Fix autotune for me (#692)

Author: chriselrod

lib/LinearSolveAutotune/Project.toml

@@ -4,50 +4,52 @@ authors = ["SciML"]
 version = "1.0.0"
 
 [deps]
-LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
-BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 Base64 = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 CPUSummary = "2a0fbf3d-bb9c-48f3-b0a9-814d99fd7ab9"
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 GitHub = "bc5e4493-9b4d-5f90-b8aa-2b2bcaad7a26"
-gh_cli_jll = "5d31d589-30fb-542f-b82d-10325e863e38"
+LAPACK_jll = "51474c39-65e3-53ba-86ba-03b1b862ec14"
+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"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
-PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
 Preferences = "21216c6a-2e73-6563-6e65-726566657250"
-Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
-Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 RecursiveFactorization = "f2c3362d-daeb-58d1-803e-2bc74f2840b4"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 blis_jll = "6136c539-28a5-5bf0-87cc-b183200dce32"
-LAPACK_jll = "51474c39-65e3-53ba-86ba-03b1b862ec14"
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-Metal = "dde4c033-4e86-420c-a63e-0dd931031962"
+gh_cli_jll = "5d31d589-30fb-542f-b82d-10325e863e38"
 
 [compat]
-LinearSolve = "3"
-BenchmarkTools = "1"
 Base64 = "1"
+BenchmarkTools = "1"
 CPUSummary = "0.2"
+CUDA = "5"
 DataFrames = "1"
+Dates = "1"
 GitHub = "5"
-gh_cli_jll = "2"
+LAPACK_jll = "3"
+LinearAlgebra = "1"
+LinearSolve = "3"
+MKL_jll = "2025.2.0"
+Metal = "1"
 Plots = "1"
-PrettyTables = "2"
 Preferences = "1"
-Statistics = "1"
-Random = "1"
-LinearAlgebra = "1"
+PrettyTables = "2"
 Printf = "1"
-Dates = "1"
-Test = "1"
 ProgressMeter = "1"
+Random = "1"
 RecursiveFactorization = "0.2"
+Statistics = "1"
+Test = "1"
 blis_jll = "0.9.0"
-LAPACK_jll = "3"
-CUDA = "5"
-Metal = "1"
-julia = "1.10"
+gh_cli_jll = "2"
+julia = "1.10"

lib/LinearSolveAutotune/src/LinearSolveAutotune.jl

@@ -18,6 +18,7 @@ using CPUSummary
 using RecursiveFactorization  
 using blis_jll
 using LAPACK_jll
+using MKL_jll
 using CUDA
 using Metal
 

lib/LinearSolveAutotune/src/algorithms.jl

@@ -25,7 +25,7 @@ function get_available_algorithms(; skip_missing_algs::Bool = false)
     end
 
     # MKL if available
-    if mkl_jll.is_available()
+    if MKL_jll.is_available()
         push!(algs, MKLLUFactorization())
         push!(alg_names, "MKLLUFactorization")
     end