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Add field-last benchmark script #1950

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Add field-last benchmark script #1950

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10 changes: 10 additions & 0 deletions .buildkite/pipeline.yml
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1294,6 +1294,16 @@ steps:
agents:
slurm_gpus: 1

- label: "Perf: benchmark scripts benchmark_field_last"
key: benchmark_field_last
command:
- "julia --project=.buildkite -e 'using CUDA; CUDA.versioninfo()'"
- "julia --color=yes --project=.buildkite benchmarks/scripts/benchmark_field_last.jl"
env:
CLIMACOMMS_DEVICE: "CUDA"
agents:
slurm_gpus: 1

- group: "Perf: Operators"
steps:

Expand Down
334 changes: 334 additions & 0 deletions benchmarks/scripts/benchmark_field_last.jl
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Original file line number Diff line number Diff line change
@@ -0,0 +1,334 @@
#=
julia --project=.buildkite
using Revise; include(joinpath("benchmarks", "scripts", "benchmark_field_last.jl"))

# Info

# Benchmark results:

Clima A100:
```
Kernel `add3(x1, x2, x3) = x1+x2+x3` and `n_reads_writes=4`:
[ Info: ArrayType = CuArray
Problem size: (63, 4, 4, 5400, 1), float_type = Float32, device_bandwidth_GBs=2039
┌─────────────────────────────────────────────────────────────────────┬──────────────────────────────────┬─────────┬─────────────┬────────────────┬────────┐
│ funcs │ time per call │ bw % │ achieved bw │ n-reads/writes │ n-reps │
├─────────────────────────────────────────────────────────────────────┼──────────────────────────────────┼─────────┼─────────────┼────────────────┼────────┤
│ FLD.aos_cart_offset!(X_aos_ref, Y_aos_ref, us; bm, nreps = 100) │ 72 microseconds, 899 nanoseconds │ 54.568 │ 1112.64 │ 4 │ 100 │
│ FLD.aos_lin_offset!(X_aos, Y_aos, us; bm, nreps = 100) │ 56 microseconds, 259 nanoseconds │ 70.708 │ 1441.74 │ 4 │ 100 │
│ FLD.soa_linear_index!(X_soa, Y_soa, us; bm, nreps = 100) │ 56 microseconds, 515 nanoseconds │ 70.3877 │ 1435.21 │ 4 │ 100 │
│ FLD.soa_cart_index!(X_soa, Y_soa, us; bm, nreps = 100) │ 67 microseconds, 462 nanoseconds │ 58.9663 │ 1202.32 │ 4 │ 100 │
└─────────────────────────────────────────────────────────────────────┴──────────────────────────────────┴─────────┴─────────────┴────────────────┴────────┘

Kernel `add3(x1, x2, x3) = x1+x2+x3` and `n_reads_writes=4`:
[ Info: ArrayType = CuArray
Problem size: (63, 4, 4, 5400, 1), float_type = Float64, device_bandwidth_GBs=2039
┌─────────────────────────────────────────────────────────────────────┬───────────────────────────────────┬─────────┬─────────────┬────────────────┬────────┐
│ funcs │ time per call │ bw % │ achieved bw │ n-reads/writes │ n-reps │
├─────────────────────────────────────────────────────────────────────┼───────────────────────────────────┼─────────┼─────────────┼────────────────┼────────┤
│ FLD.aos_cart_offset!(X_aos_ref, Y_aos_ref, us; bm, nreps = 100) │ 106 microseconds, 783 nanoseconds │ 74.5051 │ 1519.16 │ 4 │ 100 │
│ FLD.aos_lin_offset!(X_aos, Y_aos, us; bm, nreps = 100) │ 102 microseconds, 472 nanoseconds │ 77.6396 │ 1583.07 │ 4 │ 100 │
│ FLD.soa_linear_index!(X_soa, Y_soa, us; bm, nreps = 100) │ 102 microseconds, 523 nanoseconds │ 77.6008 │ 1582.28 │ 4 │ 100 │
│ FLD.soa_cart_index!(X_soa, Y_soa, us; bm, nreps = 100) │ 106 microseconds, 834 nanoseconds │ 74.4694 │ 1518.43 │ 4 │ 100 │
└─────────────────────────────────────────────────────────────────────┴───────────────────────────────────┴─────────┴─────────────┴────────────────┴────────┘

Kernel `add3(x1, x2, x3) = x1` and `n_reads_writes=2`:
[ Info: ArrayType = CuArray
Problem size: (63, 4, 4, 5400, 1), float_type = Float32, device_bandwidth_GBs=2039
┌─────────────────────────────────────────────────────────────────────┬──────────────────────────────────┬─────────┬─────────────┬────────────────┬────────┐
│ funcs │ time per call │ bw % │ achieved bw │ n-reads/writes │ n-reps │
├─────────────────────────────────────────────────────────────────────┼──────────────────────────────────┼─────────┼─────────────┼────────────────┼────────┤
│ FLD.aos_cart_offset!(X_aos_ref, Y_aos_ref, us; bm, nreps = 100) │ 61 microseconds, 185 nanoseconds │ 32.5079 │ 662.837 │ 2 │ 100 │
│ FLD.aos_lin_offset!(X_aos, Y_aos, us; bm, nreps = 100) │ 31 microseconds, 376 nanoseconds │ 63.3926 │ 1292.57 │ 2 │ 100 │
│ FLD.soa_linear_index!(X_soa, Y_soa, us; bm, nreps = 100) │ 31 microseconds, 120 nanoseconds │ 63.9141 │ 1303.21 │ 2 │ 100 │
│ FLD.soa_cart_index!(X_soa, Y_soa, us; bm, nreps = 100) │ 44 microseconds, 53 nanoseconds │ 45.1499 │ 920.607 │ 2 │ 100 │
└─────────────────────────────────────────────────────────────────────┴──────────────────────────────────┴─────────┴─────────────┴────────────────┴────────┘
```

