Add OpenBLASLUFactorization implementation

Project.toml

@@ -17,6 +17,7 @@ Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MKL_jll = "856f044c-d86e-5d09-b602-aeab76dc8ba7"
 Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
+OpenBLAS_jll = "4536629a-c528-5b80-bd46-f80d51c5b363"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
 Preferences = "21216c6a-2e73-6563-6e65-726566657250"
 RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
@@ -112,6 +113,7 @@ MPI = "0.20"
 Markdown = "1.10"
 Metal = "1.4"
 MultiFloats = "2.3"
+OpenBLAS_jll = "0.3"
 Pardiso = "1"
 Pkg = "1.10"
 PrecompileTools = "1.2"

docs/src/basics/algorithm_selection.md

@@ -71,9 +71,10 @@ sol = solve(LinearProblem(A_small, rand(50)), SimpleLUFactorization())
 A_medium = rand(200, 200)
 sol = solve(LinearProblem(A_medium, rand(200)), RFLUFactorization())
 
-# Large matrices (> 500×500): MKLLUFactorization or AppleAccelerate
+# Large matrices (> 500×500): MKLLUFactorization, OpenBLASLUFactorization, or AppleAccelerate
 A_large = rand(1000, 1000) 
 sol = solve(LinearProblem(A_large, rand(1000)), MKLLUFactorization())
+# Alternative: OpenBLASLUFactorization() for direct OpenBLAS calls
 ```
 
 ### Sparse Matrices
@@ -141,7 +142,7 @@ Is A symmetric positive definite? → CholeskyFactorization
 Is A symmetric indefinite? → BunchKaufmanFactorization
 Is A sparse? → UMFPACKFactorization or KLUFactorization
 Is A small dense? → RFLUFactorization or SimpleLUFactorization
-Is A large dense? → MKLLUFactorization or AppleAccelerateLUFactorization
+Is A large dense? → MKLLUFactorization, OpenBLASLUFactorization, or AppleAccelerateLUFactorization
 Is A GPU array? → QRFactorization or LUFactorization
 Is A an operator/function? → KrylovJL_GMRES
 Is the system overdetermined? → QRFactorization or KrylovJL_LSMR

docs/src/solvers/solvers.md

@@ -17,9 +17,11 @@ the best choices, with SVD being the slowest but most precise.
 For efficiency, `RFLUFactorization` is the fastest for dense LU-factorizations until around
 150x150 matrices, though this can be dependent on the exact details of the hardware. After this
 point, `MKLLUFactorization` is usually faster on most hardware. Note that on Mac computers
-that `AppleAccelerateLUFactorization` is generally always the fastest. `LUFactorization` will
-use your base system BLAS which can be fast or slow depending on the hardware configuration.
-`SimpleLUFactorization` will be fast only on very small matrices but can cut down on compile times.
+that `AppleAccelerateLUFactorization` is generally always the fastest. `OpenBLASLUFactorization` 
+provides direct OpenBLAS calls without going through libblastrampoline and can be faster than 
+`LUFactorization` in some configurations. `LUFactorization` will use your base system BLAS which 
+can be fast or slow depending on the hardware configuration. `SimpleLUFactorization` will be fast 
+only on very small matrices but can cut down on compile times.
 
 For very large dense factorizations, offloading to the GPU can be preferred. Metal.jl can be used
 on Mac hardware to offload, and has a cutoff point of being faster at around size 20,000 x 20,000
@@ -226,6 +228,12 @@ MKLLUFactorization
 MKL32MixedLUFactorization
 ```
 
+### OpenBLAS
+
+```@docs
+OpenBLASLUFactorization
+```
+
 ### AppleAccelerate.jl
 
 !!! note

src/LinearSolve.jl

@@ -59,6 +59,14 @@ else
     const usemkl = false
 end
 
+# OpenBLAS_jll is a standard library, but allow users to disable it via preferences
+if Preferences.@load_preference("LoadOpenBLAS_JLL", true)
+    using OpenBLAS_jll: OpenBLAS_jll
+    const useopenblas = OpenBLAS_jll.is_available()
+else
+    const useopenblas = false
+end
+
 
