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author Chris Cannam
date Wed, 20 Mar 2013 15:35:50 +0000
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56
57 <h2 class="chapter">5 Multi-threaded FFTW</h2>
58
59 <p><a name="index-parallel-transform-326"></a>In this chapter we document the parallel FFTW routines for
60 shared-memory parallel hardware. These routines, which support
61 parallel one- and multi-dimensional transforms of both real and
62 complex data, are the easiest way to take advantage of multiple
63 processors with FFTW. They work just like the corresponding
64 uniprocessor transform routines, except that you have an extra
65 initialization routine to call, and there is a routine to set the
66 number of threads to employ. Any program that uses the uniprocessor
67 FFTW can therefore be trivially modified to use the multi-threaded
68 FFTW.
69
70 <p>A shared-memory machine is one in which all CPUs can directly access
71 the same main memory, and such machines are now common due to the
72 ubiquity of multi-core CPUs. FFTW's multi-threading support allows
73 you to utilize these additional CPUs transparently from a single
74 program. However, this does not necessarily translate into
75 performance gains&mdash;when multiple threads/CPUs are employed, there is
76 an overhead required for synchronization that may outweigh the
77 computatational parallelism. Therefore, you can only benefit from
78 threads if your problem is sufficiently large.
79 <a name="index-shared_002dmemory-327"></a><a name="index-threads-328"></a>
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