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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.