 @reexport using SciMLBase
 
@@ -345,6 +353,7 @@ include("extension_algs.jl")
 include("factorization.jl")
 include("appleaccelerate.jl")
 include("mkl.jl")
+include("openblas.jl")
 include("simplelu.jl")
 include("simplegmres.jl")
 include("iterative_wrappers.jl")
@@ -462,6 +471,7 @@ export MKLPardisoFactorize, MKLPardisoIterate
 export PanuaPardisoFactorize, PanuaPardisoIterate
 export PardisoJL
 export MKLLUFactorization
+export OpenBLASLUFactorization
 export MKL32MixedLUFactorization
 export AppleAccelerateLUFactorization
 export AppleAccelerate32MixedLUFactorization

src/openblas.jl

@@ -0,0 +1,271 @@
+"""
+```julia
+OpenBLASLUFactorization()
+```
+
+A direct wrapper over OpenBLAS's LU factorization (`getrf!` and `getrs!`).
+This solver makes direct calls to OpenBLAS_jll without going through Julia's
+libblastrampoline, which can provide performance benefits in certain configurations.
+
+## Performance Characteristics
+
+  - Pre-allocates workspace to avoid allocations during solving
+  - Makes direct `ccall`s to OpenBLAS routines
+  - Can be faster than `LUFactorization` when OpenBLAS is well-optimized for the hardware
+  - Supports `Float32`, `Float64`, `ComplexF32`, and `ComplexF64` element types
+
+## When to Use
+
+  - When you want to ensure OpenBLAS is used regardless of the system BLAS configuration
+  - When benchmarking shows better performance than `LUFactorization` on your specific hardware
+  - When you need consistent behavior across different systems (always uses OpenBLAS)
+
+## Example
+
+```julia
+using LinearSolve, LinearAlgebra
+
+A = rand(100, 100)
+b = rand(100)
+prob = LinearProblem(A, b)
+sol = solve(prob, OpenBLASLUFactorization())
+```
+"""
+struct OpenBLASLUFactorization <: AbstractFactorization end
+
+# OpenBLAS methods - OpenBLAS_jll is always available as a standard library
+
+function openblas_getrf!(A::AbstractMatrix{<:ComplexF64};
+        ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2))),
+        info = Ref{BlasInt}(),
+        check = false)
+    require_one_based_indexing(A)
+    check && chkfinite(A)
+    chkstride1(A)
+    m, n = size(A)
+    lda = max(1, stride(A, 2))
+    if isempty(ipiv)
+        ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2)))
+    end
+    ccall((@blasfunc(zgetrf_), OpenBLAS_jll.libopenblas), Cvoid,
+        (Ref{BlasInt}, Ref{BlasInt}, Ptr{ComplexF64},
+            Ref{BlasInt}, Ptr{BlasInt}, Ptr{BlasInt}),
+        m, n, A, lda, ipiv, info)
+    chkargsok(info[])
+    A, ipiv, info[], info #Error code is stored in LU factorization type
+end
+
+function openblas_getrf!(A::AbstractMatrix{<:ComplexF32};
+        ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2))),
+        info = Ref{BlasInt}(),
+        check = false)
+    require_one_based_indexing(A)
+    check && chkfinite(A)
+    chkstride1(A)
+    m, n = size(A)
+    lda = max(1, stride(A, 2))
+    if isempty(ipiv)
+        ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2)))
+    end
+    ccall((@blasfunc(cgetrf_), OpenBLAS_jll.libopenblas), Cvoid,
+        (Ref{BlasInt}, Ref{BlasInt}, Ptr{ComplexF32},
+            Ref{BlasInt}, Ptr{BlasInt}, Ptr{BlasInt}),
+        m, n, A, lda, ipiv, info)
+    chkargsok(info[])
+    A, ipiv, info[], info #Error code is stored in LU factorization type
+end
+
+function openblas_getrf!(A::AbstractMatrix{<:Float64};
+        ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2))),
+        info = Ref{BlasInt}(),
+        check = false)
+    require_one_based_indexing(A)
+    check && chkfinite(A)
+    chkstride1(A)
+    m, n = size(A)
+    lda = max(1, stride(A, 2))
+    if isempty(ipiv)
+        ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2)))
+    end
+    ccall((@blasfunc(dgetrf_), OpenBLAS_jll.libopenblas), Cvoid,
+        (Ref{BlasInt}, Ref{BlasInt}, Ptr{Float64},
+            Ref{BlasInt}, Ptr{BlasInt}, Ptr{BlasInt}),
+        m, n, A, lda, ipiv, info)
+    chkargsok(info[])