# CPU (Mac M1)
```
[ Info: ArrayType = identity
Problem size: (63, 4, 4, 5400, 1), float_type = Float32, device_bandwidth_GBs=2039
┌─────────────────────────────────────────────────────────────────────┬───────────────────────────────────┬──────────┬─────────────┬────────────────┬────────┐
│ funcs │ time per call (CPU) │ bw % │ achieved bw │ n-reads/writes │ n-reps │
├─────────────────────────────────────────────────────────────────────┼───────────────────────────────────┼──────────┼─────────────┼────────────────┼────────┤
│ FLD.aos_cart_offset!(X_aos_ref, Y_aos_ref, us; bm, nreps = 100) │ 16 milliseconds, 494 microseconds │ 0.241171 │ 4.91747 │ 4 │ 100 │
│ FLD.aos_lin_offset!(X_aos, Y_aos, us; bm, nreps = 100) │ 783 microseconds, 256 nanoseconds │ 5.07871 │ 103.555 │ 4 │ 100 │
│ FLD.soa_linear_index!(X_soa, Y_soa, us; bm, nreps = 100) │ 790 microseconds, 894 nanoseconds │ 5.02966 │ 102.555 │ 4 │ 100 │
│ FLD.soa_cart_index!(X_soa, Y_soa, us; bm, nreps = 100) │ 12 milliseconds, 522 microseconds │ 0.317663 │ 6.47714 │ 4 │ 100 │
└─────────────────────────────────────────────────────────────────────┴───────────────────────────────────┴──────────┴─────────────┴────────────────┴────────┘
```

=#

#! format: off
module BenchmarkFieldLastIndex

using CUDA
include("benchmark_utils.jl")

@inline function const_linear_index(us::UniversalSizesStatic, I, field_index)
n = (get_Nv(us), get_Nij(us), get_Nij(us), get_Nh(us), 1)
i = I + prod(n)*field_index
return i
end

@inline function const_linear_index_reference(us::UniversalSizesStatic, I, field_index)
CI = CartesianIndices((get_Nv(us), get_Nij(us), get_Nij(us), get_Nh(us), 1))
LI = LinearIndices((get_Nv(us), get_Nij(us), get_Nij(us), get_Nh(us), field_index+1))
return LI[CI[I] + CartesianIndex((0, 0, 0, 0, field_index))]
end

