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rasmushenningssonrasmushenningsson
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Make ThreadedSparseMatrixCSC subtype of AbstractSparseMatrixCSC (#5)
* Make ThreadedSparseMatrixCSC subtype of AbstractSparseMatrixCSC * Remove ThreadedColumnizedSparseMatrix * Add GitHub CI
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‎.github/workflows/ci.yml

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name: CI
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on:
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pull_request:
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branches:
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- master
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push:
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branches:
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- master
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tags: '*'
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jobs:
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test:
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name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}
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runs-on: ${{ matrix.os }}
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strategy:
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fail-fast: false
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matrix:
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version:
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- '1.4' # Replace this with the minimum Julia version that your package supports. E.g. if your package requires Julia 1.5 or higher, change this to '1.5'.
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- '1' # Leave this line unchanged. '1' will automatically expand to the latest stable 1.x release of Julia.
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- 'nightly'
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os:
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- ubuntu-latest
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- macOS-latest
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- windows-latest
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arch:
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- x64
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steps:
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- uses: actions/checkout@v2
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- uses: julia-actions/setup-julia@v1
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with:
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version: ${{ matrix.version }}
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arch: ${{ matrix.arch }}
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- uses: actions/cache@v1
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env:
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cache-name: cache-artifacts
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with:
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path: ~/.julia/artifacts
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key: ${{ runner.os }}-test-${{ env.cache-name }}-${{ hashFiles('**/Project.toml') }}
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restore-keys: |
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${{ runner.os }}-test-${{ env.cache-name }}-
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${{ runner.os }}-test-
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${{ runner.os }}-
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- uses: julia-actions/julia-buildpkg@v1
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- uses: julia-actions/julia-runtest@v1
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- uses: julia-actions/julia-processcoverage@v1
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- uses: codecov/codecov-action@v1
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with:
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file: lcov.info

‎src/ThreadedSparseArrays.jl

Lines changed: 7 additions & 54 deletions
Original file line numberDiff line numberDiff line change
@@ -1,9 +1,13 @@
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module ThreadedSparseArrays
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export ThreadedSparseMatrixCSC
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using LinearAlgebra
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import LinearAlgebra: mul!
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using SparseArrays
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import SparseArrays: getcolptr
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import SparseArrays: getcolptr, AbstractSparseMatrixCSC
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const AdjOrTransDenseMatrix = if VERSION < v"1.6.0-rc2"
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SparseArrays.AdjOrTransStridedOrTriangularMatrix
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else
@@ -36,31 +40,18 @@ Base.iterate(it::RangeIterator, i::Int=1) = i>it.k ? nothing : (endpos(it,i-1)+1
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Thin container around `A::SparseMatrixCSC` that will enable certain
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threaded multiplications of `A` with dense matrices.
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"""
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struct ThreadedSparseMatrixCSC{Tv,Ti,At} <: AbstractSparseMatrix{Tv,Ti}
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struct ThreadedSparseMatrixCSC{Tv,Ti,At} <: AbstractSparseMatrixCSC{Tv,Ti}
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A::At
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ThreadedSparseMatrixCSC(A::At) where {Tv,Ti,At<:AbstractSparseMatrix{Tv,Ti}} =
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ThreadedSparseMatrixCSC(A::At) where {Tv,Ti,At<:AbstractSparseMatrixCSC{Tv,Ti}} =
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new{Tv,Ti,At}(A)
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end
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Base.size(A::ThreadedSparseMatrixCSC, args...) = size(A.A, args...)
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Base.eltype(A::ThreadedSparseMatrixCSC) = eltype(A.A)
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Base.getindex(A::ThreadedSparseMatrixCSC, args...) = getindex(A.A, args...)
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# Need to override printing
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# Need to forward findnz, etc
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for f in [:rowvals, :nonzeros, :getcolptr]
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@eval SparseArrays.$(f)(A::ThreadedSparseMatrixCSC) = SparseArrays.$(f)(A.A)
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end
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# For non-threaded implementations, fallback to sparse methods and not generic matmul.
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mul!(C::AbstractVector, A::ThreadedSparseMatrixCSC, B::AbstractVector, α::Number, β::Number) = mul!(C, A.A, B, α, β)
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mul!(C::AbstractMatrix, A::ThreadedSparseMatrixCSC, B::AbstractMatrix, α::Number, β::Number) = mul!(C, A.A, B, α, β)
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mul!(C::AbstractVector, A::Adjoint{<:Any,<:ThreadedSparseMatrixCSC}, B::AbstractVector, α::Number, β::Number) = mul!(C, adjoint(A.parent.A), B, α, β)
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mul!(C::AbstractMatrix, A::Adjoint{<:Any,<:ThreadedSparseMatrixCSC}, B::AbstractMatrix, α::Number, β::Number) = mul!(C, adjoint(A.parent.A), B, α, β)
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mul!(C::AbstractVector, A::Transpose{<:Any,<:ThreadedSparseMatrixCSC}, B::AbstractVector, α::Number, β::Number) = mul!(C, transpose(A.parent.A), B, α, β)
62-
mul!(C::AbstractMatrix, A::Transpose{<:Any,<:ThreadedSparseMatrixCSC}, B::AbstractMatrix, α::Number, β::Number) = mul!(C, transpose(A.parent.A), B, α, β)
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function mul!(C::StridedVecOrMat, A::ThreadedSparseMatrixCSC, B::Union{StridedVector,AdjOrTransDenseMatrix}, α::Number, β::Number)
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size(A, 2) == size(B, 1) || throw(DimensionMismatch())
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size(A, 1) == size(C, 1) || throw(DimensionMismatch())
@@ -201,42 +192,4 @@ function mul!(C::StridedVecOrMat, X::AdjOrTransDenseMatrix, A::ThreadedSparseMat
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C
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end
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# * ThreadedColumnizedSparseMatrix
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"""
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ThreadedColumnizedSparseMatrix(columns, m, n)
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Sparse matrix of size `m×n` where the `columns` are stored separately,
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enabling threaded multiplication. Seems faster than
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[`ThreadedSparseMatrixCSC`](@ref) for some use cases.
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"""
213-
struct ThreadedColumnizedSparseMatrix{Tv,Ti,Columns} <: AbstractSparseMatrix{Tv,Ti}
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columns::Columns
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m::Int
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n::Int
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ThreadedColumnizedSparseMatrix(::Type{Tv}, ::Type{Ti}, columns::Columns, m, n) where {Tv,Ti,Columns} =
218-
new{Tv,Ti,Columns}(columns, m, n)
219-
end
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221-
function ThreadedColumnizedSparseMatrix(A::AbstractSparseMatrix{Tv,Ti}) where {Tv,Ti}
222-
m,n = size(A)
223-
Column = typeof(A[:,1])
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columns = Column[A[:,j] for j = 1:n]
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ThreadedColumnizedSparseMatrix(Tv, Ti, columns, m, n)
226-
end
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Base.size(A::ThreadedColumnizedSparseMatrix) = (A.m,A.n)
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Base.size(A::ThreadedColumnizedSparseMatrix,i) = size(A)[i]
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Base.getindex(A::ThreadedColumnizedSparseMatrix, i, j) = A.columns[j][i]
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232-
function LinearAlgebra.mul!(A::AbstractMatrix, B::AbstractMatrix, C::ThreadedColumnizedSparseMatrix,
233-
α::Number=true, β::Number=false)
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Threads.@threads for j = 1:C.n
235-
LinearAlgebra.mul!(view(A, :, j), B, C.columns[j], α, β)
236-
end
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A
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end
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export ThreadedSparseMatrixCSC, ThreadedColumnizedSparseMatrix
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end # module

