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108 lines
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108 lines
5 KiB
Text
Executable file
This release of ScientificPython contains support for parallelization
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via the Message Passing Interface (MPI). If you don't know what that
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is, you probably don't need it, so don't read the following, and go on
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with installation. In the following I will assume that you do know
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what MPI is, and how to compile and link MPI programs on your
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platform.
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MPI support in ScientificPython does not pretend to be complete, it
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gives access to only the most important MPI function. The strong point
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of ScientificPython's interface (compared to other MPI interfaces for
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Python) is the integration into Python: communicators are Python
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objects, all communication happens via methods defined on communicator
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objects, support is provided for sending and receiving both string and
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NumPy array objects. Moreover, Python scripts can rather easily be
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written in such a way that they work both with and without MPI
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support, of course using only a single processor when no MPI is
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available. Finally, there is a full C API as well, which means that
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other C modules can make use of MPI without having to link to the MPI
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library, which is particularly useful for dynamic library modules. It
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also facilitates packaging of MPI-based code, which doesn't need to
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know anything at all about the MPI library.
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The module Scientific.MPI is documented in the ScientificPython
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manual. The main purpose of this file is to explain how to install
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ScientificPython with MPI support. It works on a couple of platforms
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without modifications, but that's no guarantee that it will work
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immediately on yours as well.
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Here is what you have to do to get MPI support in Scientific Python:
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1) Build and install Scientific Python as usual (i.e.
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"python setup.py install" in most cases).
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2) Go to the directory Src/MPI.
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3) Type "python compile.py".
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4) Move the resulting executable "mpipython" to a directory on your
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system's execution path.
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This should work under the condition that your MPI implementation
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provides a script or executable called "mpicc" which compiles C
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programs using MPI and does not require any explicitly mentioned
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libraries. If such a script exists, but with a different name, change
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the name in the beginning of compile.py. If no such script exists,
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study compile.py and adapt it as needed... However, all MPI
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implementations I have used do provide such a script.
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For running Python code that uses MPI, you must use mpipython, not the
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standard Python executable. If you use the standard python version,
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a dummy MPI module is used which behaves much like MPI with a single
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processor.
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To test your installation, execute the program Examples/mpi.py
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using more than one processor.
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If you use mpipython as the low-level support for the parallelization
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module Scientific.BSP, you can profit from the interactive parallel
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console, which is highly recommended for program development. It is
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used much like the standard Python interpreter, and is in fact
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sufficiently similar that it can be used instead of standard Python
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with the Emacs mode for Python. That combination is at the moment the
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best development environment for parallel Python code. The interactive
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parallel console is a small executable script called "impipython", to
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be found in the Src/MPI directory. It contains two hardcoded paths
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that you must adapt to your installation: the path to the mpipython
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executable, and the path to the script Concole.py in the
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ScientificPython installation. You can also change the number of
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processors to any value you wish, or add other options depending on
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your MPI installation.
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In case you wonder why a special Python executable is required, here is
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some background information:
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First of all, the MPI interface does not work with a standard Python
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interpreter. The reason is that MPI (at least some implementations,
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including MPICH) requires access to the command line parameters
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*before* Python takes over and puts them into sys.argv. Therefore
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MPI_Init() must be called before the Python main loop is entered, and
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it is also advisable to put MPI_Finalize() into a place where it is
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guaranteed to be executed even if the program crashes. The file
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Src/mpipython.c takes care of these two calls; it is in fact nothing
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but a slightly modified version of the python interpreter program,
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python.c. (In case you wonder why it's so small: all the real work is
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done in the Python library functions that are called.)
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The second problem is dynamic library support. If your MPI
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implementation comes as a dynamic library, then you shouldn't have any
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problems. If it doesn't, then it might be impossible to use the MPI
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interface module as a dynamic library, because the interpreter and the
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module would use different copies of the MPI code, which will probably
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create problems if MPI uses global variables. The safest bet is to
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link the interface module, Scientific_mpi, together with the mpipython
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executable. However, on my system (Linux/MPICH), it works fine as a
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dynamic library as well, so you might want to try it.
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Konrad Hinsen
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Centre de Biophysique Moleculaire (CNRS)
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Rue Charles Sadron
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45071 Orleans Cedex 2
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France
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E-Mail: hinsen@cnrs-orleans.fr
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