Some more Array changes

Author: andreasnoack

src/linalg.jl

@@ -1,14 +1,14 @@
 function Base.ctranspose{T}(D::DArray{T,2})
     DArray(reverse(size(D)), procs(D)) do I
-        lp = Array(T, map(length, I))
+        lp = Array{T}(map(length, I))
         rp = convert(Array, D[reverse(I)...])
         ctranspose!(lp, rp)
     end
 end
 
 function Base.transpose{T}(D::DArray{T,2})
     DArray(reverse(size(D)), procs(D)) do I
-        lp = Array(T, map(length, I))
+        lp = Array{T}(map(length, I))
         rp = convert(Array, D[reverse(I)...])
         transpose!(lp, rp)
     end
@@ -91,7 +91,7 @@ function A_mul_B!(α::Number, A::DMatrix, x::AbstractVector, β::Number, y::DVec
     end
 
     # Multiply on each tile of A
-    R = Array(Future, size(A.pids)...)
+    R = Array{Future}(size(A.pids)...)
     for j = 1:size(A.pids, 2)
         xj = x[A.cuts[2][j]:A.cuts[2][j + 1] - 1]
         for i = 1:size(A.pids, 1)
@@ -135,7 +135,7 @@ function Ac_mul_B!(α::Number, A::DMatrix, x::AbstractVector, β::Number, y::DVe
     end
 
     # Multiply on each tile of A
-    R = Array(Future, reverse(size(A.pids))...)
+    R = Array{Future}(reverse(size(A.pids))...)
     for j = 1:size(A.pids, 1)
         xj = x[A.cuts[1][j]:A.cuts[1][j + 1] - 1]
         for i = 1:size(A.pids, 2)
@@ -201,9 +201,9 @@ function _matmatmul!(α::Number, A::DMatrix, B::AbstractMatrix, β::Number, C::D
 
     # Multiply on each tile of A
     if tA == 'N'
-        R = Array(Future, size(procs(A))..., size(procs(C), 2))
+        R = Array{Future}(size(procs(A))..., size(procs(C), 2))
     else
-        R = Array(Future, reverse(size(procs(A)))..., size(procs(C), 2))
+        R = Array{Future}(reverse(size(procs(A)))..., size(procs(C), 2))
     end
     for j = 1:size(A.pids, Ad2)
         for k = 1:size(C.pids, 2)
@@ -258,12 +258,12 @@ _matmul_op = (t,s) -> t*s + t*s
 
 function (*)(A::DMatrix, x::AbstractVector)
     T = Base.promote_op(_matmul_op, eltype(A), eltype(x))
-    y = DArray(I -> Array(T, map(length, I)), (size(A, 1),), procs(A)[:,1], (size(procs(A), 1),))
+    y = DArray(I -> Array{T}(map(length, I)), (size(A, 1),), procs(A)[:,1], (size(procs(A), 1),))
     return A_mul_B!(one(T), A, x, zero(T), y)
 end
 function (*)(A::DMatrix, B::AbstractMatrix)
     T = Base.promote_op(_matmul_op, eltype(A), eltype(B))
-    C = DArray(I -> Array(T, map(length, I)),
+    C = DArray(I -> Array{T}(map(length, I)),
             (size(A, 1), size(B, 2)),
             procs(A)[:,1:min(size(procs(A), 2), size(procs(B), 2))],
             (size(procs(A), 1), min(size(procs(A), 2), size(procs(B), 2))))
@@ -272,15 +272,15 @@ end
 