+    A, ipiv, info[], info #Error code is stored in LU factorization type
+end
+
+function openblas_getrf!(A::AbstractMatrix{<:Float32};
+        ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2))),
+        info = Ref{BlasInt}(),
+        check = false)
+    require_one_based_indexing(A)
+    check && chkfinite(A)
+    chkstride1(A)
+    m, n = size(A)
+    lda = max(1, stride(A, 2))
+    if isempty(ipiv)
+        ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2)))
+    end
+    ccall((@blasfunc(sgetrf_), OpenBLAS_jll.libopenblas), Cvoid,
+        (Ref{BlasInt}, Ref{BlasInt}, Ptr{Float32},
+            Ref{BlasInt}, Ptr{BlasInt}, Ptr{BlasInt}),
+        m, n, A, lda, ipiv, info)
+    chkargsok(info[])
+    A, ipiv, info[], info #Error code is stored in LU factorization type
+end
+
+function openblas_getrs!(trans::AbstractChar,
+        A::AbstractMatrix{<:ComplexF64},
+        ipiv::AbstractVector{BlasInt},
+        B::AbstractVecOrMat{<:ComplexF64};
+        info = Ref{BlasInt}())
+    require_one_based_indexing(A, ipiv, B)
+    LinearAlgebra.LAPACK.chktrans(trans)
+    chkstride1(A, B, ipiv)
+    n = LinearAlgebra.checksquare(A)
+    if n != size(B, 1)
+        throw(DimensionMismatch("B has leading dimension $(size(B,1)), but needs $n"))
+    end
+    if n != length(ipiv)
+        throw(DimensionMismatch("ipiv has length $(length(ipiv)), but needs to be $n"))
+    end
+    nrhs = size(B, 2)
+    ccall((@blasfunc(zgetrs_), OpenBLAS_jll.libopenblas), Cvoid,
+        (Ref{UInt8}, Ref{BlasInt}, Ref{BlasInt}, Ptr{ComplexF64}, Ref{BlasInt},
+            Ptr{BlasInt}, Ptr{ComplexF64}, Ref{BlasInt}, Ptr{BlasInt}, Clong),
+        trans, n, size(B, 2), A, max(1, stride(A, 2)), ipiv, B, max(1, stride(B, 2)), info,
+        1)
+    LinearAlgebra.LAPACK.chklapackerror(BlasInt(info[]))
+    B
+end
+
+function openblas_getrs!(trans::AbstractChar,
+        A::AbstractMatrix{<:ComplexF32},
+        ipiv::AbstractVector{BlasInt},
+        B::AbstractVecOrMat{<:ComplexF32};
+        info = Ref{BlasInt}())
+    require_one_based_indexing(A, ipiv, B)
+    LinearAlgebra.LAPACK.chktrans(trans)
+    chkstride1(A, B, ipiv)
+    n = LinearAlgebra.checksquare(A)
+    if n != size(B, 1)
+        throw(DimensionMismatch("B has leading dimension $(size(B,1)), but needs $n"))
+    end
+    if n != length(ipiv)
+        throw(DimensionMismatch("ipiv has length $(length(ipiv)), but needs to be $n"))
+    end
+    nrhs = size(B, 2)
+    ccall((@blasfunc(cgetrs_), OpenBLAS_jll.libopenblas), Cvoid,
+        (Ref{UInt8}, Ref{BlasInt}, Ref{BlasInt}, Ptr{ComplexF32}, Ref{BlasInt},
+            Ptr{BlasInt}, Ptr{ComplexF32}, Ref{BlasInt}, Ptr{BlasInt}, Clong),
+        trans, n, size(B, 2), A, max(1, stride(A, 2)), ipiv, B, max(1, stride(B, 2)), info,
+        1)
+    LinearAlgebra.LAPACK.chklapackerror(BlasInt(info[]))
+    B
+end
+
+function openblas_getrs!(trans::AbstractChar,
+        A::AbstractMatrix{<:Float64},
+        ipiv::AbstractVector{BlasInt},
+        B::AbstractVecOrMat{<:Float64};
+        info = Ref{BlasInt}())
+    require_one_based_indexing(A, ipiv, B)
+    LinearAlgebra.LAPACK.chktrans(trans)
+    chkstride1(A, B, ipiv)
+    n = LinearAlgebra.checksquare(A)
+    if n != size(B, 1)
+        throw(DimensionMismatch("B has leading dimension $(size(B,1)), but needs $n"))
+    end
+    if n != length(ipiv)
+        throw(DimensionMismatch("ipiv has length $(length(ipiv)), but needs to be $n"))
+    end
+    nrhs = size(B, 2)
+    ccall((@blasfunc(dgetrs_), OpenBLAS_jll.libopenblas), Cvoid,
+        (Ref{UInt8}, Ref{BlasInt}, Ref{BlasInt}, Ptr{Float64}, Ref{BlasInt},
+            Ptr{BlasInt}, Ptr{Float64}, Ref{BlasInt}, Ptr{BlasInt}, Clong),
+        trans, n, size(B, 2), A, max(1, stride(A, 2)), ipiv, B, max(1, stride(B, 2)), info,
+        1)
+    LinearAlgebra.LAPACK.chklapackerror(BlasInt(info[]))
+    B
+end
+
+function openblas_getrs!(trans::AbstractChar,
+        A::AbstractMatrix{<:Float32},
