  The purpose of this paper is to show how existing scientific software can be
parallelized using a separate thin layer of Python code where all parallel
communication is implemented. We provide specific examples on such layers of
code, and these examples may act as templates for parallelizing a wide set of
serial scientific codes. The use of Python for parallelization is motivated by
the fact that the language is well suited for reusing existing serial codes
programmed in other languages. The extreme flexibility of Python with regard to
handling functions makes it very easy to wrap up decomposed computational tasks
of a serial scientific application as Python functions. Many
parallelization-specific components can be implemented as generic Python
functions, which may take as input those functions that perform concrete
computational tasks. The overall programming effort needed by this
parallelization approach is rather limited, and the resulting parallel Python
scripts have a compact and clean structure. The usefulness of the
parallelization approach is exemplified by three different classes of
applications in natural and social sciences.