 function Ac_mul_B(A::DMatrix, x::AbstractVector)
     T = Base.promote_op(_matmul_op, eltype(A), eltype(x))
-    y = DArray(I -> Array(T, map(length, I)),
+    y = DArray(I -> Array{T}(map(length, I)),
             (size(A, 2),),
             procs(A)[1,:],
             (size(procs(A), 2),))
     return Ac_mul_B!(one(T), A, x, zero(T), y)
 end
 function Ac_mul_B(A::DMatrix, B::AbstractMatrix)
     T = Base.promote_op(_matmul_op, eltype(A), eltype(B))
-    C = DArray(I -> Array(T, map(length, I)), (size(A, 2),
+    C = DArray(I -> Array{T}(map(length, I)), (size(A, 2),
         size(B, 2)),
         procs(A)[1:min(size(procs(A), 1), size(procs(B), 2)),:],
         (size(procs(A), 2), min(size(procs(A), 1), size(procs(B), 2))))

src/mapreduce.jl

@@ -131,7 +131,7 @@ Base.mapreducedim(f, op, R::DArray, A::DArray) = begin
 end
 
 function nnz(A::DArray)
-    B = Array(Any, size(A.pids))
+    B = Array{Any}(size(A.pids))
     @sync begin
         for i in eachindex(A.pids)
             @async B[i...] = remotecall_fetch(x -> nnz(localpart(x)), A.pids[i...], A)
@@ -301,7 +301,7 @@ function _ppeval(f, A...; dim = map(ndims, A))
     push!(ridx, 1)
     Rsize = map(last, ridx)
     Rsize[end] = dimlength
-    R = Array(eltype(R1), Rsize...)
+    R = Array{eltype(R1)}(Rsize...)
 
     for i = 1:dimlength
         for j = 1:narg

src/sort.jl

@@ -38,7 +38,7 @@ function scatter_n_sort_localparts{T}(d, myidx, refs::Array{RemoteChannel}, boun
         end
 
         if p_till == p_sorted
-            @async put!(r, Array(T,0))
+            @async put!(r, Array{T}(0))
         else
             v = sorted[p_sorted:p_till-1]
             @async put!(r, v)
@@ -66,7 +66,7 @@ function compute_boundaries{T}(d::DVector{T}; kwargs...)
 
     results = asyncmap(p -> remotecall_fetch(sample_n_setup_ref, p, d, sample_sz_on_wrkr; kwargs...), pids)
 
-    samples = Array(T,0)
+    samples = Array{T}(0)
     for x in results
         append!(samples, x[1])
     end
@@ -128,7 +128,7 @@ function Base.sort{T}(d::DVector{T}; sample=true, kwargs...)
 
         @assert lb<=ub
 
-        s = Array(T, np)
+        s = Array{T}(np)
         part = abs(ub - lb)/np
         (isnan(part) || isinf(part)) && throw(ArgumentError("lower and upper bounds must not be infinities"))
 
@@ -155,7 +155,7 @@ function Base.sort{T}(d::DVector{T}; sample=true, kwargs...)
         throw(ArgumentError("keyword arg `sample` must be Boolean, Tuple(Min,Max) or an actual sample of data : " * string(sample)))
     end
 
-    local_sort_results = Array(Tuple, np)
+    local_sort_results = Array{Tuple}(np)
 
     Base.asyncmap!((i,p) -> remotecall_fetch(
             scatter_n_sort_localparts, p, presorted ? nothing : d, i, refs, boundaries; kwargs...),

test/darray.jl

@@ -343,7 +343,7 @@ end
 check_leaks(t)
 
 t=@testset "test max / min / sum" begin
-    a = map(x -> Int(round(rand() * 100)) - 50, Array(Int, 100,1000))
+    a = map(x -> Int(round(rand() * 100)) - 50, Array{Int}(100,1000))
     d = distribute(a)
 
     @test sum(d)          == sum(a)
@@ -359,7 +359,7 @@ end
 check_leaks(t)
 
 t=@testset "test all / any" begin
-    a = map(x->Int(round(rand() * 100)) - 50, Array(Int, 100,1000))
+    a = map(x->Int(round(rand() * 100)) - 50, Array{Int}(100,1000))
     a = [true for i in 1:100]
     d = distribute(a)