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Distributed-memory FFTW with MPI

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5 Multi-threaded FFTW
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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.

   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.  

Installation and Supported Hardware/Software

Usage of Multi-threaded FFTW

How Many Threads to Use?

Thread safety