Test GPUArrays reverse

[christiangnrd]

[only julia]
[only benchmarks]

[github-actions]

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Please consider running Runic (git runic master) to apply these changes.

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diff --git a/src/CUDAKernels.jl b/src/CUDAKernels.jl
index 995728201..abb789fa4 100644
--- a/src/CUDAKernels.jl
+++ b/src/CUDAKernels.jl
@@ -160,29 +160,29 @@ end
 
 KI.kiconvert(::CUDABackend, arg) = cudaconvert(arg)
 
-function KI.kifunction(::CUDABackend, f::F, tt::TT=Tuple{}; name=nothing, kwargs...) where {F,TT}
+function KI.kifunction(::CUDABackend, f::F, tt::TT = Tuple{}; name = nothing, kwargs...) where {F, TT}
     kern = cufunction(f, tt; name, kwargs...)
-    KI.KIKernel{CUDABackend, typeof(kern)}(CUDABackend(), kern)
+    return KI.KIKernel{CUDABackend, typeof(kern)}(CUDABackend(), kern)
 end
 
-function (obj::KI.KIKernel{CUDABackend})(args...; numworkgroups=nothing, workgroupsize=nothing, kwargs...)
+function (obj::KI.KIKernel{CUDABackend})(args...; numworkgroups = nothing, workgroupsize = nothing, kwargs...)
     threadsPerThreadgroup = isnothing(workgroupsize) ? 1 : workgroupsize
     threadgroupsPerGrid = isnothing(numworkgroups) ? 1 : numworkgroups
 
-    obj.kern(args...; threads=threadsPerThreadgroup, blocks=threadgroupsPerGrid, kwargs...)
+    return obj.kern(args...; threads = threadsPerThreadgroup, blocks = threadgroupsPerGrid, kwargs...)
 end
 
 
-function KI.kernel_max_work_group_size(::CUDABackend, kikern::KI.KIKernel{<:CUDABackend}; max_work_items::Int=typemax(Int))::Int
+function KI.kernel_max_work_group_size(::CUDABackend, kikern::KI.KIKernel{<:CUDABackend}; max_work_items::Int = typemax(Int))::Int
     kernel_config = launch_configuration(kikern.kern.fun)
 
-    Int(min(kernel_config.threads, max_work_items))
+    return Int(min(kernel_config.threads, max_work_items))
 end
 function KI.max_work_group_size(::CUDABackend)::Int
-    Int(attribute(device(), CUDA.DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK))
+    return Int(attribute(device(), CUDA.DEVICE_ATTRIBUTE_MAX_THREADS_PER_BLOCK))
 end
 function KI.multiprocessor_count(::CUDABackend)::Int
-    Int(attribute(device(), CUDA.DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT))
+    return Int(attribute(device(), CUDA.DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT))
 end
 
 ## indexing
@@ -198,7 +198,7 @@ end
 end
 
 @device_override @inline function KI.get_global_id()
-    return (; x = Int((blockIdx().x-1)*blockDim().x + threadIdx().x), y = Int((blockIdx().y-1)*blockDim().y + threadIdx().y), z = Int((blockIdx().z-1)*blockDim().z + threadIdx().z))
+    return (; x = Int((blockIdx().x - 1) * blockDim().x + threadIdx().x), y = Int((blockIdx().y - 1) * blockDim().y + threadIdx().y), z = Int((blockIdx().z - 1) * blockDim().z + threadIdx().z))
 end
 
 @device_override @inline function KI.get_local_size()
diff --git a/src/accumulate.jl b/src/accumulate.jl
index 051ecc11e..d238bb8a2 100644
--- a/src/accumulate.jl
+++ b/src/accumulate.jl
@@ -22,9 +22,9 @@ function partial_scan(op::Function, output::AbstractArray{T}, input::AbstractArr
     temp = CuDynamicSharedArray(T, (2*threads,))
 
     # iterate the main dimension using threads and the first block dimension
-    i = (KI.get_group_id().x-1i32) * KI.get_local_size().x + KI.get_local_id().x
+    i = (KI.get_group_id().x - 1i32) * KI.get_local_size().x + KI.get_local_id().x
     # iterate the other dimensions using the remaining block dimensions
-    j = (KI.get_group_id().z-1i32) * KI.get_num_groups().y + KI.get_group_id().y
+    j = (KI.get_group_id().z - 1i32) * KI.get_num_groups().y + KI.get_group_id().y
 
     if j > length(Rother)
         return
@@ -105,9 +105,9 @@ function aggregate_partial_scan(op::Function, output::AbstractArray,
     block = KI.get_group_id().x
 
