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ChrisRackauckas-ClaudeChrisRackauckasclaude
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Complete BLIS integration with reference LAPACK (#666)
* WIP: Wrap BLIS Test case: ```julia using LinearSolve, blis_jll A = rand(4, 4) b = rand(4) prob = LinearProblem(A, b) sol = solve(prob,LinearSolve.BLISLUFactorization()) sol.u ``` throws: ```julia julia> sol = solve(prob,LinearSolve.BLISLUFactorization()) ERROR: TypeError: in ccall: first argument not a pointer or valid constant expression, expected Ptr, got a value of type Tuple{Symbol, Ptr{Nothing}} Stacktrace: [1] getrf!(A::Matrix{Float64}; ipiv::Vector{Int64}, info::Base.RefValue{Int64}, check::Bool) @ LinearSolveBLISExt ~/.julia/dev/LinearSolve/ext/LinearSolveBLISExt.jl:67 [2] getrf! @ LinearSolveBLISExt ~/.julia/dev/LinearSolve/ext/LinearSolveBLISExt.jl:55 [inlined] [3] #solve!#9 @ LinearSolveBLISExt ~/.julia/dev/LinearSolve/ext/LinearSolveBLISExt.jl:222 [inlined] [4] solve! @ LinearSolveBLISExt ~/.julia/dev/LinearSolve/ext/LinearSolveBLISExt.jl:216 [inlined] [5] #solve!#6 @ LinearSolve ~/.julia/dev/LinearSolve/src/common.jl:209 [inlined] [6] solve! @ LinearSolve ~/.julia/dev/LinearSolve/src/common.jl:208 [inlined] [7] #solve#5 @ LinearSolve ~/.julia/dev/LinearSolve/src/common.jl:205 [inlined] [8] solve(::LinearProblem{…}, ::LinearSolve.BLISLUFactorization) @ LinearSolve ~/.julia/dev/LinearSolve/src/common.jl:202 [9] top-level scope @ REPL[8]:1 Some type information was truncated. Use `show(err)` to see complete types. ``` * fix path * Fix BLIS integration to use BLIS for BLAS + reference LAPACK for LAPACK - Updated LinearSolveBLISExt to use both blis_jll and LAPACK_jll - Changed LAPACK function calls (getrf, getrs) to use liblapack instead of libblis - Added LAPACK_jll to weak dependencies and extension configuration - Created comprehensive test suite for BLIS + reference LAPACK functionality - Tests cover Float32/64, ComplexF32/64, accuracy, caching, and comparison with default solvers - All tests pass, confirming correct BLIS + reference LAPACK integration This fixes the issue where BLIS was incorrectly used for both BLAS and LAPACK operations. The correct approach is BLIS for optimized BLAS operations + reference LAPACK for stable LAPACK operations. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> * Simplify BLIS integration to use existing test framework - Removed dedicated BLIS test files and test group - Added BLISLUFactorization to existing test loops in basictests.jl - Added conditional loading of BLIS dependencies in tests - BLIS tests now run as part of standard "Concrete Factorizations" test suite - Tests are automatically skipped if BLIS dependencies are not available This follows the established pattern used by other factorization methods like MKL, making the integration cleaner and more maintainable. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <[email protected]> --------- Co-authored-by: Christopher Rackauckas <[email protected]> Co-authored-by: Claude <[email protected]>
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‎Project.toml

