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### 6.4 MPI Data Distribution

The most important concept to understand in using FFTW’s MPI interface is the data distribution. With a serial or multithreaded FFT, all of the inputs and outputs are stored as a single contiguous chunk of memory. With a distributed-memory FFT, the inputs and outputs are broken into disjoint blocks, one per process.

In particular, FFTW uses a 1d block distribution of the data, distributed along the first dimension. For example, if you want to perform a 100 × 200 complex DFT, distributed over 4 processes, each process will get a 25 × 200 slice of the data. That is, process 0 will get rows 0 through 24, process 1 will get rows 25 through 49, process 2 will get rows 50 through 74, and process 3 will get rows 75 through 99. If you take the same array but distribute it over 3 processes, then it is not evenly divisible so the different processes will have unequal chunks. FFTW’s default choice in this case is to assign 34 rows to processes 0 and 1, and 32 rows to process 2.

FFTW provides several ‘`fftw_mpi_local_size`’ routines that you can call to find out what portion of an array is stored on the current process. In most cases, you should use the default block sizes picked by FFTW, but it is also possible to specify your own block size. For example, with a 100 × 200 array on three processes, you can tell FFTW to use a block size of 40, which would assign 40 rows to processes 0 and 1, and 20 rows to process 2. FFTW’s default is to divide the data equally among the processes if possible, and as best it can otherwise. The rows are always assigned in “rank order,” i.e. process 0 gets the first block of rows, then process 1, and so on. (You can change this by using `MPI_Comm_split` to create a new communicator with re-ordered processes.) However, you should always call the ‘`fftw_mpi_local_size`’ routines, if possible, rather than trying to predict FFTW’s distribution choices.

In particular, it is critical that you allocate the storage size that is returned by ‘`fftw_mpi_local_size`’, which is not necessarily the size of the local slice of the array. The reason is that intermediate steps of FFTW’s algorithms involve transposing the array and redistributing the data, so at these intermediate steps FFTW may require more local storage space (albeit always proportional to the total size divided by the number of processes). The ‘`fftw_mpi_local_size`’ functions know how much storage is required for these intermediate steps and tell you the correct amount to allocate.

Next: Multi-dimensional MPI DFTs of Real Data, Previous: 2d MPI example, Up: Distributed-memory FFTW with MPI [Contents][Index]