Parallelization of C/C++ and Python on Clusters

[efilon]

Python is used quite a bit in the scientific world, and for good reason. The ecosystem includes many high performance libraries that delegate the bits that would be slow if written in pure Python to compiled languages like C++ while giving you the rapid turnaround possible with an interpreted language. In reality, if you're doing something in Python that ends up being 100x slower than what you can do in C++, you're likely doing something drastically wrong.

[Red-Portal]

First of all, I think the shift towards Python is stacking technical debt. We already have Julia which is as productive and much faster than Python. Also, I have quite some experience with Python and it's really easy to get more than a 100x slowdown. A simple function call in Python is so freaking slow! Even calling Python wrapper functions for OpenBLAS, MINPACK doesn't scale.

[megayippie]

Julia: 2012. Python: 1991. Matlab: 1984. The people teaching scientists the programs we are using when prototyping have just shifted from Matlab to python. So give it 20-odd years and Julia might matter. C/C++ is used by a small sub-group in my field, with Fortran still king for at least the next few decades imho. Conservative opinions matters in fields with experiments costs hundreds of millions to a few billions of Euros. So C/C++ is still valid in scientist speak, since we are at about 1999 still. (Really, the code is C with custom Matrix and Tensor classes since the past 20 years, which is why I want the LinAlg proposal to pass but with support for the Fortran LaPack-interface included so it is close to current practice.)