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Original file line numberDiff line numberDiff line change
@@ -30,6 +30,7 @@ UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
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[weakdeps]
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BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
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BlockDiagonals = "0a1fb500-61f7-11e9-3c65-f5ef3456f9f0"
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blis_jll = "6136c539-28a5-5bf0-87cc-b183200dce32"
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CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
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CUDSS = "45b445bb-4962-46a0-9369-b4df9d0f772e"
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EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
@@ -40,13 +41,15 @@ HYPRE = "b5ffcf37-a2bd-41ab-a3da-4bd9bc8ad771"
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IterativeSolvers = "42fd0dbc-a981-5370-80f2-aaf504508153"
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KernelAbstractions = "63c18a36-062a-441e-b654-da1e3ab1ce7c"
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KrylovKit = "0b1a1467-8014-51b9-945f-bf0ae24f4b77"
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LAPACK_jll = "51474c39-65e3-53ba-86ba-03b1b862ec14"
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Metal = "dde4c033-4e86-420c-a63e-0dd931031962"
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Pardiso = "46dd5b70-b6fb-5a00-ae2d-e8fea33afaf2"
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RecursiveFactorization = "f2c3362d-daeb-58d1-803e-2bc74f2840b4"
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SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
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Sparspak = "e56a9233-b9d6-4f03-8d0f-1825330902ac"
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[extensions]
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LinearSolveBLISExt = ["blis_jll", "LAPACK_jll"]
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LinearSolveBandedMatricesExt = "BandedMatrices"
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LinearSolveBlockDiagonalsExt = "BlockDiagonals"
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LinearSolveCUDAExt = "CUDA"
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ArrayInterface = "7.7"
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BandedMatrices = "1.5"
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BlockDiagonals = "0.1.42, 0.2"
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blis_jll = "0.9.0"
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CUDA = "5"
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CUDSS = "0.1, 0.2, 0.3, 0.4"
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ChainRulesCore = "1.22"
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Krylov = "0.10"
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KrylovKit = "0.8, 0.9, 0.10"
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KrylovPreconditioners = "0.3"
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LAPACK_jll = "3"
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LazyArrays = "1.8, 2"
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Libdl = "1.10"
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LinearAlgebra = "1.10"