+        ipiv::AbstractVector{BlasInt},
+        B::AbstractVecOrMat{<:Float32};
+        info = Ref{BlasInt}())
+    require_one_based_indexing(A, ipiv, B)
+    LinearAlgebra.LAPACK.chktrans(trans)
+    chkstride1(A, B, ipiv)
+    n = LinearAlgebra.checksquare(A)
+    if n != size(B, 1)
+        throw(DimensionMismatch("B has leading dimension $(size(B,1)), but needs $n"))
+    end
+    if n != length(ipiv)
+        throw(DimensionMismatch("ipiv has length $(length(ipiv)), but needs to be $n"))
+    end
+    nrhs = size(B, 2)
+    ccall((@blasfunc(sgetrs_), OpenBLAS_jll.libopenblas), Cvoid,
+        (Ref{UInt8}, Ref{BlasInt}, Ref{BlasInt}, Ptr{Float32}, Ref{BlasInt},
+            Ptr{BlasInt}, Ptr{Float32}, Ref{BlasInt}, Ptr{BlasInt}, Clong),
+        trans, n, size(B, 2), A, max(1, stride(A, 2)), ipiv, B, max(1, stride(B, 2)), info,
+        1)
+    LinearAlgebra.LAPACK.chklapackerror(BlasInt(info[]))
+    B
+end
+
+default_alias_A(::OpenBLASLUFactorization, ::Any, ::Any) = false
+default_alias_b(::OpenBLASLUFactorization, ::Any, ::Any) = false
+
+const PREALLOCATED_OPENBLAS_LU = begin
+    A = rand(0, 0)
+    luinst = ArrayInterface.lu_instance(A), Ref{BlasInt}()
+end
+
+function LinearSolve.init_cacheval(alg::OpenBLASLUFactorization, A, b, u, Pl, Pr,
+        maxiters::Int, abstol, reltol, verbose::LinearVerbosity,
+        assumptions::OperatorAssumptions)
+    PREALLOCATED_OPENBLAS_LU
+end
+
+function LinearSolve.init_cacheval(alg::OpenBLASLUFactorization,
+        A::AbstractMatrix{<:Union{Float32, ComplexF32, ComplexF64}}, b, u, Pl, Pr,
+        maxiters::Int, abstol, reltol, verbose::LinearVerbosity,
+        assumptions::OperatorAssumptions)
+    A = rand(eltype(A), 0, 0)
+    ArrayInterface.lu_instance(A), Ref{BlasInt}()
+end
+
+function SciMLBase.solve!(cache::LinearCache, alg::OpenBLASLUFactorization;
+        kwargs...)
+    A = cache.A
+    A = convert(AbstractMatrix, A)
+    if cache.isfresh
+        cacheval = @get_cacheval(cache, :OpenBLASLUFactorization)
+        res = openblas_getrf!(A; ipiv = cacheval[1].ipiv, info = cacheval[2])
+        fact = LU(res[1:3]...), res[4]
+        cache.cacheval = fact
+
+        if !LinearAlgebra.issuccess(fact[1])
+            return SciMLBase.build_linear_solution(
+                alg, cache.u, nothing, cache; retcode = ReturnCode.Failure)
+        end
+        cache.isfresh = false
+    end
+
+    A, info = @get_cacheval(cache, :OpenBLASLUFactorization)
+    require_one_based_indexing(cache.u, cache.b)
+    m, n = size(A, 1), size(A, 2)
+    if m > n
+        Bc = copy(cache.b)
+        openblas_getrs!('N', A.factors, A.ipiv, Bc; info)
+        copyto!(cache.u, 1, Bc, 1, n)
+    else
+        copyto!(cache.u, cache.b)
+        openblas_getrs!('N', A.factors, A.ipiv, cache.u; info)
+    end
+
+    SciMLBase.build_linear_solution(
+        alg, cache.u, nothing, cache; retcode = ReturnCode.Success)
+end

test/basictests.jl

@@ -287,6 +287,11 @@ end
         push!(test_algs, MKLLUFactorization())
     end
 
+    # Test OpenBLAS if available
+    if LinearSolve.useopenblas
+        push!(test_algs, OpenBLASLUFactorization())
+    end
+
     # Test BLIS if extension is available
     if Base.get_extension(LinearSolve, :LinearSolveBLISExt) !== nothing
         push!(test_algs, BLISLUFactorization())

test/preferences.jl

@@ -190,6 +190,14 @@ using Preferences
             println("✅ MKLLUFactorization confirmed working")
         end
 
+        # Test OpenBLAS if available
+        if LinearSolve.useopenblas
+            sol_openblas = solve(prob, OpenBLASLUFactorization())
+            @test sol_openblas.retcode == ReturnCode.Success
+            @test norm(A * sol_openblas.u - b) < 1e-8
+            println("✅ OpenBLASLUFactorization confirmed working")
+        end
+
         # Test Apple Accelerate if available
         if LinearSolve.appleaccelerate_isavailable()
             sol_apple = solve(prob, AppleAccelerateLUFactorization())