fftw3: Distributed-memory FFTW with MPI
6 Distributed-memory FFTW with MPI
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In this chapter we document the parallel FFTW routines for parallel
systems supporting the MPI message-passing interface. Unlike the
shared-memory threads described in the previous chapter, MPI allows you
to use _distributed-memory_ parallelism, where each CPU has its own
separate memory, and which can scale up to clusters of many thousands of
processors. This capability comes at a price, however: each process
only stores a _portion_ of the data to be transformed, which means that
the data structures and programming-interface are quite different from
the serial or threads versions of FFTW.
Distributed-memory parallelism is especially useful when you are
transforming arrays so large that they do not fit into the memory of a
single processor. The storage per-process required by FFTW's MPI
routines is proportional to the total array size divided by the number
of processes. Conversely, distributed-memory parallelism can easily
pose an unacceptably high communications overhead for small problems;
the threshold problem size for which parallelism becomes advantageous
will depend on the precise problem you are interested in, your hardware,
and your MPI implementation.
A note on terminology: in MPI, you divide the data among a set of
"processes" which each run in their own memory address space.
Generally, each process runs on a different physical processor, but this
is not required. A set of processes in MPI is described by an opaque
data structure called a "communicator," the most common of which is the
predefined communicator 'MPI_COMM_WORLD' which refers to _all_
processes. For more information on these and other concepts common to
all MPI programs, we refer the reader to the documentation at the MPI
home page (http://www.mcs.anl.gov/research/projects/mpi/).
We assume in this chapter that the reader is familiar with the usage
of the serial (uniprocessor) FFTW, and focus only on the concepts new to
the MPI interface.
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