Consider the following snippet

using SparseArrays
using LinearAlgebra

function setup(n)
	e = ones(n)
	e2 = -ones(n-1)
	K = spdiagm(n, n, 0 => 2.0e, 1 => e2, -1 => e2)
	Id = sparse(1.0*I, n, n)
	return kron(K, Id) + kron(Id, K)
end

function solve(n)
	A = setup(n)
	F = ones(n^2)
	@time A \ F
end

It just builds a discretization of a PDE and solves it. Now let us execute

solve(1156);
solve(1157);

Using julia 1.9.3 I get

  2.095628 seconds (66 allocations: 1.339 GiB, 0.08% gc time)
  2.134111 seconds (66 allocations: 1.339 GiB, 0.08% gc time)

and all is fine. With julia 1.10.4 instead I get

  1.966967 seconds (69 allocations: 1.339 GiB, 0.17% gc time)
  7.775528 seconds (1.99 M allocations: 9.856 GiB, 2.97% gc time)

Thus, the second solve on a slightly larger system is suddenly much slower and allocates memory. The profiling shows that cholmod_l_analyze runs much longer. The same happens with the RC of 1.11.

Digging a little bit deeper with

A = setup(1157);
B = SparseArrays.CHOLMOD.Sparse(A);
@time F = SparseArrays.CHOLMOD.symbolic(B, perm=nothing);
@time cholesky!(F, A; shift = 1., check = true);

and looking at the output of top, one can see that symbolic runs only on a single core and allocates memory, cholesky! seems to be fine (and seems to run in parallel).

I also tried to fiddle around with

SparseArrays.CHOLMOD.getcommon()[].nthreads_max

(which is 1) after startup, but it does not seem to change anything.