Fix TSVD example from README and test it (#59)

Author: andreasnoack

Project.toml

@@ -13,13 +13,15 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 DistributedArrays = "0.5, 0.6"
 MPI = "0.11"
 MPIClusterManagers = "0.1"
+TSVD = "0.4"
 julia = "1"
 
 [extras]
 MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
 MPIClusterManagers = "e7922434-ae4b-11e9-05c5-9780451d2c66"
 Primes = "27ebfcd6-29c5-5fa9-bf4b-fb8fc14df3ae"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+TSVD = "9449cd9e-2762-5aa3-a617-5413e99d722e"
 
 [targets]
-test = ["Test", "MPI", "MPIClusterManagers", "Primes"]
+test = ["Test", "MPI", "MPIClusterManagers", "Primes", "TSVD"]

README.md

@@ -93,11 +93,7 @@ julia> man = MPIManager(np = 4);
 
 julia> addprocs(man);
 
-julia> using Elemental
-
-julia> Pkg.clone("TSVD")
-
-julia> @everywhere using TSVD
+julia> @mpi_do man using Elemental, TSVD
 
 julia> @mpi_do man A = Elemental.DistMatrix(Float64);
 

src/blas_like/level1.jl

@@ -54,7 +54,7 @@ for (elty, relty, ext) in ((:Float32, :Float32, :s),
                 return rval[]
             end
 
-            function scale!(x::$mat{$elty}, val::Number)
+            function LinearAlgebra.rmul!(x::$mat{$elty}, val::Number)
                 ElError(ccall(($(string("ElScale", sym, ext)), libEl), Cuint,
                     (Ptr{Cvoid}, $elty),
                     x.obj, $elty(val)))

src/julia/generic.jl

@@ -88,7 +88,7 @@ LinearAlgebra.svdvals(A::ElementalMatrix, ctrl::SVDCtrl) = svdvals!(copy(A), ctr
 #     return dest
 # end
 
-function Base.copy!(dest::DistMatrix{T}, src::Base.AbstractVecOrMat) where {T}
+function Base.copyto!(dest::DistMatrix{T}, src::Base.AbstractVecOrMat) where {T}
     m, n = size(src, 1), size(src, 2)
     zeros!(dest, m, n)
     if MPI.commRank(comm(dest)) == 0
@@ -102,9 +102,9 @@ function Base.copy!(dest::DistMatrix{T}, src::Base.AbstractVecOrMat) where {T}
     return dest
 end
 
-Base.copy!(dest::DistMatrix, src::ElementalMatrix) = _copy!(src, dest)
+Base.copyto!(dest::DistMatrix, src::ElementalMatrix) = _copy!(src, dest)
 
-function Base.copy!(dest::Base.VecOrMat, src::DistMatrix{T}) where {T}
+function Base.copyto!(dest::Base.VecOrMat, src::DistMatrix{T}) where {T}
     m, n = size(src, 1), size(src, 2)
     if MPI.commRank(comm(src)) == 0
         for j = 1:n
@@ -177,7 +177,7 @@ function Base.convert(::Type{DistMatrix{T}}, A::DistMultiVec{T}) where {T}
 end
 
 Base.convert(::Type{Array}, xd::DistMatrix{T}) where {T} = 
-    Base.copy!(Base.zeros(T, size(xd)), xd)
+    Base.copyto!(Base.zeros(T, size(xd)), xd)
 
 Base.Array(xd::DistMatrix) = convert(Array, xd)
 
@@ -194,3 +194,11 @@ LinearAlgebra.cholesky!(A::Hermitian{<:Union{Real,Complex},<:ElementalMatrix}) =
 LinearAlgebra.cholesky(A::Hermitian{<:Union{Real,Complex},<:ElementalMatrix}) = cholesky!(copy(A))
 
 LinearAlgebra.lu(A::ElementalMatrix) = _lu!(copy(A))
+
+# Mixed multiplication with Julia Arrays
+(*)(A::DistMatrix{T}, B::StridedVecOrMat{T}) where {T} = A*convert(DistMatrix{T}, B)
+(*)(A::DistMultiVec{T}, B::StridedVecOrMat{T}) where {T} = convert(DistMatrix{T}, A)*convert(DistMatrix{T}, B)
+(*)(A::DistSparseMatrix{T}, B::StridedVecOrMat{T}) where {T} = A*convert(DistMultiVec{T}, B)
+(*)(A::Adjoint{T,DistMatrix{T}}, B::StridedVecOrMat{T}) where {T} = A*convert(DistMatrix{T}, B)
+(*)(A::Adjoint{T,DistMultiVec{T}}, B::Base.VecOrMat{T}) where {T} = convert(DistMatrix{T}, parent(A))'*convert(DistMatrix{T}, B)
+(*)(A::Adjoint{T,DistSparseMatrix{T}}, B::Base.VecOrMat{T}) where {T} = A*convert(DistMultiVec{T}, B)

test/runtests.jl

@@ -3,7 +3,7 @@ using Test
 function runtests_mpirun()
     nprocs = min(4, Sys.CPU_THREADS)
     testdir = dirname(@__FILE__)
-    testfiles = ["lav.jl", "lavdense.jl", "matrix.jl", "distmatrix.jl", "props.jl", "generic.jl", "spectral.jl"]
+    testfiles = ["lav.jl", "lavdense.jl", "matrix.jl", "distmatrix.jl", "props.jl", "generic.jl", "spectral.jl", "tsvd.jl"]
     nfail = 0
     @info "Running Elemental.jl tests"
     for f in testfiles

test/tsvd.jl

@@ -0,0 +1,7 @@
+using Elemental, TSVD, LinearAlgebra
+
+A = Elemental.DistMatrix(Float64)
+Elemental.gaussian!(A, 500, 200)
+vals_Elemental = tsvd(A, 5)[2]
+vals_LAPACK    = svdvals(convert(Array, A))[1:5]
+@test vals_Elemental ≈ vals_LAPACK