The Fortran stdlib project has garnered over 1000 stars on GitHub!

Great news: the Fortran stdlib project has garnered over 1000 stars on GitHub!

Currently stdlib includes many utilities (hash maps, strings, IO, logging,…), sorting procedures), and many mathematics-related procedures (linear algebra, statistics, random numbers),…).

The largest and most recent addition to stdlib is indubiously the whole set of BLAS/LAPACK procedures, a project led by @FedericoPerini .

Thank you to all the contributors! I strongly believe that stdlib, in combination with fpm, makes Fortran even more accessible!

More to come soon!


And today the Fortran Discourse has exactly 1600 registered users. :tada:


Thank you @jeremie.vandenplas and congrats to all the stdlib developers for the achievement!
Excited to share that after the BLAS/LAPACK modernization, now a nearly NumPy-full set of dense linear algebra functions is being made available via stdlib:

Determinant API
Stdlib x = det(A [, overwrite_A=.false.] [, err=state])
x = .det.A ! can be chained
NumPy x = det(A)
SciPy x = det(A [, overwrite_A=False] [, check_finite=True])
Singular Value Decomposition API
Stdlib call svd(a,s [,u] [,vt] [,overwrite_a] [,full_matrices] [,err]) ! whole svd
S = svdvals(a [, err]) ! values only
NumPy U,S,Vh = svd(a, full_matrices=True, compute_uv=True, hermitian=False)
S = svdvals(a)
SciPy U,S,Vh = svd(a, full_matrices=True, compute_uv=True, overwrite_a=False, check_finite=True, lapack_driver="gesdd")
S = svdvals(a [,overwrite_A=False] [, check_finite=True])
Linear System (LU) API
Stdlib x = solve(A, b [, overwrite_A=.false.] [, err=state])
call solve_lu(A, b, x [, pivot] [, overwrite_A] [, err])
NumPy x = solve(A, b)
SciPy x = solve(A [, lower=False] [, overwrite_A=False] [, overwrite_b=False] [, check_finite=True] [,assume_a=’gen’] [,transposed=False])
Least Squares API
Stdlib x = lstsq(A, b [, cond] [, overwrite_A=.false.] [, err=state])
call solve_lstsq(A, b, x [, real_storage] [, int_storage] [, cmpl_storage] [, cond] [, singvals] [,overwrite_a] [, rank] [, err])
call lstsq_space(a, b, lrwork, liwork, lcwork)
NumPy x = lstsq(A, b [, rcond])
SciPy x = lstsq(A, b [, cond=None] [,overwrite_a=False] [,overwrite_b=False] [, check_finite=True] [, lapack_driver=None])
Eigendecomposition API
Stdlib lambda = eigvals(a [, err])
call eig (a, lambda [,right] [,left] [,overwrite_a] [,err])
NumPy lambda, v = eig(a)
lambda = eigvals(a)
SciPy lambda = eig(a, b=None, left=False, right=True, overwrite_a=False, overwrite_b=False, check_finite=True, homogeneous_eigvals=False)
Matrix inverse API
Stdlib Am1 = inv(A [, err])
call invert (a [,pivot] [,err])
call invert (a, Am1 [,pivot] [,err])
Am1 = .inv.A
NumPy x = inv(A)
SciPy x = inv(A [, overwrite_A=False] [, check_finite=True])
QR factorization API
Stdlib call qr (a,q,r [,overwrite_a] [,storage] [,err])
call qr_space(a, lwork [,err])
NumPy Q, R = qr(a [, mode=’reduced’])
SciPy Q, R, p = qr(a [, overwrite_a=False] [, lwork=None] [, mode='full'] [, pivoting=False] [, check_finite=True])
Cholesky factorization API
Stdlib call cholesky (a, lower [, other_zeroed=.true.] [,err])
call cholesky (a, c, lower [, other_zeroed=.true.] [,err])
L = chol(a, lower [, other_zeroed=.true.])
NumPy L = cholesky(a, /, *, upper=False)
SciPy L = cholesky(a, lower=False, overwrite_a=False, check_finite=True)
  • Most of the interfaces are pure, such as most BLAS/LAPACK backends
  • All operations work for real and complex matrices
  • All KINDs supported: 32, 64, 80 and 128 bits
  • Enable fast external BLAS/LAPACK with 1 preprocessor macro


LLVM Flang top-of-tree is compiling stdlib pretty well but not yet perfect.

The following tests FAILED:
          9 - hash_functions (Failed)
         37 - mean (SEGFAULT)
         55 - string_to_string (Failed)
Errors while running CTest

This is really a nice addition! And I am wondering if this can be tested possibly online somehow…? (e.g. Compiler Explorer + adding stdlib option…?) (even if no, no problem at all)

thank you @septc. provides stdlib-fpm as a library.


As mentioned by @jeremie.vandenplas you can find stdlib along with some other fpm packages on compiler explorer. Keep in mind however that stdlib on compiler explorer lives at HEAD (i.e. it’s the latest commit on the main branch, and you can’t select a specific version).