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13 This manual is for FFTW
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14 (version 3.3.3, 25 November 2012).
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15
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16 Copyright (C) 2003 Matteo Frigo.
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17
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18 Copyright (C) 2003 Massachusetts Institute of Technology.
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47 <body>
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48 <div class="node">
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49 <a name="Transposed-distributions"></a>
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50 <p>
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51 Next: <a rel="next" accesskey="n" href="One_002ddimensional-distributions.html#One_002ddimensional-distributions">One-dimensional distributions</a>,
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52 Previous: <a rel="previous" accesskey="p" href="Load-balancing.html#Load-balancing">Load balancing</a>,
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53 Up: <a rel="up" accesskey="u" href="MPI-Data-Distribution.html#MPI-Data-Distribution">MPI Data Distribution</a>
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54 <hr>
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55 </div>
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56
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57 <h4 class="subsection">6.4.3 Transposed distributions</h4>
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58
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59 <p>Internally, FFTW's MPI transform algorithms work by first computing
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60 transforms of the data local to each process, then by globally
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61 <em>transposing</em> the data in some fashion to redistribute the data
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62 among the processes, transforming the new data local to each process,
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63 and transposing back. For example, a two-dimensional <code>n0</code> by
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64 <code>n1</code> array, distributed across the <code>n0</code> dimension, is
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65 transformd by: (i) transforming the <code>n1</code> dimension, which are
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66 local to each process; (ii) transposing to an <code>n1</code> by <code>n0</code>
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67 array, distributed across the <code>n1</code> dimension; (iii) transforming
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68 the <code>n0</code> dimension, which is now local to each process; (iv)
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69 transposing back.
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70 <a name="index-transpose-379"></a>
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71
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72 <p>However, in many applications it is acceptable to compute a
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73 multidimensional DFT whose results are produced in transposed order
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74 (e.g., <code>n1</code> by <code>n0</code> in two dimensions). This provides a
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75 significant performance advantage, because it means that the final
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76 transposition step can be omitted. FFTW supports this optimization,
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77 which you specify by passing the flag <code>FFTW_MPI_TRANSPOSED_OUT</code>
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78 to the planner routines. To compute the inverse transform of
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79 transposed output, you specify <code>FFTW_MPI_TRANSPOSED_IN</code> to tell
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80 it that the input is transposed. In this section, we explain how to
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81 interpret the output format of such a transform.
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82 <a name="index-FFTW_005fMPI_005fTRANSPOSED_005fOUT-380"></a><a name="index-FFTW_005fMPI_005fTRANSPOSED_005fIN-381"></a>
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83
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84 <p>Suppose you have are transforming multi-dimensional data with (at
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85 least two) dimensions n<sub>0</sub> × n<sub>1</sub> × n<sub>2</sub> × … × n<sub>d-1</sub>. As always, it is distributed along
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86 the first dimension n<sub>0</sub>. Now, if we compute its DFT with the
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87 <code>FFTW_MPI_TRANSPOSED_OUT</code> flag, the resulting output data are stored
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88 with the first <em>two</em> dimensions transposed: n<sub>1</sub> × n<sub>0</sub> × n<sub>2</sub> ×…× n<sub>d-1</sub>,
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89 distributed along the n<sub>1</sub> dimension. Conversely, if we take the
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90 n<sub>1</sub> × n<sub>0</sub> × n<sub>2</sub> ×…× n<sub>d-1</sub> data and transform it with the
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91 <code>FFTW_MPI_TRANSPOSED_IN</code> flag, then the format goes back to the
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92 original n<sub>0</sub> × n<sub>1</sub> × n<sub>2</sub> × … × n<sub>d-1</sub> array.
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93
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94 <p>There are two ways to find the portion of the transposed array that
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95 resides on the current process. First, you can simply call the
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96 appropriate ‘<samp><span class="samp">local_size</span></samp>’ function, passing n<sub>1</sub> × n<sub>0</sub> × n<sub>2</sub> ×…× n<sub>d-1</sub> (the
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97 transposed dimensions). This would mean calling the ‘<samp><span class="samp">local_size</span></samp>’
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98 function twice, once for the transposed and once for the
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99 non-transposed dimensions. Alternatively, you can call one of the
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100 ‘<samp><span class="samp">local_size_transposed</span></samp>’ functions, which returns both the
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101 non-transposed and transposed data distribution from a single call.
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102 For example, for a 3d transform with transposed output (or input), you
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103 might call:
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104
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105 <pre class="example"> ptrdiff_t fftw_mpi_local_size_3d_transposed(
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106 ptrdiff_t n0, ptrdiff_t n1, ptrdiff_t n2, MPI_Comm comm,
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107 ptrdiff_t *local_n0, ptrdiff_t *local_0_start,
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108 ptrdiff_t *local_n1, ptrdiff_t *local_1_start);
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109 </pre>
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110 <p><a name="index-fftw_005fmpi_005flocal_005fsize_005f3d_005ftransposed-382"></a>
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111 Here, <code>local_n0</code> and <code>local_0_start</code> give the size and
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112 starting index of the <code>n0</code> dimension for the
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113 <em>non</em>-transposed data, as in the previous sections. For
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114 <em>transposed</em> data (e.g. the output for
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115 <code>FFTW_MPI_TRANSPOSED_OUT</code>), <code>local_n1</code> and
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116 <code>local_1_start</code> give the size and starting index of the <code>n1</code>
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117 dimension, which is the first dimension of the transposed data
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118 (<code>n1</code> by <code>n0</code> by <code>n2</code>).
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119
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120 <p>(Note that <code>FFTW_MPI_TRANSPOSED_IN</code> is completely equivalent to
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121 performing <code>FFTW_MPI_TRANSPOSED_OUT</code> and passing the first two
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122 dimensions to the planner in reverse order, or vice versa. If you
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123 pass <em>both</em> the <code>FFTW_MPI_TRANSPOSED_IN</code> and
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124 <code>FFTW_MPI_TRANSPOSED_OUT</code> flags, it is equivalent to swapping the
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125 first two dimensions passed to the planner and passing <em>neither</em>
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126 flag.)
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127
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128 </body></html>
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129
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