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Add check keyword argument to generic calls #33

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Apr 19, 2024
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Add check keyword argument to generic calls #33

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16 changes: 8 additions & 8 deletions src/generic.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@ The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.

* `solver`: an opaque structure [`CudssSolver`](@ref) that stores the factors of the LU decomposition.
"""
function LinearAlgebra.lu(A::CuSparseMatrixCSR{T,Cint}) where T <: BlasFloat
function LinearAlgebra.lu(A::CuSparseMatrixCSR{T,Cint}; check = false) where T <: BlasFloat
n = checksquare(A)
solver = CudssSolver(A, "G", 'F')
x = CudssMatrix(T, n)
Expand All @@ -28,7 +28,7 @@ end
Compute the LU factorization of a sparse matrix `A` on an NVIDIA GPU, reusing the symbolic factorization stored in `solver`.
The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
"""
function LinearAlgebra.lu!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}) where T <: BlasFloat
function LinearAlgebra.lu!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}; check = false) where T <: BlasFloat
n = checksquare(A)
cudss_set(solver, A)
x = CudssMatrix(T, n)
Expand All @@ -55,7 +55,7 @@ The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.

* `solver`: Opaque structure [`CudssSolver`](@ref) that stores the factors of the LDLᴴ decomposition.
"""
function LinearAlgebra.ldlt(A::CuSparseMatrixCSR{T,Cint}; view::Char='F') where T <: BlasFloat
function LinearAlgebra.ldlt(A::CuSparseMatrixCSR{T,Cint}; view::Char='F', check = false) where T <: BlasFloat
n = checksquare(A)
structure = T <: Real ? "S" : "H"
solver = CudssSolver(A, structure, view)
Expand All @@ -76,7 +76,7 @@ LinearAlgebra.ldlt(A::Hermitian{T,<:CuSparseMatrixCSR{T,Cint}}) where T <: BlasF
Compute the LDLᴴ factorization of a sparse matrix `A` on an NVIDIA GPU, reusing the symbolic factorization stored in `solver`.
The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
"""
function LinearAlgebra.ldlt!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}) where T <: BlasFloat
function LinearAlgebra.ldlt!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}; check = false) where T <: BlasFloat
n = checksquare(A)
cudss_set(solver, A)
x = CudssMatrix(T, n)
Expand All @@ -103,7 +103,7 @@ The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.

* `solver`: Opaque structure [`CudssSolver`](@ref) that stores the factors of the LLᴴ decomposition.
"""
function LinearAlgebra.cholesky(A::CuSparseMatrixCSR{T,Cint}; view::Char='F') where T <: BlasFloat
function LinearAlgebra.cholesky(A::CuSparseMatrixCSR{T,Cint}; view::Char='F', check = false) where T <: BlasFloat
n = checksquare(A)
structure = T <: Real ? "SPD" : "HPD"
solver = CudssSolver(A, structure, view)
Expand All @@ -114,16 +114,16 @@ function LinearAlgebra.cholesky(A::CuSparseMatrixCSR{T,Cint}; view::Char='F') wh
return solver
end

LinearAlgebra.cholesky(A::Symmetric{T,<:CuSparseMatrixCSR{T,Cint}}) where T <: BlasReal = LinearAlgebra.cholesky(A.data, view=A.uplo)
LinearAlgebra.cholesky(A::Hermitian{T,<:CuSparseMatrixCSR{T,Cint}}) where T <: BlasFloat = LinearAlgebra.cholesky(A.data, view=A.uplo)
LinearAlgebra.cholesky(A::Symmetric{T,<:CuSparseMatrixCSR{T,Cint}}; check = false) where T <: BlasReal = LinearAlgebra.cholesky(A.data, view=A.uplo)
LinearAlgebra.cholesky(A::Hermitian{T,<:CuSparseMatrixCSR{T,Cint}}; check = false) where T <: BlasFloat = LinearAlgebra.cholesky(A.data, view=A.uplo)

"""
solver = cholesky!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint})

Compute the LLᴴ factorization of a sparse matrix `A` on an NVIDIA GPU, reusing the symbolic factorization stored in `solver`.
The type `T` can be `Float32`, `Float64`, `ComplexF32` or `ComplexF64`.
"""
function LinearAlgebra.cholesky!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}) where T <: BlasFloat
function LinearAlgebra.cholesky!(solver::CudssSolver{T}, A::CuSparseMatrixCSR{T,Cint}; check = false) where T <: BlasFloat
n = checksquare(A)
cudss_set(solver, A)
x = CudssMatrix(T, n)
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