Add OpenBLAS_jll dependency to LinearSolveAutotune

Fixes the missing OpenBLAS_jll dependency that was preventing OpenBLASLUFactorization
from being properly detected and included in autotune benchmarks.

Changes:
- Added OpenBLAS_jll to LinearSolveAutotune/Project.toml dependencies
- Added OpenBLAS_jll import to LinearSolveAutotune.jl
- Set compat entry for OpenBLAS_jll = "0.3"

This resolves the undefined variable error when checking OpenBLAS_jll.is_available()
in get_available_algorithms(), ensuring OpenBLAS direct wrapper is properly included
in autotune benchmarks alongside other manual BLAS wrappers.

Fixes: #764 (comment)

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

Author: ChrisRackauckas

lib/LinearSolveAutotune/Project.toml

@@ -20,6 +20,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 MKL_jll = "856f044c-d86e-5d09-b602-aeab76dc8ba7"
 Metal = "dde4c033-4e86-420c-a63e-0dd931031962"
+OpenBLAS_jll = "4536629a-c528-5b80-bd46-f80d51c5b363"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Preferences = "21216c6a-2e73-6563-6e65-726566657250"
@@ -47,6 +48,7 @@ LinearAlgebra = "1"
 LinearSolve = "3"
 MKL_jll = "2025.2.0"
 Metal = "1"
+OpenBLAS_jll = "0.3"
 Pkg = "1"
 Plots = "1"
 Preferences = "1.5"

lib/LinearSolveAutotune/src/LinearSolveAutotune.jl

@@ -3,6 +3,7 @@ module LinearSolveAutotune
 # Ensure MKL is available for benchmarking by setting the preference before loading LinearSolve
 using Preferences
 using MKL_jll
+using OpenBLAS_jll
 
 # Set MKL preference to true for benchmarking if MKL is available
 # We need to use UUID instead of the module since LinearSolve isn't loaded yet