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christiangnrdchristiangnrd
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Copy-paste CUDA benchmarks
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‎perf/.gitignore‎

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results.json
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reference.json

‎perf/Project.toml‎

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[deps]
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BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
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HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
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JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
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StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
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StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"

‎perf/array.jl‎

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group = addgroup!(SUITE, "array")
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const m = 512
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const n = 1000
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# generate some arrays
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cpu_mat = rand(rng, Float32, m, n)
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gpu_mat = CuArray{Float32}(undef, size(cpu_mat))
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gpu_vec = reshape(gpu_mat, length(gpu_mat))
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gpu_arr_3d = reshape(gpu_mat, (m, 40, 25))
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gpu_arr_4d = reshape(gpu_mat, (m, 10, 10, 10))
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gpu_mat_ints = CuArray(rand(rng, Int, m, n))
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gpu_vec_ints = reshape(gpu_mat_ints, length(gpu_mat_ints))
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gpu_mat_bools = CuArray(rand(rng, Bool, m, n))
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gpu_vec_bools = reshape(gpu_mat_bools, length(gpu_mat_bools))
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group["construct"] = @benchmarkable CuArray{Int}(undef, 1)
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group["copy"] = @async_benchmarkable copy($gpu_mat)
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gpu_mat2 = copy(gpu_mat)
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let group = addgroup!(group, "copyto!")
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group["cpu_to_gpu"] = @async_benchmarkable copyto!($gpu_mat, $cpu_mat)
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group["gpu_to_cpu"] = @async_benchmarkable copyto!($cpu_mat, $gpu_mat)
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group["gpu_to_gpu"] = @async_benchmarkable copyto!($gpu_mat2, $gpu_mat)
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end
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let group = addgroup!(group, "iteration")
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group["scalar"] = @benchmarkable CUDA.@allowscalar [$gpu_vec[i] for i in 1:10]
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group["logical"] = @benchmarkable $gpu_vec[$gpu_vec_bools]
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let group = addgroup!(group, "findall")
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group["bool"] = @benchmarkable findall($gpu_vec_bools)
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group["int"] = @benchmarkable findall(isodd, $gpu_vec_ints)
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end
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let group = addgroup!(group, "findfirst")
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group["bool"] = @benchmarkable findfirst($gpu_vec_bools)
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group["int"] = @benchmarkable findfirst(isodd, $gpu_vec_ints)
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end
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let group = addgroup!(group, "findmin") # findmax
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group["1d"] = @async_benchmarkable findmin($gpu_vec)
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group["2d"] = @async_benchmarkable findmin($gpu_mat; dims=1)
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end
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end
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let group = addgroup!(group, "reverse")
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group["1d"] = @async_benchmarkable reverse($gpu_vec)
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group["2d"] = @async_benchmarkable reverse($gpu_mat; dims=1)
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group["1d_inplace"] = @async_benchmarkable reverse!($gpu_vec)
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group["2d_inplace"] = @async_benchmarkable reverse!($gpu_mat; dims=1)
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end
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group["broadcast"] = @async_benchmarkable $gpu_mat .= 0f0
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# no need to test inplace version, which performs the same operation (but with an alloc)
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let group = addgroup!(group, "accumulate")
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group["1d"] = @async_benchmarkable accumulate(+, $gpu_vec)
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group["2d"] = @async_benchmarkable accumulate(+, $gpu_mat; dims=1)
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end
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let group = addgroup!(group, "reductions")
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let group = addgroup!(group, "reduce")
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group["1d"] = @async_benchmarkable reduce(+, $gpu_vec)
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group["2d"] = @async_benchmarkable reduce(+, $gpu_mat; dims=1)
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end
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let group = addgroup!(group, "mapreduce")
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group["1d"] = @async_benchmarkable mapreduce(x->x+1, +, $gpu_vec)
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group["2d"] = @async_benchmarkable mapreduce(x->x+1, +, $gpu_mat; dims=1)
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end
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# used by sum, prod, minimum, maximum, all, any, count
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end
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let group = addgroup!(group, "random")
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let group = addgroup!(group, "rand")
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group["Float32"] = @async_benchmarkable CUDA.rand(Float32, m*n)
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group["Int64"] = @async_benchmarkable CUDA.rand(Int64, m*n)
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end
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let group = addgroup!(group, "rand!")
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group["Float32"] = @async_benchmarkable CUDA.rand!($gpu_vec)
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group["Int64"] = @async_benchmarkable CUDA.rand!($gpu_vec_ints)
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end
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let group = addgroup!(group, "randn")
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group["Float32"] = @async_benchmarkable CUDA.randn(Float32, m*n)
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#group["Int64"] = @async_benchmarkable CUDA.randn(Int64, m*n)
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end
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let group = addgroup!(group, "randn!")
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group["Float32"] = @async_benchmarkable CUDA.randn!($gpu_vec)
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#group["Int64"] = @async_benchmarkable CUDA.randn!($gpu_vec_ints)
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end
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end
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let group = addgroup!(group, "sorting")
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group["1d"] = @async_benchmarkable sort($gpu_vec)
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group["2d"] = @async_benchmarkable sort($gpu_mat; dims=1)
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group["by"] = @async_benchmarkable sort($gpu_vec; by=sin)
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end
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let group = addgroup!(group, "permutedims")
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group["2d"] = @async_benchmarkable permutedims($gpu_mat, (2,1))
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group["3d"] = @async_benchmarkable permutedims($gpu_arr_3d, (3,1,2))
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group["4d"] = @async_benchmarkable permutedims($gpu_arr_4d, (2,1,4,3))
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end

