Unittest

src/DistStat.jl

@@ -10,7 +10,7 @@ function __init__()
         @require CUDAnative="be33ccc6-a3ff-5ff2-a52e-74243cff1e17" begin
             include("cuda.jl")
             set_device!()
-            CuArrays.allowscalar(false)
+            #CuArrays.allowscalar(false)
         end
     end
 

src/distlinalg.jl

@@ -174,7 +174,7 @@ end
 function LinearAlgebra.mul!(C::AbstractVector{T}, A::Transpose{T, MPIMatrix{T,AT}}, B::AbstractVector{T}) where {T,AT}
     localA = get_local(A)
     fill!(C, zero(T))
-    LinearAlgebra.mul!(C[transpose(A).partitioning[Rank()+1][2]], localA, B[transpose(A).partitioning[Rank()+1][1]])
+    LinearAlgebra.mul!(@view(C[transpose(A).partitioning[Rank()+1][2]]), localA, B[transpose(A).partitioning[Rank()+1][1]])
     sync()
     Allreduce!(C)
     C

test/test_aux.jl

@@ -0,0 +1,37 @@
+using DistStat, Random, Test, Pkg
+
+type=[Float32,Float64]
+
+if get(ENV,"JULIA_MPI_TEST_ARRAYTYPE","") == "CuArray"
+    using CuArrays
+    ArrayType = CuArray
+else
+    ArrayType = Array
+end
+
+for T in type
+  A=ArrayType{T}(undef,7,10)
+  A_dist=distribute(A)
+  fill!(A, 1.0)
+  fill!(A_dist,1.0)
+  cols1=A_dist.partitioning[DistStat.Rank()+1][2]
+
+  @test isapprox(A_dist.localarray,A[:,cols1])
+
+  B=ArrayType{T}(reshape(collect(1:70), 7, 10))
+  B_dist=distribute(B)
+  cols2=B_dist.partitioning[DistStat.Rank()+1][2]
+
+  @test isapprox(B_dist.localarray,B[:,cols2])
+
+  C_dist = MPIArray{T, 2, ArrayType}(undef, 7, 9)
+  cols3=C_dist.partitioning[DistStat.Rank()+1][2]
+  randn!(C_dist; seed=0,common_init=true)
+
+  C=Array{T}(undef,size(C_dist))
+  Random.seed!(0)
+  randn!(C)
+
+  @test isapprox(C_dist.localarray, ArrayType{T}(C[:,cols3]))
+
+end

test/test_distribute.jl

@@ -0,0 +1,23 @@
+using Pkg, Test, DistStat
+
+type=[Float64,Float32]
+
+if get(ENV,"JULIA_MPI_TEST_ARRAYTYPE","") == "CuArray"
+    using CuArrays
+    ArrayType = CuArray
+else
+    ArrayType = Array
+end
+
+for T in type
+
+ data =ArrayType{T}(reshape(collect(1:42),6,7))
+ data_dist1 = distribute(data)
+ data_dist2 = distribute(ArrayType{T}(transpose(data)))
+ cols1=data_dist1.partitioning[DistStat.Rank()+1][2]
+ cols2=data_dist2.partitioning[DistStat.Rank()+1][2]
+
+ @test data_dist1.localarray==data[:,cols1]
+ @test data_dist2.localarray==(ArrayType{T}(transpose(data)))[:,cols2]
+
+end

test/test_dot.jl

@@ -0,0 +1,24 @@
+using DistStat, Random, Test, LinearAlgebra
+
+type=[Float32,Float64]
+
+if get(ENV,"JULIA_MPI_TEST_ARRAYTYPE","") == "CuArray"
+    using CuArrays
+    ArrayType = CuArray
+else
+    ArrayType = Array
+end
+
+for T in type
+    A=ArrayType{T}(reshape(collect(1:36),4,9))
+    B=ArrayType{T}(reshape(collect(-7:28),4,9))
+
+    A_dist=distribute(A); B_dist=distribute(B)
+
+    @test isapprox(LinearAlgebra.dot(A_dist,B_dist),LinearAlgebra.dot(A,B))
+
+    A_vec=vec(A); B_vec=vec(B)
+
+    @test isapprox(LinearAlgebra.dot(A_dist,B_dist),LinearAlgebra.dot(A_vec,B_vec))
+
+end

test/test_mul2.jl

@@ -0,0 +1,39 @@
+using DistStat, LinearAlgebra, Test, Random
+
+type=[Float32,Float64]
+
+if get(ENV,"JULIA_MPI_TEST_ARRAYTYPE","") == "CuArray"
+    using CuArrays
+    ArrayType = CuArray
+else
+    ArrayType = Array
+end
+
+for T in type
+
+    A=ArrayType{T}(reshape(collect(1:63),7,9))
+    B=ArrayType{T}(reshape(collect(-31:31),7,9))
+    A_dist=distribute(A)
+    B_dist=distribute(B)
+    B_distt=distribute(ArrayType{T}(transpose(B)))
+
+    C=MPIMatrix{T,ArrayType}(undef,9,9)
+    cols1=B_dist.partitioning[DistStat.Rank()+1][2]
+    cols2=B_distt.partitioning[DistStat.Rank()+1][2]
+
+    result1=LinearAlgebra.mul!(C,transpose(A),B_dist)
+    ans1=transpose(A)*B
+
+    @test isapprox(result1.localarray, ans1[:,cols1])
+
+    result2=LinearAlgebra.mul!(transpose(C),transpose(A_dist),B)
+    @test isapprox(result2.localarray, ArrayType{T}(transpose(ans1))[:,cols1])
+
+
+    B_vec = ArrayType{T}(collect(1:7))
+    C_vec = ArrayType{T}(undef, 9)
+    LinearAlgebra.mul!(C_vec, transpose(A_dist), B_vec)
+    C_true = transpose(A) * B_vec
+    @test isapprox(C_vec, C_true)
+
+end

test/test_opnorm2.jl

@@ -0,0 +1,20 @@
+using DistStat, LinearAlgebra, Test 
+
+type=[Float32,Float64]
+
+if get(ENV,"JULIA_MPI_TEST_ARRAYTYPE","") == "CuArray"
+    using CuArrays
+    ArrayType = CuArray
+else
+    ArrayType = Array
+end
+
+for T in type
+    A=ArrayType{T}(reshape(collect(1:45),5,9))
+    A_dist=distribute(A)
+
+    @test isapprox(opnorm(A_dist,1),opnorm(A,1))
+    @test isapprox(opnorm(A_dist,2),opnorm(A,2))
+    @test isapprox(opnorm(A_dist,Inf),opnorm(A,Inf))
+
+end