Replace to_host by Array. (#226)

NEWS.md

@@ -23,7 +23,7 @@ Breaking changes:
 - Argument conversion has been removed; the user should make sure Julia arguments passed to
   kernels match the OpenCL argument types (i.e., no empty types, 4-element tuples for
   a 3-element `float3` arguments).
-
+- The `to_host` function has been replaced by simply calling `Array` on the `CLArray`.
 
 New features:
 

src/array.jl

@@ -1,6 +1,6 @@
 import LinearAlgebra
 
-export CLArray, CLMatrix, CLVector, to_host
+export CLArray, CLMatrix, CLVector
 
 mutable struct CLArray{T, N} <: CLObject
     ctx::cl.Context
@@ -66,26 +66,24 @@ Base.size(A::CLArray) = A.size
 Base.size(A::CLArray, dim::Integer) = A.size[dim]
 Base.ndims(A::CLArray) = length(size(A))
 Base.length(A::CLArray) = prod(size(A))
-Base.:(==)(A:: CLArray, B:: CLArray) =
-    buffer(A) == buffer(B) && size(A) == size(B)
+Base.:(==)(A:: CLArray, B:: CLArray) = buffer(A) == buffer(B) && size(A) == size(B)
+
 function Base.reshape(A::CLArray, dims...)
     @assert prod(dims) == prod(size(A))
     return copy(A, size=dims)
 end
 
-## show
-
-Base.show(io::IO, A::CLArray{T,N}) where {T, N} =
-    print(io, "CLArray{$T,$N}($(buffer(A)),$(size(A)))")
-
-## to_host
-
-function to_host(A::CLArray{T,N}) where {T, N}
+function Base.Array(A::CLArray{T,N}) where {T, N}
     hA = Array{T}(undef, size(A)...)
     copy!(hA, buffer(A))
     return hA
 end
 
+## show
+
+Base.show(io::IO, A::CLArray{T,N}) where {T, N} =
+    print(io, "CLArray{$T,$N}($(buffer(A)),$(size(A)))")
+
 ## other array operations
 
 const TRANSPOSE_FLOAT_PROGRAM_PATH = joinpath(@__DIR__, "kernels", "transpose_float.cl")
@@ -107,8 +105,8 @@ function LinearAlgebra.transpose!(B::CLMatrix{Float32}, A::CLMatrix{Float32})
                         block_size=block_size)
     h, w = size(A)
     lmem = cl.LocalMem(Float32, block_size * (block_size + 1))
-    cl.set_args!(kernel, buffer(B), buffer(A), UInt32(h), UInt32(w), lmem)
-    return cl.enqueue_kernel(kernel, (h, w), (block_size, block_size))
+    return cl.call(kernel, buffer(B), buffer(A), UInt32(h), UInt32(w), lmem;
+                   global_size=(h, w), local_size=(block_size, block_size))
 end
 
 """Transpose CLMatrix A"""
@@ -128,10 +126,9 @@ function LinearAlgebra.transpose!(B::CLMatrix{Float64}, A::CLMatrix{Float64})
     kernel = get_kernel(TRANSPOSE_DOUBLE_PROGRAM_PATH, "transpose",
                         block_size=block_size)
     h, w = size(A)
-    # lmem = cl.LocalMem(Float64, block_size * (block_size + 1))
-    lmem = cl.LocalMem(Float64, block_size * block_size)
-    cl.set_args!(kernel, buffer(B), buffer(A), UInt32(h), UInt32(w), lmem)
-    return cl.enqueue_kernel(kernel, (h, w), (block_size, block_size))
+    lmem = cl.LocalMem(Float32, block_size * (block_size + 1))
+    return cl.call(kernel, buffer(B), buffer(A), UInt32(h), UInt32(w), lmem;
+                   global_size=(h, w), local_size=(block_size, block_size))
 end
 
 """Transpose CLMatrix A"""

test/array.jl

@@ -18,10 +18,10 @@ using LinearAlgebra
     end
 
     @testset "fill" begin
-        @test to_host(OpenCL.fill(Float32, Float32(0.5),
-                                  32, 64)) == fill(Float32(0.5), 32, 64)
-        @test to_host(OpenCL.zeros(Float32, 64)) == zeros(Float32, 64)
-        @test to_host(OpenCL.ones(Float32, 64)) == ones(Float32, 64)
+        @test Array(OpenCL.fill(Float32, Float32(0.5),
+                                32, 64)) == fill(Float32(0.5), 32, 64)
+        @test Array(OpenCL.zeros(Float32, 64)) == zeros(Float32, 64)
+        @test Array(OpenCL.ones(Float32, 64)) == ones(Float32, 64)
     end
 
     @testset "core functions" begin
@@ -35,10 +35,8 @@ using LinearAlgebra
         @test reshape(B, 128, 64) == A
 
         # transpose
-        X = CLArray(rand(Float32, 32, 32))
         B = OpenCL.zeros(Float32, 64, 128)
         ev = transpose!(B, A)
-        cl.wait(ev)
-        #@test to_host(copy(A')) == to_host(B)
+        @test Array(A)' == Array(B)
     end
 end