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			129 lines
		
	
	
		
			6.2 KiB
		
	
	
	
		
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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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								manual under the conditions for verbatim copying, provided that the
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								Permission is granted to copy and distribute translations of this manual
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								into another language, under the above conditions for modified versions,
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								except that this permission notice may be stated in a translation
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								<title>One-dimensional distributions (FFTW 3.3.10)</title>
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								<meta name="description" content="One-dimensional distributions (FFTW 3.3.10)">
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								<link href="Concept-Index.html" rel="index" title="Concept Index">
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								<link href="index.html#SEC_Contents" rel="contents" title="Table of Contents">
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								<link href="MPI-Data-Distribution.html" rel="up" title="MPI Data Distribution">
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								<link href="Multi_002ddimensional-MPI-DFTs-of-Real-Data.html" rel="next" title="Multi-dimensional MPI DFTs of Real Data">
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								<span id="One_002ddimensional-distributions"></span><div class="header">
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								<p>
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								Previous: <a href="Transposed-distributions.html" accesskey="p" rel="prev">Transposed distributions</a>, Up: <a href="MPI-Data-Distribution.html" accesskey="u" rel="up">MPI Data Distribution</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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								</div>
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								<hr>
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								<span id="One_002ddimensional-distributions-1"></span><h4 class="subsection">6.4.4 One-dimensional distributions</h4>
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								<p>For one-dimensional distributed DFTs using FFTW, matters are slightly
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								more complicated because the data distribution is more closely tied to
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								how the algorithm works.  In particular, you can no longer pass an
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								arbitrary block size and must accept FFTW’s default; also, the block
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								sizes may be different for input and output.  Also, the data
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								distribution depends on the flags and transform direction, in order
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								for forward and backward transforms to work correctly.
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								</p>
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								<div class="example">
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								<pre class="example">ptrdiff_t fftw_mpi_local_size_1d(ptrdiff_t n0, MPI_Comm comm,
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								                int sign, unsigned flags,
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								                ptrdiff_t *local_ni, ptrdiff_t *local_i_start,
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								                ptrdiff_t *local_no, ptrdiff_t *local_o_start);
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								</pre></div>
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								<span id="index-fftw_005fmpi_005flocal_005fsize_005f1d"></span>
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								<p>This function computes the data distribution for a 1d transform of
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								size <code>n0</code> with the given transform <code>sign</code> and <code>flags</code>.
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								Both input and output data use block distributions.  The input on the
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								current process will consist of <code>local_ni</code> numbers starting at
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								index <code>local_i_start</code>; e.g. if only a single process is used,
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								then <code>local_ni</code> will be <code>n0</code> and <code>local_i_start</code> will
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								be <code>0</code>.  Similarly for the output, with <code>local_no</code> numbers
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								starting at index <code>local_o_start</code>.  The return value of
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								<code>fftw_mpi_local_size_1d</code> will be the total number of elements to
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								allocate on the current process (which might be slightly larger than
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								the local size due to intermediate steps in the algorithm).
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								</p>
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								<p>As mentioned above (see <a href="Load-balancing.html">Load balancing</a>), the data will be divided
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								equally among the processes if <code>n0</code> is divisible by the
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								<em>square</em> of the number of processes.  In this case,
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								<code>local_ni</code> will equal <code>local_no</code>.  Otherwise, they may be
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								different.
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								</p>
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								<p>For some applications, such as convolutions, the order of the output
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								data is irrelevant.  In this case, performance can be improved by
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								specifying that the output data be stored in an FFTW-defined
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								“scrambled” format.  (In particular, this is the analogue of
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								transposed output in the multidimensional case: scrambled output saves
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								a communications step.)  If you pass <code>FFTW_MPI_SCRAMBLED_OUT</code> in
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								the flags, then the output is stored in this (undocumented) scrambled
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								order.  Conversely, to perform the inverse transform of data in
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								scrambled order, pass the <code>FFTW_MPI_SCRAMBLED_IN</code> flag.
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								<span id="index-FFTW_005fMPI_005fSCRAMBLED_005fOUT"></span>
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								<span id="index-FFTW_005fMPI_005fSCRAMBLED_005fIN"></span>
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								</p>
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								<p>In MPI FFTW, only composite sizes <code>n0</code> can be parallelized; we
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								have not yet implemented a parallel algorithm for large prime sizes.
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								</p>
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								<hr>
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								<div class="header">
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								<p>
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								Previous: <a href="Transposed-distributions.html" accesskey="p" rel="prev">Transposed distributions</a>, Up: <a href="MPI-Data-Distribution.html" accesskey="u" rel="up">MPI Data Distribution</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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