Paul Rubin <no.email@nospam.invalid> wrote: > Sturla Molden <sturla.mol...@gmail.com> writes: >> When should we use C++ or Fortran instead of Python? Ever? > > When performance matters?
Sometimes, but usually performance only matters in certain types of calculations like matrix algebra or FFTs. But we always use specialized libraries for that, such as Intel MKL, OpenBLAS, AMD ACML, or Apple's Accelerate Framework. "When performance matters" is not really a good answer, because performance might not matter where we think or as much as we think. For examle if I were to write a Levenberg-Marquardt solver, I would e.g. find that 95% of the time is spent solving the linearized least-squares equations (J' J + lambda diag(J' J)) dx = J' error using QR or SVD. If I used LAPACK method DGELS or DGELSS to solve these equations with QR or SVD, I would find that my otherwise idential Fortran and Python code would perform roughly the same. None of the performance libraries I mentioned cares if I am calling them from Python or Fortran. The differences in runtime would thus be in the 5 % spent outside the performance libraries. And if i/o is more efficient in Python, which is very likely, it might even be that the Python version runs faster. So the question is, when does it matter for me? Now and then, perhaps, but very rarely. For example it did matter when we wrote the cKDTree object for SciPy. But it never matters when I use it. I will make the bold claim that 90 % of the use of C++ or Fortran code in scientific computing stems from the misconception that "writing everything" in a compiled language will be faster. I have done this mistake myself, and lost valuable time. I have promised myself I will not do it again, and then realized I have done the same mistake yet again. It is a very dangerous misconception. Always prototype in Python first, then profile, then choose between optimization or using a bigger computer. Starting out by writing C, C++, Cython or Fortran is a trap. Yet many do, myself included, because we errorneously trust ourselves to know when we should just use Python. But the thing is, nobody does, we just think we do. Sturla -- https://mail.python.org/mailman/listinfo/python-list