‎perf/byval.jl‎

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module ByVal
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using CUDA, BenchmarkTools, Random
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using CUDA: i32
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const threads = 256
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# simple add matrixes kernel
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function kernel_add_mat(n, x1, x2, y)
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i = (blockIdx().x-1i32) * blockDim().x + threadIdx().x
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if i <= n
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@inbounds y[i] = x1[i] + x2[i]
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end
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return
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end
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@inline get_inputs3(indx_y, a, b, c) = (a, b, c)
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@inline get_inputs3(indx_y, a1, a2, b1, b2, c1, c2) = indx_y == 1 ? (a1, b1, c1) : (a2, b2, c2)
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@inline get_inputs3(indx_y, a1, a2, a3, b1, b2, b3, c1, c2, c3) = indx_y == 1 ? (a1, b1, c1) : indx_y == 2 ? (a2, b2, c2) : (a3, b3, c3)
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# add arrays of matrixes kernel
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function kernel_add_mat_z_slices(n, vararg...)
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x1, x2, y = get_inputs3(blockIdx().y, vararg...)
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i = (blockIdx().x-1i32) * blockDim().x + threadIdx().x
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if i <= n
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@inbounds y[i] = x1[i] + x2[i]
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end
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return
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end
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function add_z_slices!(y, x1, x2)
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m1, n1 = size(x1[1]) #get size of first slice
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blocks = (m1 * n1 + threads - 1) ÷ threads
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# get length(x1) more blocks than needed to process 1 slice
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@cuda blocks = blocks, length(x1) threads = threads kernel_add_mat_z_slices(m1 * n1, x1..., x2..., y...)
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end
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function add!(y, x1, x2)
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m1, n1 = size(x1)
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blocks = (m1 * n1 + threads - 1) ÷ threads
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@cuda blocks = blocks, 1 threads = threads kernel_add_mat(m1 * n1, x1, x2, y)
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end
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function main()
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results = BenchmarkGroup()
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num_z_slices = 3
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Random.seed!(1)
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#m, n = 7, 5 # tiny to measure overhead
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#m, n = 521, 111
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#m, n = 1521, 1111
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#m, n = 3001, 1511 # prime numbers to test memory access correctness
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m, n = 3072, 1536 # 256 multiplier
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#m, n = 6007, 3001 # prime numbers to test memory access correctness
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x1 = [cu(randn(Float32, (m, n)) .+ Float32(0.5)) for i = 1:num_z_slices]
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x2 = [cu(randn(Float32, (m, n)) .+ Float32(0.5)) for i = 1:num_z_slices]
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y1 = [similar(x1[1]) for i = 1:num_z_slices]
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# reference down to bones add on GPU
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results["reference"] = @benchmark CUDA.@sync blocking=true add!($y1[1], $x1[1], $x2[1])
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# adding arrays in an array
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for slices = 1:num_z_slices
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results["slices=$slices"] = @benchmark CUDA.@sync blocking=true add_z_slices!($y1[1:$slices], $x1[1:$slices], $x2[1:$slices])
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end
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# BenchmarkTools captures inputs, JuliaCI/BenchmarkTools.jl#127, so forcibly free them
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CUDA.unsafe_free!.(x1)
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CUDA.unsafe_free!.(x2)
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CUDA.unsafe_free!.(y1)
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return results
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end
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end
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ByVal.main()

