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			109 lines
		
	
	
		
			5.2 KiB
		
	
	
	
		
			HTML
		
	
	
	
	
	
<!DOCTYPE html PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
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<!-- This manual is for FFTW
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(version 3.3.10, 10 December 2020).
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Copyright (C) 2003 Matteo Frigo.
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Copyright (C) 2003 Massachusetts Institute of Technology.
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Permission is granted to make and distribute verbatim copies of this
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Permission is granted to copy and distribute modified versions of this
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<title>Multi-threaded FFTW (FFTW 3.3.10)</title>
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<span id="Multi_002dthreaded-FFTW"></span><div class="header">
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<p>
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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>
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<span id="index-parallel-transform"></span>
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<p>In this chapter we document the parallel FFTW routines for
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shared-memory parallel hardware.  These routines, which support
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parallel one- and multi-dimensional transforms of both real and
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complex data, are the easiest way to take advantage of multiple
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processors with FFTW.  They work just like the corresponding
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uniprocessor transform routines, except that you have an extra
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initialization routine to call, and there is a routine to set the
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number of threads to employ.  Any program that uses the uniprocessor
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FFTW can therefore be trivially modified to use the multi-threaded
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FFTW.
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</p>
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<p>A shared-memory machine is one in which all CPUs can directly access
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the same main memory, and such machines are now common due to the
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ubiquity of multi-core CPUs.  FFTW’s multi-threading support allows
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you to utilize these additional CPUs transparently from a single
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program.  However, this does not necessarily translate into
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performance gains—when multiple threads/CPUs are employed, there is
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an overhead required for synchronization that may outweigh the
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computatational parallelism.  Therefore, you can only benefit from
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threads if your problem is sufficiently large.
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<span id="index-shared_002dmemory"></span>
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<span id="index-threads"></span>
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</p>
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<tr><td align="left" valign="top">• <a href="Installation-and-Supported-Hardware_002fSoftware.html" accesskey="1">Installation and Supported Hardware/Software</a></td><td>  </td><td align="left" valign="top">
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</td></tr>
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<tr><td align="left" valign="top">• <a href="Usage-of-Multi_002dthreaded-FFTW.html" accesskey="2">Usage of Multi-threaded FFTW</a></td><td>  </td><td align="left" valign="top">
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</td></tr>
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<tr><td align="left" valign="top">• <a href="How-Many-Threads-to-Use_003f.html" accesskey="3">How Many Threads to Use?</a></td><td>  </td><td align="left" valign="top">
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</td></tr>
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<tr><td align="left" valign="top">• <a href="Thread-safety.html" accesskey="4">Thread safety</a></td><td>  </td><td align="left" valign="top">
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