Kristoffer Carlsson

Can you try run: ``` julia> Pardiso.load_lib_fortran("libgomp", 8) Ptr{Nothing} @0x0000000004370080 julia> Pardiso.load_lib_fortran("libgomp", 9) Ptr{Nothing} @0x0000000004370080 julia> Pardiso.load_lib_fortran("libgomp", 10) Ptr{Nothing} @0x0000000004370080 ```

Where is your gcc installed? Pardiso looks for it here: https://github.com/JuliaSparse/Pardiso.jl/blob/e0e08fcef1e4d8ff848b148cae31371360363037/src/Pardiso.jl#L76-L84. It is very possible this detection could be improved..

Also, just pointing out. Pardiso.jl should work fine if you don't give `MKL_ROOT` by using the bundled MKL. Is there a reason you want to provide your own MKL?

> When simply adding and using Pardiso without trying to provide it an MKLROOT I was getting: Maybe that was fixed in the most recent release (that only supports 1.6)....

I am not sure that will work since we are trying to open the library via an absolute path. Did you try it?

It seems loading MKLSparse breaks things. My guess is that https://github.com/JuliaSparse/MKLSparse.jl/blob/6060cd08a2586b275c95aac650c379ff48130ef7/src/MKLSparse.jl#L6 sets MKL in 64 bit mode but Pardiso.jl assumes that integers should be passed to MKL as 32 bits...

Well, not perfect :P. We should figure out a way to make the two packages collaborate.

The problem is that SparseMatrices in Julia has `Int64` indices in almost all cases. So the code in MKLSparse doesn't really make sense (`LinearAlgebra.BlasInt`) is pretty much irrelevant to sparse...

Not a problem with the MKL solver: ``` julia> ps = MKLPardisoSolver() julia> @show norm(A*xx-yy) # what I get: 0.6085402893836485 norm(A * xx - yy) = 1.9314667586919627e-14 1.9314667586919627e-14 ``` And...

It should be on the latest backport PR in Julia.