5 ms·

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?
        gravypod 9y ago
        Modern library availability, larger pool of programmers, better compiler technologies, more features that apply to modern hardware, cleaner code, and better documentation are all present on my languages. They are not present on FORTRAN.
    - kronos29296 9y ago
      Numpy, Scipy and a lot of other numerical libraries use Fortran for the underlying operations along with C. Because Fortran is just faster for such operations and will continue to be. Numba is still very much alpha. Julia is still immature for production (Good language from what I hear but still very much in development). Don't know if C++ can ever be as fast as Fortran because all fortran compilers are optimized for the architecture on which they run. (Best fortran compiler for intel cpus is by intel). As for ViennaCL I don't know much about GPU programming and its performance. Never done it. (My only exp is with Cython and Scipy stuff)
- crispyambulance 9y ago
  No, because computer scientists are too lazy to create modern domain specific languages that are powerful enough for practitioners to put to effective use. :-)
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.

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?
        gravypod 9y ago
        Modern library availability, larger pool of programmers, better compiler technologies, more features that apply to modern hardware, cleaner code, and better documentation are all present on my languages. They are not present on FORTRAN.
  - kronos29296 9y ago
    Numpy, Scipy and a lot of other numerical libraries use Fortran for the underlying operations along with C. Because Fortran is just faster for such operations and will continue to be. Numba is still very much alpha. Julia is still immature for production (Good language from what I hear but still very much in development). Don't know if C++ can ever be as fast as Fortran because all fortran compilers are optimized for the architecture on which they run. (Best fortran compiler for intel cpus is by intel). As for ViennaCL I don't know much about GPU programming and its performance. Never done it. (My only exp is with Cython and Scipy stuff)
crispyambulance 9y ago
No, because computer scientists are too lazy to create modern domain specific languages that are powerful enough for practitioners to put to effective use. :-)

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.