for the
<em>non</em>-transposed data, as in the previous sections. For
<em>transposed</em> data (e.g. the output for
<code>FFTW_MPI_TRANSPOSED_OUT</code>), <code>local_n1</code> and
<code>local_1_start</code> give the size and starting index of the <code>n1</code>
dimension, which is the first dimension of the transposed data
(<code>n1</code> by <code>n0</code> by <code>n2</code>).
</p>
<p>(Note that <code>FFTW_MPI_TRANSPOSED_IN</code> is completely equivalent to
performing <code>FFTW_MPI_TRANSPOSED_OUT</code> and passing the first two
dimensions to the planner in reverse order, or vice versa. If you
pass <em>both</em> the <code>FFTW_MPI_TRANSPOSED_IN</code> and
<code>FFTW_MPI_TRANSPOSED_OUT</code> flags, it is equivalent to swapping the
first two dimensions passed to the planner and passing <em>neither</em>
flag.)
</p>
<hr>
<div class="header">
<p>
Next: <a href="One_002ddimensional-distributions.html" accesskey="n" rel="next">One-dimensional distributions</a>, Previous: <a href="Load-balancing.html" accesskey="p" rel="prev">Load balancing</a>, Up: <a href="MPI-Data-Distribution.html" accesskey="u" rel="up">MPI Data Distribution</a> &nbsp; [<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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