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This is the first time that I have ever encountered someone who used Fortran for something other than earth sciences related modeling. Thank you, now I have a

by fhood 9y ago

This is the first time that I have ever encountered someone who used Fortran for something other than earth sciences related modeling. Thank you, now I have a second example the nest time someone claims "nobody used fortran anymore".

azag0 9y ago
I’ll say it again. If the natural language of your problem domain is linear algebra and/or matrices (tensors), Fortran is the perfect tool.
- kutkloon7 9y ago
  Why is it better than C(++) for this?
  - gjjrfcbugxbhf 9y ago
    Because for example Fortran has multi dimensional arrays as first class entities. C does not. Yes you can get the same speed in C - yes you can do the same things in C but you can do them with simpler code easier to reason about code in Fortran.
  - atemerev 9y ago
    Modern Fortran is high-level enough to pass for MATLAB, has full vectorized operations support, but in many cases faster than C. What's not to like?
  - shoo 9y ago
    Historically i believe Fortran language had stronger assumptions that variables arent aliases for the same memory. This means that the compiler can be more aggressive (than e.g. C) in terms of the generated code as it doesnt have to behave correctly if e.g. memory for unrelated arrays overlap. See https://en.wikipedia.org/wiki/Pointer_aliasing https://en.wikipedia.org/wiki/Pointer_aliasing I am not sure if this performance advantage is still the case as the wikipedia page on pointer aliasing notes that C99 added a "restrict" keyword so that the C programmer can tell the C compiler where similar strong assumptions can hold.
  - gravypod 9y ago
    Because scientists already know fortran and they're too lazy to learn something else even if there would be significantly improved results.
    - atemerev 9y ago
      Like what?
      - gravypod 9y ago
        Julia, C++ with libraries, Python with Cython or Numba, etc. You could also get much faster if most of your work is matrix multiplication if you make use of libraries like ViennaCL and a modern C++ compiler.
        ZenoArrow 9y ago
        Both Julia and Python use Fortran code under the hood to speed up matrix multiplication. Julia comes with OpenBLAS which is partially written in Fortran, and the following page of SciPy documentation indicates that a Fortran compiler is required for some NumPy modules: https://docs.scipy.org/doc/numpy-1.10.1/user/install.html https://docs.scipy.org/doc/numpy-1.10.1/user/install.html "To build any extension modules for Python, you’ll need a C compiler. Various NumPy modules use FORTRAN 77 libraries, so you’ll also need a FORTRAN 77 compiler installed."
        gravypod 9y ago
        I would also say that no one in the scientific world should be writing ASM for their programs. Does that mean I'm also telling them not to use any compiled language? No.
        ZenoArrow 9y ago
        ASM != Machine code. That aside, you haven't explained why you think the languages you listed are better suited for scientists than Fortran. What are your issues with modern Fortran?
  - AnimalMuppet 9y ago
    Because of the libraries. You say you can get matrix libraries for C++. And you're right, you can. But with Fortran, you can get (for example) functions that will solve Hermitian matrices, which run faster than solving regular matrices. Or special routines for sparse matrices, which run faster and use less memory. Or... And you could write every one of those in C++. But the Fortran ones have 30 years of debugging and optimization on them. They're solid. You can't get that by writing your equivalent in C++.
  - cls59 9y ago
    Because Fortran was purpose-built for manipulating array-based data while C++ is a general-purpose programming language. That means Fortran is optimized for this problem domain starting at the _grammar of the language_ while C++ can provide at best access to a locally optimized set of libraries. Those libraries might be quite good, but the comparative experience is still like trying to drive a Phillips-head screw using a flat-head screwdriver.
Elrac 9y ago
Sorry about the "me too" but I'm another example of someone who programs non-science in FORTRAN. I work on a crew management system for a major airline. Most of this and related applications engage in data shuffling rather than number crunching, but FORTRAN used to be one way of creating code that ran at nearly assembly language speed and size in (historically) memory constrained environments. That said, the application (and I) are nearing retirement.
shoo 9y ago
i haven't been doing any numerical computing for the last 5 or so years, but previously I was using python's numpy + scipy pretty heavily for data-science / applied-maths stuff, and under the hood quite a lot of the numerical optimisation algorithms in scipy are wrappers around old fortran libraries. so at that stage it still gets used but perhaps most people can pretend it doesnt exist. similarly, the backend of the original "random forest" machine learning algorithm for R was written in fortran, and then wrapped as an R library, but as an R user you could largely pretend that fortran didnt exist. R was a pitifully slow language so this was a good move.

