Migrating from CUDSS.jl

The handle API keeps the names of the parameters and phases of CUDSS.jl; the functions are renamed after Julia conventions.

CUDSS.jl	SparseDirectSolver.jl
`CudssSolver(A, structure, view)`	`DirectSolver(A, structure, view)`
`cudss(phase, solver, x, b)`	`execute!(phase, solver, x, b)`
`cudss_set(solver, name, value)`	`setparam!(solver, name, value)`
`cudss_get(solver, name)`	`getparam(solver, name)` (or `getparam!` into a buffer)
`cudss_update(solver, A)`	`update!(solver, A)`
`CudssMatrix`, `CudssData`, `CudssConfig`	`CSR`, `MatrixDescriptor`, `Options`
`cholesky`, `ldlt`, `lu` on `CuSparseMatrixCSR`	the same functions, see LinearAlgebra interface

Structures ("G", "S", "H", "SPD", "HPD"), views ('L', 'U', 'F'), phase strings and parameter strings are identical, so a port is mostly a renaming:

# CUDSS.jl
solver = CudssSolver(A_gpu, "S", 'L')
cudss_set(solver, "pivot_threshold", 0.01)
cudss("analysis", solver, x_gpu, b_gpu)
cudss("factorization", solver, x_gpu, b_gpu)
inertia = cudss_get(solver, "inertia")
cudss("solve", solver, x_gpu, b_gpu)

# SparseDirectSolver.jl
solver = DirectSolver(A_gpu, "S", 'L')
setparam!(solver, "pivot_threshold", 0.01)
execute!("analysis", solver, x_gpu, b_gpu)
execute!("factorization", solver, x_gpu, b_gpu)
inertia = getparam(solver, "inertia")
execute!("solve", solver, x_gpu, b_gpu)

Differences

cuDSS parameters that are not implemented (hybrid memory and execution modes, multiple devices, non-uniform batches) raise a NotSupportedError instead of being ignored.
"factorization_alg" and "solve_alg" keep their spellings but select this package's own algorithms (see SparseDirectSolver.FactorizationAlg and SparseDirectSolver.SolveAlg).
Global pivoting (pivot_type 'C'/'R') is not supported: pivots are chosen inside each front, and failed pivots are perturbed rather than delayed.
Extensions beyond cuDSS: pivot_sign, pivot_stats, ir_mode = "fgmres", pivot_pairs, amalgamation, schedule (EXTRA_PARAMETERS).
The matrix and the vectors may live on any KernelAbstractions backend, not only CUDA.