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The code for the example can be found on the Florida State University Department of Scientific Computing site under the name hello_openmp.f90 (GNU LGPL license). ) statements go to standard output (stdout). The only change is to convert it from a "program" to a "subroutine." No arguments are passed to the subroutine in this case, and all output ( write The modifications to the initial code are simple. Īs a first example, the simple "hello world" hw.f90 OpenMP code is used as a starting point for creating a Python module.
You can find some good documentation on the NumPy and SciPy documentation page and some good basic introductions online. F2PY accesses Fortran module data from Python, allowing Python functions to be called from Fortran or C (callbacks) and automatically handling the difference between multidimensional Fortran and Python array data storage order. The tool named F2PY, Fortran-to-Python, creates a connection between the two languages that calls Fortran 77/90/95 code, Fortran 90/95 modules, and C functions from Python. To achieve this, I use F2PY to build the module. In this article, I take a look at what's possible by writing Python modules in Fortran that use all of the cores on a node or a subset of nodes (user controllable). Writing parallel functions in Python is very difficult, but it's fairly straightforward in C and Fortran with the use of a variety of abstractions, including OpenMP, which provides a path for Python functions to utilize all of the available cores. For example, you can write extensions in Fortran and C as Python modules with tools such as SWIG, Pyfort, and F2PY.
), and the mpi4py bindings of the Message Passing Interface (MPI) standard for writing MPI code in Python.Īnother cool aspect of Python is that it's possible to write extensions in other languages that can be loaded as modules using "wrapper" tools. However, Python code by itself is limited to a single thread because of the almighty global interpreter lock (GIL), "a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes at once." Given servers with 32 cores per socket, it seems somewhat wasteful to use just one core when programming in Python.Ī number of tools and modules allow you to write multiprocessing or multithreaded code, including the multiprocessing module that comes in the standard library, Parallel Python, variations on queuing systems such as 0MQ ( zeromq Python is arguably one of the most popular languages in use today and is being used more and more in HPC.
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