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