by fhood 9y ago

azag0 9y ago

I’ll say it again. If the natural language of your problem domain is linear algebra and/or matrices (tensors), Fortran is the perfect tool.

kutkloon7 9y ago
Why is it better than C(++) for this?
- gjjrfcbugxbhf 9y ago
  Because for example Fortran has multi dimensional arrays as first class entities. C does not. Yes you can get the same speed in C - yes you can do the same things in C but you can do them with simpler code easier to reason about code in Fortran.
- atemerev 9y ago
  Modern Fortran is high-level enough to pass for MATLAB, has full vectorized operations support, but in many cases faster than C. What's not to like?
- shoo 9y ago
  Historically i believe Fortran language had stronger assumptions that variables arent aliases for the same memory. This means that the compiler can be more aggressive (than e.g. C) in terms of the generated code as it doesnt have to behave correctly if e.g. memory for unrelated arrays overlap. See https://en.wikipedia.org/wiki/Pointer_aliasing https://en.wikipedia.org/wiki/Pointer_aliasing I am not sure if this performance advantage is still the case as the wikipedia page on pointer aliasing notes that C99 added a "restrict" keyword so that the C programmer can tell the C compiler where similar strong assumptions can hold.
- gravypod 9y ago
  Because scientists already know fortran and they're too lazy to learn something else even if there would be significantly improved results.
  - atemerev 9y ago
    Like what?
    - gravypod 9y ago
      Julia, C++ with libraries, Python with Cython or Numba, etc. You could also get much faster if most of your work is matrix multiplication if you make use of libraries like ViennaCL and a modern C++ compiler.
      - ZenoArrow 9y ago
        Both Julia and Python use Fortran code under the hood to speed up matrix multiplication. Julia comes with OpenBLAS which is partially written in Fortran, and the following page of SciPy documentation indicates that a Fortran compiler is required for some NumPy modules: https://docs.scipy.org/doc/numpy-1.10.1/user/install.html https://docs.scipy.org/doc/numpy-1.10.1/user/install.html "To build any extension modules for Python, you’ll need a C compiler. Various NumPy modules use FORTRAN 77 libraries, so you’ll also need a FORTRAN 77 compiler installed."
        gravypod 9y ago
        I would also say that no one in the scientific world should be writing ASM for their programs. Does that mean I'm also telling them not to use any compiled language? No.
        ZenoArrow 9y ago
        ASM != Machine code. That aside, you haven't explained why you think the languages you listed are better suited for scientists than Fortran. What are your issues with modern Fortran?
- AnimalMuppet 9y ago
  Because of the libraries. You say you can get matrix libraries for C++. And you're right, you can. But with Fortran, you can get (for example) functions that will solve Hermitian matrices, which run faster than solving regular matrices. Or special routines for sparse matrices, which run faster and use less memory. Or... And you could write every one of those in C++. But the Fortran ones have 30 years of debugging and optimization on them. They're solid. You can't get that by writing your equivalent in C++.
- cls59 9y ago
  Because Fortran was purpose-built for manipulating array-based data while C++ is a general-purpose programming language. That means Fortran is optimized for this problem domain starting at the _grammar of the language_ while C++ can provide at best access to a locally optimized set of libraries. Those libraries might be quite good, but the comparative experience is still like trying to drive a Phillips-head screw using a flat-head screwdriver.

Elrac 9y ago

Sorry about the "me too" but I'm another example of someone who programs non-science in FORTRAN. I work on a crew management system for a major airline. Most of this and related applications engage in data shuffling rather than number crunching, but FORTRAN used to be one way of creating code that ran at nearly assembly language speed and size in (historically) memory constrained environments. That said, the application (and I) are nearing retirement.

shoo 9y ago

i haven't been doing any numerical computing for the last 5 or so years, but previously I was using python's numpy + scipy pretty heavily for data-science / applied-maths stuff, and under the hood quite a lot of the numerical optimisation algorithms in scipy are wrappers around old fortran libraries. so at that stage it still gets used but perhaps most people can pretend it doesnt exist. similarly, the backend of the original "random forest" machine learning algorithm for R was written in fortran, and then wrapped as an R library, but as an R user you could largely pretend that fortran didnt exist. R was a pitifully slow language so this was a good move.