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For a paper I want to argue why I have used Python for the implementation of my algorithm. Besides the typical arguments that it is fast -using suitable libraries- and it is easy to implement the algorithm with it, I thought maybe there are some big HPC projects that are using it.
Does anyone know a famous project that uses Python for large parallel calculations, maybe with a paper which I can cite?
To be honest, as great a language as python is, it wouldn’t be a suitable environment for scientific computing and in particular high performance computing, if those libraries weren’t available. So you can see python as one pieces of a larger puzzle – much as MATLAB can be.
The two key reasons to use python for scientific or high-performance computing can then be said to be because of the convenient interfaces to software packages written in other languages, or because you need fast turn around on a project. Commonly, both issues arise at the time.
The classic example of this is the paper “Feeding a Large-scale Physics Application to Python”, by David M. Beazley which combines performance intensive C++ with python using SWIG
If you’re looking for something very current, there is a new paper, “A New Modelling System for Seasonal Streamflow Forecasting Service of the Bureau of Meteorology, Australia”, by Daehyok Shin et al., that due to be presented at MODSIM2011. I saw the first author speak at the Melbourne Python Users Group about how ipython was used being used as a mechanism for bridging high performance fortran models and HDF5 data in such a way that even non-programmers could make effective contributions to a larger scientific program.