5 ms·

If you are unwilling to teach through python's warts you should use Matlab, not fortran.

dubya 1y ago
I’d suggest Octave over Matlab, because current Matlab has tons of distracting AI and autocomplete front and center. Probably really helpful for getting a plot just right or implementing an algorithm from a paper, but not so good for learning the basics.
goerz 1y ago
Even better: Julia (although Fortran is pretty good!)
- adgjlsfhk1 1y ago
  I translated the jacobi example to julia, and it does seem to address every one of his gripes with Python.
  - cbolton 1y ago
    I think his main point is about strict typing in Fortran. You can add type annotations in Julia but it's almost an anti-pattern if you don't need them e.g. for dispatch. In any case the type annotations in these examples would be quite unnecessary, unlike in Fortran (where as I understand you can at best enable implicit typing but then must use variable names with specific patterns).
    - TimorousBestie 1y ago
      Numerical linear algebra is the exemplar killer app for multiple dispatch; not exploiting it would be a waste.
    - adgjlsfhk1 1y ago
      I would argue that the strict typing in Fortran is actually a significant hindrance. there's nothing about any of these algorithms that requires double precision (or even contiguous storage), so why should the algorithm randomly restrict it? Annotations of rank (e.g. AbstractMatrix in julia) help document what the code does, but the strict annotations of Fortran are restrictions without value.
- QuadmasterXLII 1y ago
  I love julia, but the default workflow is Step 1) Write the function using high level abstractions Step 2) Glance over the generated assembly and make sure that it vectorized the way you wanted.
  - mofeing 1y ago
    > Glance over the generated assembly and make sure that it vectorized the way you wanted. Isn't that sth you would also need to do in Fortran? IMO Julia makes this so easy with its `@code_*` macros and is one of the main reasons why I use it.
    - wtcactus 1y ago
      In my experience, Fortran compiler is heavily optimized. It competes head to head with C. Julia’s on the other hand, many times puts out very unoptimized code. Mind you, last time I looked at Julia was 2-3 years ago, maybe things have changed.
      - patagurbon 1y ago
        If you write Julia similar to Fortran, with explicit argument types and for loops and avoiding allocations it shouldn’t be too far off. Fortran IIRC has a few semantics which might make it more optimal in a few cases like aliasing But indeed there are almost certainly less performance surprises in Fortran
  - TimorousBestie 1y ago
    This is the default workflow in every high-level language. Even if I’m writing explicit SIMD intrinsics in C targeting a specific processor, I still have to benchmark and maybe look at the assembly to make sure it did what I intended (or something better).
kergonath 1y ago
Fortran is much more approachable and more regular than Matlab. Really, there’s no contest.
a-dub 1y ago
this is the way. octave or matlab. people like to complain about matlab as a programming language but if you're using it that way you're doing it wrong. matlab (the core language) is awesome for expressing matrices and vectors and their operations as well as visualizing the results. matrix expressions in matlab look almost identical to how they look in mathematical notation (or how one might write them in an email). you shouldn't be using programming language flow control (or any of the other programming language features), you should be learning how to write for loops as vector and matrix operations and learning from the excellent toolboxes.
- OkayPhysicist 1y ago
  IMO, the issue is that "Scientific computing" covers several disparate use cases. When you care about the math, Mathematica. It's a replacement for several pages of hand-written math, or a chalkboard. When you care about the result, MatLab. It's a replacement for your calculator, and maybe Excel. When you care about the resulting software? Python/Julia/Fortran.

I’d suggest Octave over Matlab, because current Matlab has tons of distracting AI and autocomplete front and center. Probably really helpful for getting a plot just right or implementing an algorithm from a paper, but not so good for learning the basics.

goerz 1y ago

Even better: Julia (although Fortran is pretty good!)

adgjlsfhk1 1y ago
I translated the jacobi example to julia, and it does seem to address every one of his gripes with Python.
- cbolton 1y ago
  I think his main point is about strict typing in Fortran. You can add type annotations in Julia but it's almost an anti-pattern if you don't need them e.g. for dispatch. In any case the type annotations in these examples would be quite unnecessary, unlike in Fortran (where as I understand you can at best enable implicit typing but then must use variable names with specific patterns).
  - TimorousBestie 1y ago
    Numerical linear algebra is the exemplar killer app for multiple dispatch; not exploiting it would be a waste.
  - adgjlsfhk1 1y ago
    I would argue that the strict typing in Fortran is actually a significant hindrance. there's nothing about any of these algorithms that requires double precision (or even contiguous storage), so why should the algorithm randomly restrict it? Annotations of rank (e.g. AbstractMatrix in julia) help document what the code does, but the strict annotations of Fortran are restrictions without value.
QuadmasterXLII 1y ago
I love julia, but the default workflow is Step 1) Write the function using high level abstractions Step 2) Glance over the generated assembly and make sure that it vectorized the way you wanted.
- mofeing 1y ago
  > Glance over the generated assembly and make sure that it vectorized the way you wanted. Isn't that sth you would also need to do in Fortran? IMO Julia makes this so easy with its `@code_*` macros and is one of the main reasons why I use it.
  - wtcactus 1y ago
    In my experience, Fortran compiler is heavily optimized. It competes head to head with C. Julia’s on the other hand, many times puts out very unoptimized code. Mind you, last time I looked at Julia was 2-3 years ago, maybe things have changed.
    - patagurbon 1y ago
      If you write Julia similar to Fortran, with explicit argument types and for loops and avoiding allocations it shouldn’t be too far off. Fortran IIRC has a few semantics which might make it more optimal in a few cases like aliasing But indeed there are almost certainly less performance surprises in Fortran
- TimorousBestie 1y ago
  This is the default workflow in every high-level language. Even if I’m writing explicit SIMD intrinsics in C targeting a specific processor, I still have to benchmark and maybe look at the assembly to make sure it did what I intended (or something better).

kergonath 1y ago

Fortran is much more approachable and more regular than Matlab. Really, there’s no contest.

a-dub 1y ago

this is the way. octave or matlab. people like to complain about matlab as a programming language but if you're using it that way you're doing it wrong. matlab (the core language) is awesome for expressing matrices and vectors and their operations as well as visualizing the results. matrix expressions in matlab look almost identical to how they look in mathematical notation (or how one might write them in an email). you shouldn't be using programming language flow control (or any of the other programming language features), you should be learning how to write for loops as vector and matrix operations and learning from the excellent toolboxes.

OkayPhysicist 1y ago
IMO, the issue is that "Scientific computing" covers several disparate use cases. When you care about the math, Mathematica. It's a replacement for several pages of hand-written math, or a chalkboard. When you care about the result, MatLab. It's a replacement for your calculator, and maybe Excel. When you care about the resulting software? Python/Julia/Fortran.