‎ext/LinearSolveBLISExt.jl

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module LinearSolveBLISExt
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using Libdl
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using blis_jll
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using LAPACK_jll
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using LinearAlgebra
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using LinearSolve
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using LinearAlgebra: BlasInt, LU
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using LinearAlgebra.LAPACK: require_one_based_indexing, chkfinite, chkstride1,
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@blasfunc, chkargsok
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using LinearSolve: ArrayInterface, BLISLUFactorization, @get_cacheval, LinearCache, SciMLBase
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const global libblis = blis_jll.blis
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const global liblapack = LAPACK_jll.liblapack
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function getrf!(A::AbstractMatrix{<:ComplexF64};
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ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2))),
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info = Ref{BlasInt}(),
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check = false)
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require_one_based_indexing(A)
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check && chkfinite(A)
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chkstride1(A)
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m, n = size(A)
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lda = max(1, stride(A, 2))
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if isempty(ipiv)
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ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2)))
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end
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ccall((@blasfunc(zgetrf_), liblapack), Cvoid,
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(Ref{BlasInt}, Ref{BlasInt}, Ptr{ComplexF64},
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Ref{BlasInt}, Ptr{BlasInt}, Ptr{BlasInt}),
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m, n, A, lda, ipiv, info)
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chkargsok(info[])
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A, ipiv, info[], info #Error code is stored in LU factorization type
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end
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function getrf!(A::AbstractMatrix{<:ComplexF32};
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ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2))),
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info = Ref{BlasInt}(),
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check = false)
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require_one_based_indexing(A)
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check && chkfinite(A)
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chkstride1(A)
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m, n = size(A)
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lda = max(1, stride(A, 2))
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if isempty(ipiv)
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ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2)))
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end
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ccall((@blasfunc(cgetrf_), liblapack), Cvoid,
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(Ref{BlasInt}, Ref{BlasInt}, Ptr{ComplexF32},
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Ref{BlasInt}, Ptr{BlasInt}, Ptr{BlasInt}),
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m, n, A, lda, ipiv, info)
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chkargsok(info[])
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A, ipiv, info[], info #Error code is stored in LU factorization type
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end
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function getrf!(A::AbstractMatrix{<:Float64};
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ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2))),
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info = Ref{BlasInt}(),
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check = false)
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require_one_based_indexing(A)
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check && chkfinite(A)
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chkstride1(A)
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m, n = size(A)
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lda = max(1, stride(A, 2))
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if isempty(ipiv)
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ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2)))
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end
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ccall((@blasfunc(dgetrf_), liblapack), Cvoid,
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(Ref{BlasInt}, Ref{BlasInt}, Ptr{Float64},
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Ref{BlasInt}, Ptr{BlasInt}, Ptr{BlasInt}),
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m, n, A, lda, ipiv, info)
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chkargsok(info[])
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A, ipiv, info[], info #Error code is stored in LU factorization type
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end
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function getrf!(A::AbstractMatrix{<:Float32};
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ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2))),
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info = Ref{BlasInt}(),
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check = false)
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require_one_based_indexing(A)
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check && chkfinite(A)
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chkstride1(A)
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m, n = size(A)
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lda = max(1, stride(A, 2))
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if isempty(ipiv)
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ipiv = similar(A, BlasInt, min(size(A, 1), size(A, 2)))
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end
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ccall((@blasfunc(sgetrf_), liblapack), Cvoid,
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(Ref{BlasInt}, Ref{BlasInt}, Ptr{Float32},
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Ref{BlasInt}, Ptr{BlasInt}, Ptr{BlasInt}),
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m, n, A, lda, ipiv, info)
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chkargsok(info[])
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A, ipiv, info[], info #Error code is stored in LU factorization type
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end
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function getrs!(trans::AbstractChar,
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A::AbstractMatrix{<:ComplexF64},
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ipiv::AbstractVector{BlasInt},
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B::AbstractVecOrMat{<:ComplexF64};
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info = Ref{BlasInt}())
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require_one_based_indexing(A, ipiv, B)
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LinearAlgebra.LAPACK.chktrans(trans)
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chkstride1(A, B, ipiv)
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n = LinearAlgebra.checksquare(A)
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if n != size(B, 1)
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throw(DimensionMismatch("B has leading dimension $(size(B,1)), but needs $n"))
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end
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if n != length(ipiv)
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throw(DimensionMismatch("ipiv has length $(length(ipiv)), but needs to be $n"))
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end
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nrhs = size(B, 2)
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ccall(("zgetrs_", liblapack), Cvoid,
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(Ref{UInt8}, Ref{BlasInt}, Ref{BlasInt}, Ptr{ComplexF64}, Ref{BlasInt},
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Ptr{BlasInt}, Ptr{ComplexF64}, Ref{BlasInt}, Ptr{BlasInt}, Clong),
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trans, n, size(B, 2), A, max(1, stride(A, 2)), ipiv, B, max(1, stride(B, 2)), info,
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1)
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LinearAlgebra.LAPACK.chklapackerror(BlasInt(info[]))
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B
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end
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function getrs!(trans::AbstractChar,
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A::AbstractMatrix{<:ComplexF32},
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ipiv::AbstractVector{BlasInt},
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B::AbstractVecOrMat{<:ComplexF32};
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info = Ref{BlasInt}())
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require_one_based_indexing(A, ipiv, B)
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LinearAlgebra.LAPACK.chktrans(trans)
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chkstride1(A, B, ipiv)
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n = LinearAlgebra.checksquare(A)
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if n != size(B, 1)
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throw(DimensionMismatch("B has leading dimension $(size(B,1)), but needs $n"))
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end
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if n != length(ipiv)
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throw(DimensionMismatch("ipiv has length $(length(ipiv)), but needs to be $n"))
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end
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nrhs = size(B, 2)
