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author | Chris Cannam |
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date | Wed, 20 Mar 2013 15:35:50 +0000 |
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1 <html lang="en"> | |
2 <head> | |
3 <title>One-dimensional distributions - FFTW 3.3.3</title> | |
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11 <!-- | |
12 This manual is for FFTW | |
13 (version 3.3.3, 25 November 2012). | |
14 | |
15 Copyright (C) 2003 Matteo Frigo. | |
16 | |
17 Copyright (C) 2003 Massachusetts Institute of Technology. | |
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19 Permission is granted to make and distribute verbatim copies of | |
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24 this manual under the conditions for verbatim copying, provided | |
25 that the entire resulting derived work is distributed under the | |
26 terms of a permission notice identical to this one. | |
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47 <div class="node"> | |
48 <a name="One-dimensional-distributions"></a> | |
49 <a name="One_002ddimensional-distributions"></a> | |
50 <p> | |
51 Previous: <a rel="previous" accesskey="p" href="Transposed-distributions.html#Transposed-distributions">Transposed distributions</a>, | |
52 Up: <a rel="up" accesskey="u" href="MPI-Data-Distribution.html#MPI-Data-Distribution">MPI Data Distribution</a> | |
53 <hr> | |
54 </div> | |
55 | |
56 <h4 class="subsection">6.4.4 One-dimensional distributions</h4> | |
57 | |
58 <p>For one-dimensional distributed DFTs using FFTW, matters are slightly | |
59 more complicated because the data distribution is more closely tied to | |
60 how the algorithm works. In particular, you can no longer pass an | |
61 arbitrary block size and must accept FFTW's default; also, the block | |
62 sizes may be different for input and output. Also, the data | |
63 distribution depends on the flags and transform direction, in order | |
64 for forward and backward transforms to work correctly. | |
65 | |
66 <pre class="example"> ptrdiff_t fftw_mpi_local_size_1d(ptrdiff_t n0, MPI_Comm comm, | |
67 int sign, unsigned flags, | |
68 ptrdiff_t *local_ni, ptrdiff_t *local_i_start, | |
69 ptrdiff_t *local_no, ptrdiff_t *local_o_start); | |
70 </pre> | |
71 <p><a name="index-fftw_005fmpi_005flocal_005fsize_005f1d-383"></a> | |
72 This function computes the data distribution for a 1d transform of | |
73 size <code>n0</code> with the given transform <code>sign</code> and <code>flags</code>. | |
74 Both input and output data use block distributions. The input on the | |
75 current process will consist of <code>local_ni</code> numbers starting at | |
76 index <code>local_i_start</code>; e.g. if only a single process is used, | |
77 then <code>local_ni</code> will be <code>n0</code> and <code>local_i_start</code> will | |
78 be <code>0</code>. Similarly for the output, with <code>local_no</code> numbers | |
79 starting at index <code>local_o_start</code>. The return value of | |
80 <code>fftw_mpi_local_size_1d</code> will be the total number of elements to | |
81 allocate on the current process (which might be slightly larger than | |
82 the local size due to intermediate steps in the algorithm). | |
83 | |
84 <p>As mentioned above (see <a href="Load-balancing.html#Load-balancing">Load balancing</a>), the data will be divided | |
85 equally among the processes if <code>n0</code> is divisible by the | |
86 <em>square</em> of the number of processes. In this case, | |
87 <code>local_ni</code> will equal <code>local_no</code>. Otherwise, they may be | |
88 different. | |
89 | |
90 <p>For some applications, such as convolutions, the order of the output | |
91 data is irrelevant. In this case, performance can be improved by | |
92 specifying that the output data be stored in an FFTW-defined | |
93 “scrambled” format. (In particular, this is the analogue of | |
94 transposed output in the multidimensional case: scrambled output saves | |
95 a communications step.) If you pass <code>FFTW_MPI_SCRAMBLED_OUT</code> in | |
96 the flags, then the output is stored in this (undocumented) scrambled | |
97 order. Conversely, to perform the inverse transform of data in | |
98 scrambled order, pass the <code>FFTW_MPI_SCRAMBLED_IN</code> flag. | |
99 <a name="index-FFTW_005fMPI_005fSCRAMBLED_005fOUT-384"></a><a name="index-FFTW_005fMPI_005fSCRAMBLED_005fIN-385"></a> | |
100 | |
101 <p>In MPI FFTW, only composite sizes <code>n0</code> can be parallelized; we | |
102 have not yet implemented a parallel algorithm for large prime sizes. | |
103 | |
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106 |