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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>MPI Data Distribution - FFTW 3.3.3</title>
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cannam@95 13 This manual is for FFTW
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cannam@95 49 <a name="MPI-Data-Distribution"></a>
cannam@95 50 <p>
cannam@95 51 Next:&nbsp;<a rel="next" accesskey="n" href="Multi_002ddimensional-MPI-DFTs-of-Real-Data.html#Multi_002ddimensional-MPI-DFTs-of-Real-Data">Multi-dimensional MPI DFTs of Real Data</a>,
cannam@95 52 Previous:&nbsp;<a rel="previous" accesskey="p" href="2d-MPI-example.html#g_t2d-MPI-example">2d MPI example</a>,
cannam@95 53 Up:&nbsp;<a rel="up" accesskey="u" href="Distributed_002dmemory-FFTW-with-MPI.html#Distributed_002dmemory-FFTW-with-MPI">Distributed-memory FFTW with MPI</a>
cannam@95 54 <hr>
cannam@95 55 </div>
cannam@95 56
cannam@95 57 <h3 class="section">6.4 MPI Data Distribution</h3>
cannam@95 58
cannam@95 59 <p><a name="index-data-distribution-368"></a>
cannam@95 60 The most important concept to understand in using FFTW's MPI interface
cannam@95 61 is the data distribution. With a serial or multithreaded FFT, all of
cannam@95 62 the inputs and outputs are stored as a single contiguous chunk of
cannam@95 63 memory. With a distributed-memory FFT, the inputs and outputs are
cannam@95 64 broken into disjoint blocks, one per process.
cannam@95 65
cannam@95 66 <p>In particular, FFTW uses a <em>1d block distribution</em> of the data,
cannam@95 67 distributed along the <em>first dimension</em>. For example, if you
cannam@95 68 want to perform a 100&nbsp;&times;&nbsp;200 complex DFT, distributed over 4
cannam@95 69 processes, each process will get a 25&nbsp;&times;&nbsp;200 slice of the data.
cannam@95 70 That is, process 0 will get rows 0 through 24, process 1 will get rows
cannam@95 71 25 through 49, process 2 will get rows 50 through 74, and process 3
cannam@95 72 will get rows 75 through 99. If you take the same array but
cannam@95 73 distribute it over 3 processes, then it is not evenly divisible so the
cannam@95 74 different processes will have unequal chunks. FFTW's default choice
cannam@95 75 in this case is to assign 34 rows to processes 0 and 1, and 32 rows to
cannam@95 76 process 2.
cannam@95 77 <a name="index-block-distribution-369"></a>
cannam@95 78
cannam@95 79 <p>FFTW provides several &lsquo;<samp><span class="samp">fftw_mpi_local_size</span></samp>&rsquo; routines that you can
cannam@95 80 call to find out what portion of an array is stored on the current
cannam@95 81 process. In most cases, you should use the default block sizes picked
cannam@95 82 by FFTW, but it is also possible to specify your own block size. For
cannam@95 83 example, with a 100&nbsp;&times;&nbsp;200 array on three processes, you can
cannam@95 84 tell FFTW to use a block size of 40, which would assign 40 rows to
cannam@95 85 processes 0 and 1, and 20 rows to process 2. FFTW's default is to
cannam@95 86 divide the data equally among the processes if possible, and as best
cannam@95 87 it can otherwise. The rows are always assigned in &ldquo;rank order,&rdquo;
cannam@95 88 i.e. process 0 gets the first block of rows, then process 1, and so
cannam@95 89 on. (You can change this by using <code>MPI_Comm_split</code> to create a
cannam@95 90 new communicator with re-ordered processes.) However, you should
cannam@95 91 always call the &lsquo;<samp><span class="samp">fftw_mpi_local_size</span></samp>&rsquo; routines, if possible,
cannam@95 92 rather than trying to predict FFTW's distribution choices.
cannam@95 93
cannam@95 94 <p>In particular, it is critical that you allocate the storage size that
cannam@95 95 is returned by &lsquo;<samp><span class="samp">fftw_mpi_local_size</span></samp>&rsquo;, which is <em>not</em>
cannam@95 96 necessarily the size of the local slice of the array. The reason is
cannam@95 97 that intermediate steps of FFTW's algorithms involve transposing the
cannam@95 98 array and redistributing the data, so at these intermediate steps FFTW
cannam@95 99 may require more local storage space (albeit always proportional to
cannam@95 100 the total size divided by the number of processes). The
cannam@95 101 &lsquo;<samp><span class="samp">fftw_mpi_local_size</span></samp>&rsquo; functions know how much storage is required
cannam@95 102 for these intermediate steps and tell you the correct amount to
cannam@95 103 allocate.
cannam@95 104
cannam@95 105 <ul class="menu">
cannam@95 106 <li><a accesskey="1" href="Basic-and-advanced-distribution-interfaces.html#Basic-and-advanced-distribution-interfaces">Basic and advanced distribution interfaces</a>
cannam@95 107 <li><a accesskey="2" href="Load-balancing.html#Load-balancing">Load balancing</a>
cannam@95 108 <li><a accesskey="3" href="Transposed-distributions.html#Transposed-distributions">Transposed distributions</a>
cannam@95 109 <li><a accesskey="4" href="One_002ddimensional-distributions.html#One_002ddimensional-distributions">One-dimensional distributions</a>
cannam@95 110 </ul>
cannam@95 111
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