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Next: <a href="Combining-MPI-and-Threads.html#Combining-MPI-and-Threads" accesskey="n" rel="next">Combining MPI and Threads</a>, Previous: <a href="Avoiding-MPI-Deadlocks.html#Avoiding-MPI-Deadlocks" accesskey="p" rel="prev">Avoiding MPI Deadlocks</a>, Up: <a href="Distributed_002dmemory-FFTW-with-MPI.html#Distributed_002dmemory-FFTW-with-MPI" accesskey="u" rel="up">Distributed-memory FFTW with MPI</a> &nbsp; [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>][<a href="Concept-Index.html#Concept-Index" title="Index" rel="index">Index</a>]</p>
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<a name="FFTW-MPI-Performance-Tips-1"></a>
<h3 class="section">6.10 FFTW MPI Performance Tips</h3>

<p>In this section, we collect a few tips on getting the best performance
out of FFTW&rsquo;s MPI transforms.
</p>
<p>First, because of the 1d block distribution, FFTW&rsquo;s parallelization is
currently limited by the size of the first dimension.
(Multidimensional block distributions may be supported by a future
version.) More generally, you should ideally arrange the dimensions so
that FFTW can divide them equally among the processes. See <a href="Load-balancing.html#Load-balancing">Load balancing</a>.
<a name="index-block-distribution-2"></a>
<a name="index-load-balancing-1"></a>
</p>

<p>Second, if it is not too inconvenient, you should consider working
with transposed output for multidimensional plans, as this saves a
considerable amount of communications.  See <a href="Transposed-distributions.html#Transposed-distributions">Transposed distributions</a>.
<a name="index-transpose-3"></a>
</p>

<p>Third, the fastest choices are generally either an in-place transform
or an out-of-place transform with the <code>FFTW_DESTROY_INPUT</code> flag
(which allows the input array to be used as scratch space).  In-place
is especially beneficial if the amount of data per process is large.
<a name="index-FFTW_005fDESTROY_005fINPUT-1"></a>
</p>

<p>Fourth, if you have multiple arrays to transform at once, rather than
calling FFTW&rsquo;s MPI transforms several times it usually seems to be
faster to interleave the data and use the advanced interface.  (This
groups the communications together instead of requiring separate
messages for each transform.)
</p>


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author	Chris Cannam
date	Tue, 18 Oct 2016 13:40:26 +0100
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