     # iterate the main dimension using threads and the first block dimension
-    i = (KI.get_group_id().x-1i32) * KI.get_local_size().x + KI.get_local_id().x
+    i = (KI.get_group_id().x - 1i32) * KI.get_local_size().x + KI.get_local_id().x
     # iterate the other dimensions using the remaining block dimensions
-    j = (KI.get_group_id().z-1i32) * KI.get_num_groups().y + KI.get_group_id().y
+    j = (KI.get_group_id().z - 1i32) * KI.get_num_groups().y + KI.get_group_id().y
 
     @inbounds if i <= length(Rdim) && j <= length(Rother)
         I = Rother[j]
diff --git a/src/device/random.jl b/src/device/random.jl
index 7d72d90a1..063c736ed 100644
--- a/src/device/random.jl
+++ b/src/device/random.jl
@@ -73,8 +73,8 @@ end
         @inbounds global_random_counters()[warpId]
     elseif field === :ctr2
         globalId = KI.get_global_id().x +
-                   (KI.get_global_id().y - 1i32) * KI.get_global_size().x +
-                   (KI.get_global_id().z - 1i32) * KI.get_global_size().x * KI.get_global_size().y
+            (KI.get_global_id().y - 1i32) * KI.get_global_size().x +
+            (KI.get_global_id().z - 1i32) * KI.get_global_size().x * KI.get_global_size().y
         globalId%UInt32
     end::UInt32
 end
diff --git a/src/mapreduce.jl b/src/mapreduce.jl
index 97a4176b4..6fccff91e 100644
--- a/src/mapreduce.jl
+++ b/src/mapreduce.jl
@@ -294,8 +294,9 @@ function GPUArrays.mapreducedim!(f::F, op::OP, R::AnyCuArray{T},
         end
 
         partial_kernel(f, op, init, Rreduce, Rother, Val(shuffle), partial, A;
-                    threads=partial_threads, blocks=partial_blocks, shmem=partial_shmem)
-                    # workgroupsize=partial_threads, numworkgroups=partial_blocks, shmem=partial_shmem)
+            threads = partial_threads, blocks = partial_blocks, shmem = partial_shmem
+        )
+        # workgroupsize=partial_threads, numworkgroups=partial_blocks, shmem=partial_shmem)
 
         GPUArrays.mapreducedim!(identity, op, R, partial; init)
     end
diff --git a/test/base/kernelabstractions.jl b/test/base/kernelabstractions.jl
index 2f2c4300b..1e674d3be 100644
--- a/test/base/kernelabstractions.jl
+++ b/test/base/kernelabstractions.jl
@@ -4,9 +4,14 @@ using SparseArrays
 
 include(joinpath(dirname(pathof(KernelAbstractions)), "..", "test", "testsuite.jl"))
 
-Testsuite.testsuite(()->CUDABackend(false, false), "CUDA", CUDA, CuArray, CuDeviceArray; skip_tests=Set([
-    "CPU synchronization",
-    "fallback test: callable types",]))
+Testsuite.testsuite(
+    () -> CUDABackend(false, false), "CUDA", CUDA, CuArray, CuDeviceArray; skip_tests = Set(
+        [
+            "CPU synchronization",
+            "fallback test: callable types",
+        ]
+    )
+)
 for (PreferBlocks, AlwaysInline) in Iterators.product((true, false), (true, false))
     Testsuite.unittest_testsuite(()->CUDABackend(PreferBlocks, AlwaysInline), "CUDA", CUDA, CuDeviceArray)
 end

[github-actions]

CUDA.jl Benchmarks

Benchmark suite	Current: 69871e5	Previous: f4c05e0	Ratio
array/reverse/1d	22693 ns	19857 ns	1.14
array/reverse/2dL_inplace	71436 ns	66720 ns	1.07
array/reverse/1dL	73269 ns	70068 ns	1.05
array/reverse/2d	25715 ns	21721 ns	1.18
array/reverse/1d_inplace	11652 ns	11535 ns	1.01
array/reverse/2d_inplace	15940 ns	13153 ns	1.21
array/reverse/2dL	91082 ns	73755 ns	1.23
array/reverse/1dL_inplace	67942 ns	66862 ns	1.02

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