‎test/runtests.jl

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@@ -9,7 +9,7 @@ using Test
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T = ComplexF64
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C = sprand(T, N, n, 0.05)
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@testset "$(Mat)_R" for Mat in [ThreadedSparseMatrixCSC, ThreadedColumnizedSparseMatrix]
12+
@testset "$(Mat)_R" for Mat in [ThreadedSparseMatrixCSC]
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Ct = Mat(C)
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1515
eye = Matrix(one(T)*I, N, N)
@@ -56,8 +56,7 @@ using Test
5656
@testset "$(Mat)_L_sparsevec" for Mat in [ThreadedSparseMatrixCSC]
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Ct = Mat(C)
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59-
out = similar(sx, T, N)
60-
LinearAlgebra.mul!(out, Ct, sx)
59+
out = Ct*sx
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ref = C*sx
6261
@test norm(ref-out) == 0
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@test typeof(ref)==typeof(out)
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@testset "$(Mat)_L_$(op)_sparsevec" for op in [adjoint,transpose], Mat in [ThreadedSparseMatrixCSC]
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Ct = Mat(C)
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70-
out = similar(sx, T, n)
71-
LinearAlgebra.mul!(out, op(Ct), sx)
69+
out = op(Ct)*sx
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ref = op(C)*sx
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@test norm(ref-out) == 0
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@test typeof(ref)==typeof(out)
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@testset "$(Mat)_L_sparse" for Mat in [ThreadedSparseMatrixCSC]
7977
Ct = Mat(C)
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81-
out = similar(sx, T, N, 10)
82-
LinearAlgebra.mul!(out, Ct, sx)
79+
out = Ct*sx
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ref = C*sx
8481
@test norm(ref-out) == 0
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@test typeof(ref)==typeof(out)
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8986
@testset "$(Mat)_L_$(op)_sparse" for op in [adjoint,transpose], Mat in [ThreadedSparseMatrixCSC]
9087
Ct = Mat(C)
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92-
out = similar(sx, T, n, 10)
93-
LinearAlgebra.mul!(out, op(Ct), sx)
89+
out = op(Ct)*sx
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ref = op(C)*sx
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@test norm(ref-out) == 0
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@test typeof(ref)==typeof(out)

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