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<span id="Multi_002dthreaded-FFTW"></span><div class="header">
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Next: <a href="Distributed_002dmemory-FFTW-with-MPI.html" accesskey="n" rel="next">Distributed-memory FFTW with MPI</a>, Previous: <a href="FFTW-Reference.html" accesskey="p" rel="prev">FFTW Reference</a>, Up: <a href="index.html" accesskey="u" rel="up">Top</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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<span id="Multi_002dthreaded-FFTW-1"></span><h2 class="chapter">5 Multi-threaded FFTW</h2>
<span id="index-parallel-transform"></span>
<p>In this chapter we document the parallel FFTW routines for
shared-memory parallel hardware. These routines, which support
parallel one- and multi-dimensional transforms of both real and
complex data, are the easiest way to take advantage of multiple
processors with FFTW. They work just like the corresponding
uniprocessor transform routines, except that you have an extra
initialization routine to call, and there is a routine to set the
number of threads to employ. Any program that uses the uniprocessor
FFTW can therefore be trivially modified to use the multi-threaded
FFTW.
</p>
<p>A shared-memory machine is one in which all CPUs can directly access
the same main memory, and such machines are now common due to the
ubiquity of multi-core CPUs. FFTW’s multi-threading support allows
you to utilize these additional CPUs transparently from a single
program. However, this does not necessarily translate into
performance gains—when multiple threads/CPUs are employed, there is
an overhead required for synchronization that may outweigh the
computatational parallelism. Therefore, you can only benefit from
threads if your problem is sufficiently large.
<span id="index-shared_002dmemory"></span>
<span id="index-threads"></span>
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