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ccall(("cgetrs_", liblapack), Cvoid,
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(Ref{UInt8}, Ref{BlasInt}, Ref{BlasInt}, Ptr{ComplexF32}, Ref{BlasInt},
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Ptr{BlasInt}, Ptr{ComplexF32}, Ref{BlasInt}, Ptr{BlasInt}, Clong),
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trans, n, size(B, 2), A, max(1, stride(A, 2)), ipiv, B, max(1, stride(B, 2)), info,
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1)
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LinearAlgebra.LAPACK.chklapackerror(BlasInt(info[]))
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B
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end
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function getrs!(trans::AbstractChar,
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A::AbstractMatrix{<:Float64},
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ipiv::AbstractVector{BlasInt},
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B::AbstractVecOrMat{<:Float64};
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info = Ref{BlasInt}())
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require_one_based_indexing(A, ipiv, B)
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LinearAlgebra.LAPACK.chktrans(trans)
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chkstride1(A, B, ipiv)
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n = LinearAlgebra.checksquare(A)
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if n != size(B, 1)
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throw(DimensionMismatch("B has leading dimension $(size(B,1)), but needs $n"))
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end
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if n != length(ipiv)
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throw(DimensionMismatch("ipiv has length $(length(ipiv)), but needs to be $n"))
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end
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nrhs = size(B, 2)
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ccall(("dgetrs_", liblapack), Cvoid,
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(Ref{UInt8}, Ref{BlasInt}, Ref{BlasInt}, Ptr{Float64}, Ref{BlasInt},
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Ptr{BlasInt}, Ptr{Float64}, Ref{BlasInt}, Ptr{BlasInt}, Clong),
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trans, n, size(B, 2), A, max(1, stride(A, 2)), ipiv, B, max(1, stride(B, 2)), info,
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1)
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LinearAlgebra.LAPACK.chklapackerror(BlasInt(info[]))
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B
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end
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function getrs!(trans::AbstractChar,
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A::AbstractMatrix{<:Float32},
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ipiv::AbstractVector{BlasInt},
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B::AbstractVecOrMat{<:Float32};
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info = Ref{BlasInt}())
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require_one_based_indexing(A, ipiv, B)
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LinearAlgebra.LAPACK.chktrans(trans)
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chkstride1(A, B, ipiv)
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n = LinearAlgebra.checksquare(A)
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if n != size(B, 1)
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throw(DimensionMismatch("B has leading dimension $(size(B,1)), but needs $n"))
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end
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if n != length(ipiv)
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throw(DimensionMismatch("ipiv has length $(length(ipiv)), but needs to be $n"))
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end
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nrhs = size(B, 2)
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ccall(("sgetrs_", liblapack), Cvoid,
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(Ref{UInt8}, Ref{BlasInt}, Ref{BlasInt}, Ptr{Float32}, Ref{BlasInt},
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Ptr{BlasInt}, Ptr{Float32}, Ref{BlasInt}, Ptr{BlasInt}, Clong),
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trans, n, size(B, 2), A, max(1, stride(A, 2)), ipiv, B, max(1, stride(B, 2)), info,
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1)
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LinearAlgebra.LAPACK.chklapackerror(BlasInt(info[]))
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B
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end
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default_alias_A(::BLISLUFactorization, ::Any, ::Any) = false
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default_alias_b(::BLISLUFactorization, ::Any, ::Any) = false
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const PREALLOCATED_BLIS_LU = begin
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A = rand(0, 0)
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luinst = ArrayInterface.lu_instance(A), Ref{BlasInt}()
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end
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function LinearSolve.init_cacheval(alg::BLISLUFactorization, A, b, u, Pl, Pr,
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maxiters::Int, abstol, reltol, verbose::Bool,
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assumptions::OperatorAssumptions)
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PREALLOCATED_BLIS_LU
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end
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function LinearSolve.init_cacheval(alg::BLISLUFactorization, A::AbstractMatrix{<:Union{Float32,ComplexF32,ComplexF64}}, b, u, Pl, Pr,
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maxiters::Int, abstol, reltol, verbose::Bool,
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assumptions::OperatorAssumptions)
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A = rand(eltype(A), 0, 0)
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ArrayInterface.lu_instance(A), Ref{BlasInt}()
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end
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function SciMLBase.solve!(cache::LinearCache, alg::BLISLUFactorization;
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kwargs...)
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A = cache.A
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A = convert(AbstractMatrix, A)
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if cache.isfresh
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cacheval = @get_cacheval(cache, :BLISLUFactorization)
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res = getrf!(A; ipiv = cacheval[1].ipiv, info = cacheval[2])
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fact = LU(res[1:3]...), res[4]
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cache.cacheval = fact
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cache.isfresh = false
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end
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y = ldiv!(cache.u, @get_cacheval(cache, :BLISLUFactorization)[1], cache.b)
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SciMLBase.build_linear_solution(alg, y, nothing, cache)
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#=
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A, info = @get_cacheval(cache, :BLISLUFactorization)
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LinearAlgebra.require_one_based_indexing(cache.u, cache.b)
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m, n = size(A, 1), size(A, 2)
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if m > n
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Bc = copy(cache.b)
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getrs!('N', A.factors, A.ipiv, Bc; info)
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return copyto!(cache.u, 1, Bc, 1, n)
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else
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copyto!(cache.u, cache.b)
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getrs!('N', A.factors, A.ipiv, cache.u; info)
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end
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SciMLBase.build_linear_solution(alg, cache.u, nothing, cache)
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=#
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end
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end

‎src/extension_algs.jl

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to avoid allocations and automatically offloads to the GPU.
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"""
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struct MetalLUFactorization <: AbstractFactorization end
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struct BLISLUFactorization <: AbstractFactorization end

‎test/basictests.jl

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using Test
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import Random
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# Try to load BLIS extension
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try
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using blis_jll, LAPACK_jll
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catch LoadError
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# BLIS dependencies not available, tests will be skipped
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end
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const Dual64 = ForwardDiff.Dual{Nothing, Float64, 1}
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n = 8
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push!(test_algs, MKLLUFactorization())
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end
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# Test BLIS if extension is available
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if Base.get_extension(LinearSolve, :LinearSolveBLISExt) !== nothing
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push!(test_algs, BLISLUFactorization())
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end
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@testset "Concrete Factorizations" begin
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for alg in test_algs
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@testset "$alg" begin

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