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FORTRAN programs

Course and self-directed work in FORTRAN for numerical methods, linear algebra routines, and small scientific simulations—typical of engineering and physics curricula.

Problem​

Many legacy and high-performance workflows still rely on numerically stable, compiler-optimized Fortran for tight loops. The learning goal is to understand memory layout, array semantics, and floating-point behavior without hiding details behind higher-level frameworks.

Solution​

Implement classic algorithms (e.g. numerical integration, eigenvalue steps, or PDE discretizations) with clear module boundaries, explicit interfaces where appropriate, and reproducible build instructions.

Impact​

  • Builds intuition for performance-sensitive code paths used in research and industry HPC.
  • Portable numeric cores can be wrapped by Python, C, or modern Fortran interfaces later.

Lessons learned​

  • Compiler flags and optimization levels change answers slightly—always document the test matrix.
  • Prefer modern Fortran features (modules, intent, allocatables) over fixed legacy patterns when allowed.
  • Pair Fortran numerics with small golden-file tests or comparisons to known solutions.

Tech stack​

  • Language: Fortran (F90/F95 or later per course)
  • Build: Make, CMake, or vendor toolchain (e.g. gfortran, Intel Fortran)
  • Optional: Python or plotting tools for post-processing results