cuda arrayenhancementgood first issue
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Description
If you are looking like me to compute logdet on Symmetric matrices you need to go call some "low-level" functions in CUDA.CUSOLVER
This is connected to #116
Here is an unsafe gist for logdet :
function LinearAlgebra.logdet(A::CUDA.CuMatrix)
d_A = copy(A) # Create a copy
_, info = CUDA.CUSOLVER.potrfBatched!('L', [d_A]) # Update d_A in-place to be passed to a Cholesky constructor
L = LinearAlgebra.Cholesky(d_A, 'L', first(info)).L # Create the corresponding lower-triangular matrix
return 2 * sum(log, diag(L)) # Compute logdet by summing the elements of the diagonal
end
I suppose this could be improved and be more faithful to the implementation in LinearAlgebra but that's further work
For inv one can do a similar thing :
function LinearAlgebra.inv(A::CUDA.CuMatrix)
d_A = copy(A)
_, info = CUDA.CUSOLVER.potrfBatched!('L', [d_A])
L = LinearAlgebra.Cholesky(d_A, 'L', first(info)).L
return (I / L) / L'
end
If you'd rather use lu instead replace the second and third line of each algorithm by
d_A, p_inv, info = CUDA.CUSOLVER.getrf!(d_A) # Update d_A in-place to be passed to be passed to a LU constructor
fact = LinearAlgebra.LU(d_A, Int64.(collect(p_inv), zero()) # Create a LU object
and change the last line accordingly