Pardiso.jl
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JuliaSparse
Input inconsistent with MKL Pardiso and example in README.md
I just tried to run the example in the README.md with MKLPardisoSolver.
I use MKLSparse v1.1.0 and Pardiso v0.5.1 in Julia 1.5.1. MKL is installed automatically by MKLSparse.
Does somebody have any idea what could be the issue? The input matrix sizes seem to be correct.
Thank you for this nice package!
Example script:
using MKLSparse
using Pardiso
using SparseArrays
using LinearAlgebra
ps = MKLPardisoSolver()
A = sparse(rand(10, 10))
B = rand(10, 2)
X = zeros(10, 2)
solve!(ps, X, A, B)
#same error with
#B = rand(10)
#X = solve(ps, A, B)
Error message:
julia> solve!(ps, X, A, B)
ERROR: Input inconsistent.
Stacktrace:
[1] check_error at /home/abarth/.julia/packages/Pardiso/yZsYO/src/mkl_pardiso.jl:76 [inlined]
[2] ccall_pardiso(::MKLPardisoSolver, ::Int64, ::Array{Float64,1}, ::Array{Int64,1}, ::Array{Int64,1}, ::Int64, ::Array{Float64,2}, ::Array{Float64,2}) at /home/aba\
rth/.julia/packages/Pardiso/yZsYO/src/mkl_pardiso.jl:71
[3] pardiso(::MKLPardisoSolver, ::Array{Float64,2}, ::SparseMatrixCSC{Float64,Int64}, ::Array{Float64,2}) at /home/abarth/.julia/packages/Pardiso/yZsYO/src/Pardiso.\
jl:337
[4] solve!(::MKLPardisoSolver, ::Array{Float64,2}, ::SparseMatrixCSC{Float64,Int64}, ::Array{Float64,2}, ::Symbol) at /home/abarth/.julia/packages/Pardiso/yZsYO/src\
/Pardiso.jl:273
[5] solve!(::MKLPardisoSolver, ::Array{Float64,2}, ::SparseMatrixCSC{Float64,Int64}, ::Array{Float64,2}) at /home/abarth/.julia/packages/Pardiso/yZsYO/src/Pardiso.j\
l:238
[6] top-level scope at REPL[71]:1
Environment:
julia> versioninfo()
Julia Version 1.5.1
Commit 697e782ab8 (2020-08-25 20:08 UTC)
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Core(TM) i7-7700 CPU @ 3.60GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-9.0.1 (ORCJIT, skylake)
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 (unless https://github.com/JuliaSparse/Pardiso.jl/blob/81c8391e86cebaf71e936363664d878b22b1fd9a/src/Pardiso.jl#L29-L30 is true, but this is not the case here).
Perfect! Indeed without loading MKLSparse the code works! Thank you very much!
Well, not perfect :P. We should figure out a way to make the two packages collaborate.
For your information, on my system LinearAlgebra.BLAS.vendor() is :openblas64 and LinearAlgebra.BlasInt is Int64.
I am wondering if we can use the same logic than in MKLSparse to determine MklInt.
const MklInt = (Base.USE_BLAS64 ? Int64 : Int32)
# or simply?
const MklInt = LinearAlgebra.BlasInt
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 matrices.
It's just kinda hard how to do this properly because the MKL Interface state is global and packages use it like it is their own local setting to play with.
Any work around for using both Pardiso and MKLSparse? Using SparseMatrices with Ti=Int32 only?
