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We still are using FORTRAN for Business Applications and I fail to see the need to switch them to a slower language to modernize them. Unless the switch is solv

by baldfat 11y ago

We still are using FORTRAN for Business Applications and I fail to see the need to switch them to a slower language to modernize them. Unless the switch is solving a program leave them alone. This is why in R there is still plenty of FORTRAN code. Personally I LOVE Python but I always feel it is the 2nd best choice. I do use Python but it rarely is the best tool to use for the problem you are solving. Maybe Pypy will turn this around, and I hope so.

pjmlp 11y ago
I don't understand numerical analysis as business applications. For me business applications are what you would use Cobol, Clipper, Java, Delphi, C#, Eifel, .... Of course Fortran makes sense for number crushing, but that is the language domain, not doing CRUD, ETL or distributed computing stuff (not counting MPI here).
- nickpsecurity 11y ago
  Accounting, spreadsheets, transaction processing, business intelligence?
  - pjmlp 11y ago
    Of course those are business applications as well. Do you intend to refer to a product done in those areas Fortran in modern days, maybe with Fortran 2008? Outside scientific computing and heavy number crushing algorithms and libraries, I don't see a use for it.
    - nickpsecurity 11y ago
      No, I'm just saying they're business applications. A segment of business applications are numerically focused. Fortran handles them well. So, in that segment, it can be considered a language for business applications with numeric focus. I'm not saying anyone should use it today lol. Julia and R are the best for that sort of stuff. Way the hell ahead of Fortran. Although I thought about making a nice web application in Fortran just to screw with people who eventually try to extend it and gasp in horror. ;) http://www.aim.env.uea.ac.uk/aim/info/fortran.pdf http://www.aim.env.uea.ac.uk/aim/info/fortran.pdf
jahewson 11y ago
R != Business Application.
- baldfat 11y ago
  Used R as an example of a language that still uses FORTRAN. BUT I think Microsoft and Business Analysis people would not agree with you.
jzd 11y ago
How can SciPy be fast if it is written in an interpreted language like Python? Actually, the time-critical loops are usually implemented in C or Fortran. Much of SciPy is a thin layer of code on top of the scientific routines that are freely available at http://www.netlib.org/ http://www.netlib.org/. Netlib is a huge repository of incredibly valuable and robust scientific algorithms written in C and Fortran. It would be silly to rewrite these algorithms and would take years to debug them. SciPy uses a variety of methods to generate “wrappers” around these algorithms so that they can be used in Python. Some wrappers were generated by hand coding them in C. The rest were generated using either SWIG or f2py. Some of the newer contributions to SciPy are either written entirely or wrapped with Cython. A second answer is that for difficult problems, a better algorithm can make a tremendous difference in the time it takes to solve a problem. So using scipy’s built-in algorithms may be much faster than a simple algorithm coded in C. http://www.scipy.org/scipylib/faq.html#how-can-scipy-be-fast-if-it-is-written-in-an-interpreted-language-like-python http://www.scipy.org/scipylib/faq.html#how-can-scipy-be-fast...
- semi-extrinsic 11y ago
  This is also why the scipy/numpy crowd doesn't care much about PyPy. In this world Python is the equivalent of ducttape. You don't performance optimize ducttape, you want it to be easy to use and to fix any problem you have.
  - camaraj 11y ago
    PyPy is taking a similar approach. The difference is Numpy is using CPython's CAPI as the duct tape where as PyPy is using cffi as the duct tape. Both approaches end up using the same underlying libraries. Too many people just have the wrong impression, thinking that PyPy plans on re-implementing all the libraries that Numpy and scipy use but that's just completely false. They have re-implemented the Numpy array so that it can take advantage of the JIT and so that parts of an algorithm implemented In Python that uses Numpy can also be optimized. Unlike what occurs when using Numpy under CPython where the Python code does not get optimized unless it is converted to Cython, C Code, or some alternative to Python to have it be optimized.
rch 11y ago
I like the quote: "Python is generally the second-best language for everything".

by baldfat 11y ago

pjmlp 11y ago

I don't understand numerical analysis as business applications. For me business applications are what you would use Cobol, Clipper, Java, Delphi, C#, Eifel, .... Of course Fortran makes sense for number crushing, but that is the language domain, not doing CRUD, ETL or distributed computing stuff (not counting MPI here).

nickpsecurity 11y ago
Accounting, spreadsheets, transaction processing, business intelligence?
- pjmlp 11y ago
  Of course those are business applications as well. Do you intend to refer to a product done in those areas Fortran in modern days, maybe with Fortran 2008? Outside scientific computing and heavy number crushing algorithms and libraries, I don't see a use for it.
  - nickpsecurity 11y ago
    No, I'm just saying they're business applications. A segment of business applications are numerically focused. Fortran handles them well. So, in that segment, it can be considered a language for business applications with numeric focus. I'm not saying anyone should use it today lol. Julia and R are the best for that sort of stuff. Way the hell ahead of Fortran. Although I thought about making a nice web application in Fortran just to screw with people who eventually try to extend it and gasp in horror. ;) http://www.aim.env.uea.ac.uk/aim/info/fortran.pdf http://www.aim.env.uea.ac.uk/aim/info/fortran.pdf

jahewson 11y ago

R != Business Application.

baldfat 11y ago
Used R as an example of a language that still uses FORTRAN. BUT I think Microsoft and Business Analysis people would not agree with you.

jzd 11y ago

How can SciPy be fast if it is written in an interpreted language like Python? Actually, the time-critical loops are usually implemented in C or Fortran. Much of SciPy is a thin layer of code on top of the scientific routines that are freely available at http://www.netlib.org/ http://www.netlib.org/. Netlib is a huge repository of incredibly valuable and robust scientific algorithms written in C and Fortran. It would be silly to rewrite these algorithms and would take years to debug them. SciPy uses a variety of methods to generate “wrappers” around these algorithms so that they can be used in Python. Some wrappers were generated by hand coding them in C. The rest were generated using either SWIG or f2py. Some of the newer contributions to SciPy are either written entirely or wrapped with Cython. A second answer is that for difficult problems, a better algorithm can make a tremendous difference in the time it takes to solve a problem. So using scipy’s built-in algorithms may be much faster than a simple algorithm coded in C. http://www.scipy.org/scipylib/faq.html#how-can-scipy-be-fast-if-it-is-written-in-an-interpreted-language-like-python http://www.scipy.org/scipylib/faq.html#how-can-scipy-be-fast...

semi-extrinsic 11y ago
This is also why the scipy/numpy crowd doesn't care much about PyPy. In this world Python is the equivalent of ducttape. You don't performance optimize ducttape, you want it to be easy to use and to fix any problem you have.
- camaraj 11y ago
  PyPy is taking a similar approach. The difference is Numpy is using CPython's CAPI as the duct tape where as PyPy is using cffi as the duct tape. Both approaches end up using the same underlying libraries. Too many people just have the wrong impression, thinking that PyPy plans on re-implementing all the libraries that Numpy and scipy use but that's just completely false. They have re-implemented the Numpy array so that it can take advantage of the JIT and so that parts of an algorithm implemented In Python that uses Numpy can also be optimized. Unlike what occurs when using Numpy under CPython where the Python code does not get optimized unless it is converted to Cython, C Code, or some alternative to Python to have it be optimized.

rch 11y ago

I like the quote: "Python is generally the second-best language for everything".