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In particular, suppose that we have an n0 by n1 array in
Chris@19: row-major order, block-distributed across the n0 dimension. To
Chris@19: transpose this into an n1 by n0 array block-distributed
Chris@19: across the n1 dimension, we would create a plan by calling the
Chris@19: following function:
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fftw_plan fftw_mpi_plan_transpose(ptrdiff_t n0, ptrdiff_t n1, Chris@19: double *in, double *out, Chris@19: MPI_Comm comm, unsigned flags); Chris@19:Chris@19:
Chris@19: The input and output arrays (in and out) can be the
Chris@19: same. The transpose is actually executed by calling
Chris@19: fftw_execute on the plan, as usual.
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The flags are the usual FFTW planner flags, but support
Chris@19: two additional flags: FFTW_MPI_TRANSPOSED_OUT and/or
Chris@19: FFTW_MPI_TRANSPOSED_IN. What these flags indicate, for
Chris@19: transpose plans, is that the output and/or input, respectively, are
Chris@19: locally transposed. That is, on each process input data is
Chris@19: normally stored as a local_n0 by n1 array in row-major
Chris@19: order, but for an FFTW_MPI_TRANSPOSED_IN plan the input data is
Chris@19: stored as n1 by local_n0 in row-major order. Similarly,
Chris@19: FFTW_MPI_TRANSPOSED_OUT means that the output is n0 by
Chris@19: local_n1 instead of local_n1 by n0.
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To determine the local size of the array on each process before and
Chris@19: after the transpose, as well as the amount of storage that must be
Chris@19: allocated, one should call fftw_mpi_local_size_2d_transposed,
Chris@19: just as for a 2d DFT as described in the previous section:
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ptrdiff_t fftw_mpi_local_size_2d_transposed Chris@19: (ptrdiff_t n0, ptrdiff_t n1, MPI_Comm comm, Chris@19: ptrdiff_t *local_n0, ptrdiff_t *local_0_start, Chris@19: ptrdiff_t *local_n1, ptrdiff_t *local_1_start); Chris@19:Chris@19:
Chris@19: Again, the return value is the local storage to allocate, which in
Chris@19: this case is the number of real (double) values rather
Chris@19: than complex numbers as in the previous examples.
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