Fix return code in tests and fix tests (#62)

* Add number of test failures instaed of computing the bitwise and of them

* Fix tests

* Fix conversion from distributed vector to local Julia matrix such
that each worker gets a copy of the distribted matrix. Also, fix
seed in TSVD tests to avoid different seeds on each worker

* Add Random to test dependencies

Author: andreasnoack

Project.toml

@@ -20,8 +20,9 @@ julia = "1"
 MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
 MPIClusterManagers = "e7922434-ae4b-11e9-05c5-9780451d2c66"
 Primes = "27ebfcd6-29c5-5fa9-bf4b-fb8fc14df3ae"
-Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 TSVD = "9449cd9e-2762-5aa3-a617-5413e99d722e"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["Test", "MPI", "MPIClusterManagers", "Primes", "TSVD"]
+test = ["Test", "MPI", "MPIClusterManagers", "Primes", "TSVD", "Random"]

README.md

@@ -93,12 +93,14 @@ julia> man = MPIManager(np = 4);
 
 julia> addprocs(man);
 
-julia> @mpi_do man using Elemental, TSVD
+julia> @mpi_do man using Elemental, TSVD, Random
 
 julia> @mpi_do man A = Elemental.DistMatrix(Float64);
 
 julia> @mpi_do man Elemental.gaussian!(A, 5000, 2000);
 
+julia> @mpi_do man Random.seed!(123) # to avoid different initial vectors on the workers
+
 julia> @mpi_do man r = tsvd(A, 5);
 
 julia> @mpi_do man println(r[2][1:5])

src/julia/generic.jl

@@ -106,13 +106,13 @@ Base.copyto!(dest::DistMatrix, src::ElementalMatrix) = _copy!(src, dest)
 
 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
+    # if MPI.commRank(comm(src)) == 0
         for j = 1:n
             for i = 1:m
                 queuePull(src, i, j)
             end
         end
-    end
+    # end
     dest_mat = ndims(dest) == 1 ? reshape(dest, :, 1) : dest
     processPullQueue(src, dest_mat)
     return dest
@@ -176,8 +176,8 @@ function Base.convert(::Type{DistMatrix{T}}, A::DistMultiVec{T}) where {T}
     return B
 end
 
-Base.convert(::Type{Array}, xd::DistMatrix{T}) where {T} = 
-    Base.copyto!(Base.zeros(T, size(xd)), xd)
+Base.convert(::Type{Array}, xd::DistMatrix{T}) where {T} =
+    copyto!(Base.zeros(T, size(xd)), xd)
 
 Base.Array(xd::DistMatrix) = convert(Array, xd)
 

test/generic.jl

@@ -6,5 +6,5 @@ for T in (Float32, Float64, Complex{Float32}, Complex{Float64})
     Elemental.gaussian!(A, 10)
     @test convert(Elemental.DistMatrix{T}, ones(T, 5)) == ones(5,1)
     B = Elemental.DistMatrix(T)
-    Array(copy!(B, T[2 1; 1 2])) == T[2 1; 1 2]
+    Array(copyto!(B, T[2 1; 1 2])) == T[2 1; 1 2]
 end

test/lavdense.jl

@@ -104,8 +104,8 @@ end
 for T in (Float32, Float64)
     A = El.DistMatrix(T)
     b = El.DistMatrix(T)
-    copy!(A, T[2 1; 1 2])
-    copy!(b, T[4, 5])
+    copyto!(A, T[2 1; 1 2])
+    copyto!(b, T[4, 5])
     x = Array(El.leastSquares(A, b))
     if worldRank == 0
         @test isapprox(x, T[1, 2])

test/runtests.jl

@@ -51,7 +51,7 @@ function runtests_repl()
 end
 
 function runtests()
-    return runtests_mpirun() & runtests_repl()
+    return runtests_mpirun() + runtests_repl()
 end
 
 exit(runtests())

test/tsvd.jl

@@ -1,7 +1,12 @@
-using Elemental, TSVD, LinearAlgebra
-
+using Elemental, TSVD, LinearAlgebra, Test
+using Random
 A = Elemental.DistMatrix(Float64)
 Elemental.gaussian!(A, 500, 200)
+
+# Warning! If seed is not set, each worker will end up with a different initial vector
+# making the Lanczos processes inconsistent
+Random.seed!(123)
 vals_Elemental = tsvd(A, 5)[2]
-vals_LAPACK    = svdvals(convert(Array, A))[1:5]
+localA = Array(A)
+vals_LAPACK    = svdvals(localA)[1:5]
 @test vals_Elemental ≈ vals_LAPACK