Define LU factorization for JLArray

lib/JLArrays/src/JLArrays.jl

@@ -349,6 +349,31 @@ function Base.unsafe_convert(::Type{Ptr{T}}, x::JLArray{T}) where {T}
     error("Illegal conversion of a JLArray to a Ptr")
 end
 
+"""
+    lu(A::JLArray, args...; kwargs...)
+
+Compute an LU factorization using the host representation of `A`.
+"""
+function LinearAlgebra.lu(A::JLArray{T, 2}, args...; kwargs...) where {T}
+    return LinearAlgebra.lu(Array(A), args...; kwargs...)
+end
+
+function LinearAlgebra.ldiv!(
+        F::LinearAlgebra.LU{T, <:StridedMatrix{T}}, B::JLArray{T, N}
+    ) where {T <: LinearAlgebra.BlasFloat, N}
+    B_cpu = Array(B)
+    LinearAlgebra.ldiv!(F, B_cpu)
+    copyto!(B, B_cpu)
+    return B
+end
+
+function LinearAlgebra.ldiv!(Y::JLArray, F::LinearAlgebra.LU, B::JLArray)
+    B_cpu = Array(B)
+    LinearAlgebra.ldiv!(F, B_cpu)
+    copyto!(Y, B_cpu)
+    return Y
+end
+
 
 ## interop with Julia arrays
 

test/jlarrays_lu.jl

@@ -0,0 +1,16 @@
+using Test, JLArrays, LinearAlgebra
+
+@testset "JLArray LU" begin
+    A = jl([2.0 1.0; 1.0 3.0])
+    F = lu(A; check = false)
+
+    @test F isa LU{Float64, Matrix{Float64}, Vector{Int}}
+
+    b = jl([1.0, 2.0])
+    ldiv!(F, b)
+    @test Array(b) ≈ [0.2, 0.6]
+
+    y = similar(b)
+    ldiv!(y, F, jl([1.0, 2.0]))
+    @test Array(y) ≈ [0.2, 0.6]
+end

test/runtests.jl

@@ -1,6 +1,8 @@
 using ParallelTestRunner: runtests, parse_args
 import GPUArrays
 
+include("jlarrays_lu.jl")
+
 include("testsuite.jl")
 
 const init_worker_code = quote