Managed language for scientific computing software

Question

Scientific computing is algorithm intensive and can also be data intensive. It often needs to use a lot of memory to run analysis and release it before continuing with the next. Sometime it also uses memory pool to recycle memory for each analysis. Managed language is interesting here because it can allow the developer to concentrate on the application logic. Since it might need to deal with huge dataset, performance is important too. But how can we control memory and performance with managed language?

Answer 1

Python has become pretty big in scientific computing lately. It is a managed language, so you don't have to remember to free your memory. At the same time, it has packages for scientific and numerical computing (NumPy, SciPy), which gives you performance similar to compiled languages. Also, Python can be pretty easily integrated with C code.

Python is a very expressive language, making it easier to write and read than many traditional languages. It also resembles MATLAB in some ways, making it easier to use for scientists than, say, C++ or Fortran.

The University of Oslo has recently starting teaching Python as the default language for all science students outside the department of informatics (who still learn Java).

Simula Research Laboratory, which is heavily into scientific computing, partial differential equations etc., uses python extensively.

Answer 2

You are asking a fundamentally flawed question. The entire point of managed languages is that you don't handle memory. That is handled by the Garbage Collector, while you can make certain actions to better allow it to do its job in an efficient manner, it is not your job to do its job.

The things you can do to improve performance in a world where performance is not controlled by you are simple. Make sure you don't hold onto references you don't need. And use stack based variables if you need more control over the situation.