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cannam@95: Next: <a rel="next" accesskey="n" href="Advanced-distributed_002dtranspose-interface.html#Advanced-distributed_002dtranspose-interface">Advanced distributed-transpose interface</a>,
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cannam@95: <h4 class="subsection">6.7.1 Basic distributed-transpose interface</h4>
cannam@95:
cannam@95: <p>In particular, suppose that we have an <code>n0</code> by <code>n1</code> array in
cannam@95: row-major order, block-distributed across the <code>n0</code> dimension. To
cannam@95: transpose this into an <code>n1</code> by <code>n0</code> array block-distributed
cannam@95: across the <code>n1</code> dimension, we would create a plan by calling the
cannam@95: following function:
cannam@95:
cannam@95: <pre class="example"> fftw_plan fftw_mpi_plan_transpose(ptrdiff_t n0, ptrdiff_t n1,
cannam@95: double *in, double *out,
cannam@95: MPI_Comm comm, unsigned flags);
cannam@95: </pre>
cannam@95: <p><a name="index-fftw_005fmpi_005fplan_005ftranspose-397"></a>
cannam@95: The input and output arrays (<code>in</code> and <code>out</code>) can be the
cannam@95: same. The transpose is actually executed by calling
cannam@95: <code>fftw_execute</code> on the plan, as usual.
cannam@95: <a name="index-fftw_005fexecute-398"></a>
cannam@95:
cannam@95: <p>The <code>flags</code> are the usual FFTW planner flags, but support
cannam@95: two additional flags: <code>FFTW_MPI_TRANSPOSED_OUT</code> and/or
cannam@95: <code>FFTW_MPI_TRANSPOSED_IN</code>. What these flags indicate, for
cannam@95: transpose plans, is that the output and/or input, respectively, are
cannam@95: <em>locally</em> transposed. That is, on each process input data is
cannam@95: normally stored as a <code>local_n0</code> by <code>n1</code> array in row-major
cannam@95: order, but for an <code>FFTW_MPI_TRANSPOSED_IN</code> plan the input data is
cannam@95: stored as <code>n1</code> by <code>local_n0</code> in row-major order. Similarly,
cannam@95: <code>FFTW_MPI_TRANSPOSED_OUT</code> means that the output is <code>n0</code> by
cannam@95: <code>local_n1</code> instead of <code>local_n1</code> by <code>n0</code>.
cannam@95: <a name="index-FFTW_005fMPI_005fTRANSPOSED_005fOUT-399"></a><a name="index-FFTW_005fMPI_005fTRANSPOSED_005fIN-400"></a>
cannam@95:
cannam@95: <p>To determine the local size of the array on each process before and
cannam@95: after the transpose, as well as the amount of storage that must be
cannam@95: allocated, one should call <code>fftw_mpi_local_size_2d_transposed</code>,
cannam@95: just as for a 2d DFT as described in the previous section:
cannam@95: <a name="index-data-distribution-401"></a>
cannam@95: <pre class="example"> ptrdiff_t fftw_mpi_local_size_2d_transposed
cannam@95: (ptrdiff_t n0, ptrdiff_t n1, MPI_Comm comm,
cannam@95: ptrdiff_t *local_n0, ptrdiff_t *local_0_start,
cannam@95: ptrdiff_t *local_n1, ptrdiff_t *local_1_start);
cannam@95: </pre>
cannam@95: <p><a name="index-fftw_005fmpi_005flocal_005fsize_005f2d_005ftransposed-402"></a>
cannam@95: Again, the return value is the local storage to allocate, which in
cannam@95: this case is the number of <em>real</em> (<code>double</code>) values rather
cannam@95: than complex numbers as in the previous examples.
cannam@95:
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