Abstract:

Python increasingly is being utilized as a powerful scientific processing language. It successfully has been used as a glue language to drive simulations written in C, Fortran or the array manipulation language provided by the NumPy package. Originally Python only was used as a glue language because the original Python implementation was relatively slow. With the recent progress in the PyPy project that is showing significant performance improvements in each release, Python is nearing performance comparable to native C language implementations. In this talk I will describe three stages: how to use it right now, in the near future and our plans to provide a very robust infrastructure for implementing numerical computations. I also will spend some time exploring ideas how dynamic compilation eventually can outperform static compilation and how a high-level language helps accomplish this.