‎perf/kernel.jl‎

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using CUDA: i32
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group = addgroup!(SUITE, "kernel")
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group["launch"] = @benchmarkable @cuda identity(nothing)
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group["occupancy"] = @benchmarkable begin
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kernel = @cuda launch=false identity(nothing)
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launch_configuration(kernel.fun)
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end
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src = CUDA.rand(Float32, 512, 1000)
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dest = similar(src)
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function indexing_kernel(dest, src)
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i = (blockIdx().x-1i32) * blockDim().x + threadIdx().x
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@inbounds dest[i] = src[i]
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return
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end
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group["indexing"] = @async_benchmarkable @cuda threads=size(src,1) blocks=size(src,2) $indexing_kernel($dest, $src)
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function checked_indexing_kernel(dest, src)
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i = (blockIdx().x-1i32) * blockDim().x + threadIdx().x
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dest[i] = src[i]
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return
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end
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group["indexing_checked"] = @async_benchmarkable @cuda threads=size(src,1) blocks=size(src,2) $checked_indexing_kernel($dest, $src)
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function rand_kernel(dest::AbstractArray{T}) where {T}
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i = (blockIdx().x-1i32) * blockDim().x + threadIdx().x
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dest[i] = rand(T)
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return
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end
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group["rand"] = @async_benchmarkable @cuda threads=size(src,1) blocks=size(src,2) $rand_kernel($dest)

‎perf/latency.jl‎

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module Latency
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using CUDA
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using BenchmarkTools
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function main()
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results = BenchmarkGroup()
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base_cmd = Base.julia_cmd()
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if Base.JLOptions().project != C_NULL
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base_cmd = `$base_cmd --project=$(unsafe_string(Base.JLOptions().project))`
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end
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# NOTE: we don't ust Base.active_project() here because of how CI launches this script,
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# starting with --project in the main CUDA.jl project.
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# time to precompile the package and its dependencies
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precompile_cmd =
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`$base_cmd -e "pkg = Base.identify_package(\"CUDA\")
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Base.compilecache(pkg)"`
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results["precompile"] = @benchmark run($precompile_cmd) evals=1 seconds=60
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# time to actually import the package
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import_cmd =
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`$base_cmd -e "using CUDA"`
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results["import"] = @benchmark run($import_cmd) evals=1 seconds=30
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# time to actually compile a kernel
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ttfp_cmd =
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`$base_cmd -e "using CUDA
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kernel() = return
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CUDA.code_ptx(devnull, kernel, Tuple{}; kernel=true)"`
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results["ttfp"] = @benchmark run($ttfp_cmd) evals=1 seconds=60
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results
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end
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end
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Latency.main()

‎perf/metal.jl‎

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group = addgroup!(SUITE, "cuda")
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let group = addgroup!(group, "synchronization")
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let group = addgroup!(group, "stream")
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group["blocking"] = @benchmarkable synchronize(blocking=true)
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group["auto"] = @benchmarkable synchronize()
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group["nonblocking"] = @benchmarkable synchronize(spin=false)
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end
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let group = addgroup!(group, "context")
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group["blocking"] = @benchmarkable device_synchronize(blocking=true)
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group["auto"] = @benchmarkable device_synchronize()
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group["nonblocking"] = @benchmarkable device_synchronize(spin=false)
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end
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end

‎perf/metaldevrt.jl‎

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module cudadevrt
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using CUDA, BenchmarkTools, Random
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const threads = 256
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#simple add matrix and vector kernel
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function kernel_add_mat_vec(m, x1, x2, y)
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# one block per column
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offset = (blockIdx().x-1) * m
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@inbounds xtmp = x2[blockIdx().x]
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for i = threadIdx().x : blockDim().x : m
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@inbounds y[offset + i] = x1[offset + i] + xtmp
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end
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return
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end
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function add!(y, x1, x2)
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m, n = size(x1)
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@cuda blocks = n, 1 threads = threads kernel_add_mat_vec(m, x1, x2, y)
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end
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function main()
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Random.seed!(1)
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m, n = 3072, 1536 # 256 multiplier
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x1 = cu(randn(Float32, (m, n)) .+ Float32(0.5))
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x2 = cu(randn(Float32, (1, n)) .+ Float32(0.5))
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y1 = similar(x1)
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results = @benchmark CUDA.@sync blocking=true add!($y1, $x1, $x2)
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# BenchmarkTools captures inputs, JuliaCI/BenchmarkTools.jl#127, so forcibly free them
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CUDA.unsafe_free!(x1)
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CUDA.unsafe_free!(x2)
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CUDA.unsafe_free!(y1)
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return results
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end
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end
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cudadevrt.main()
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