Add CUSOLVERRF.jl integration for GPU-accelerated sparse LU factorization

[ChrisRackauckas-Claude]

Summary

This PR adds support for NVIDIA's cusolverRF sparse LU factorization library through a package extension, providing high-performance GPU-accelerated solving for sparse linear systems.

Motivation

CUSOLVERRF.jl provides access to NVIDIA's cusolverRF library, which offers significant performance improvements for sparse LU factorization on GPUs. This integration makes it accessible through LinearSolve.jl's unified interface.

Key Features

• New CUSOLVERRFFactorization algorithm with configurable options:
  • symbolic: Choose between :RF (default) or :KLU for symbolic factorization
  • reuse_symbolic: Reuse symbolic factorization for matrices with same sparsity pattern
• Automatic CPU-to-GPU conversion for convenience
• Support for multiple right-hand sides
• Adjoint solve support
• Comprehensive test suite

Implementation Details

The implementation follows LinearSolve.jl's extension pattern:

• Extension module in ext/LinearSolveCUSOLVERRFExt.jl
• Core types and exports in src/factorization.jl and src/LinearSolve.jl
• Weak dependency configuration in Project.toml
• Tests in test/gpu/cusolverrf.jl

Usage Example

using LinearSolve, CUSOLVERRF, SparseArrays

# Create sparse system
A = sprand(1000, 1000, 0.01) + 5I
b = rand(1000)

# Solve with default options
prob = LinearProblem(A, b)
sol = solve(prob, CUSOLVERRFFactorization())

# Use KLU for symbolic factorization
sol = solve(prob, CUSOLVERRFFactorization(symbolic = :KLU))

Limitations

• Only supports Float64 element types with Int32 indices (CUSOLVERRF limitation)
• Requires CUDA-capable GPU

Testing

Tests have been added to the GPU test suite and can be run with appropriate hardware.

This is a rebased version of #651.

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