# add3(x1, x2, x3) = x1 + x2 + x3
add3(x1, x2, x3) = x1

function aos_cart_offset!(X, Y, us; nreps = 1, bm=nothing, n_trials = 30)
if Y isa Array
e = Inf
CI = CartesianIndices((get_Nv(us), get_Nij(us), get_Nij(us), get_Nh(us), 1))
for t in 1:n_trials
et = Base.@elapsed begin
for i in 1:nreps
@inbounds @simd for I in 1:get_N(us)
CI1 = CI[I]
CI2 = CI1 + CartesianIndex((0, 0, 0, 0, 1))
CI3 = CI1 + CartesianIndex((0, 0, 0, 0, 2))
Y[CI1] = add3(X[CI1], X[CI2], X[CI3])
end
end
end
e = min(e, et)
end
else
e = Inf
kernel = CUDA.@cuda always_inline = true launch = false aos_cart_offset_kernel!(X,Y,us)
config = CUDA.launch_configuration(kernel.fun)
threads = min(get_N(us), config.threads)
blocks = cld(get_N(us), threads)
for t in 1:n_trials
et = CUDA.@elapsed begin
for i in 1:nreps # reduce variance / impact of launch latency
kernel(X,Y,us; threads, blocks)
end
end
e = min(e, et)
end
end
push_info(bm; kernel_time_s=e/nreps, nreps, caller = @caller_name(@__FILE__), problem_size = size(us), n_reads_writes=4)
return nothing
end;
function aos_cart_offset_kernel!(X, Y, us)
@inbounds begin
I = (CUDA.blockIdx().x - Int32(1)) * CUDA.blockDim().x + CUDA.threadIdx().x
if I ≤ get_N(us)
n = (get_Nv(us), get_Nij(us), get_Nij(us), get_Nh(us), 1)
CI1 = CartesianIndices(map(x -> Base.OneTo(x), n))[I]
CI2 = CI1 + CartesianIndex((0, 0, 0, 0, 1))
CI3 = CI1 + CartesianIndex((0, 0, 0, 0, 2))
Y[CI1] = add3(X[CI1], X[CI2], X[CI3])
end
end
return nothing
end;

function aos_lin_offset!(X, Y, us; nreps = 1, bm=nothing, n_trials = 30)
if Y isa Array
e = Inf
for t in 1:n_trials
et = Base.@elapsed begin
for i in 1:nreps
@inbounds @simd for I in 1:get_N(us)
LY1 = const_linear_index(us, I, 0)
LX1 = const_linear_index(us, I, 0)
LX2 = const_linear_index(us, I, 1)
LX3 = const_linear_index(us, I, 2)
Y[LY1] = add3(X[LX1], X[LX2], X[LX3])
end
end
end
e = min(e, et)
end
else
e = Inf
kernel = CUDA.@cuda always_inline = true launch = false aos_lin_offset_kernel!(X,Y,us)
config = CUDA.launch_configuration(kernel.fun)
threads = min(get_N(us), config.threads)
blocks = cld(get_N(us), threads)
for t in 1:n_trials
et = CUDA.@elapsed begin
for i in 1:nreps
kernel(X,Y,us; threads, blocks)
end
end
e = min(e, et)
end
end
push_info(bm; kernel_time_s=e/nreps, nreps, caller = @caller_name(@__FILE__), problem_size = size(us), n_reads_writes=4)
return nothing
end;
function aos_lin_offset_kernel!(X, Y, us)
@inbounds begin
I = (CUDA.blockIdx().x - Int32(1)) * CUDA.blockDim().x + CUDA.threadIdx().x
if I ≤ get_N(us)
LY1 = const_linear_index(us, I, 0)
LX1 = const_linear_index(us, I, 0)
LX2 = const_linear_index(us, I, 1)
LX3 = const_linear_index(us, I, 2)
Y[LY1] = add3(X[LX1], X[LX2], X[LX3])
end
end
return nothing
end;

function soa_cart_index!(X, Y, us; nreps = 1, bm=nothing, n_trials = 30)
e = Inf
if first(Y) isa Array
CI = CartesianIndices((get_Nv(us), get_Nij(us), get_Nij(us), get_Nh(us)))
for t in 1:n_trials
et = Base.@elapsed begin
for i in 1:nreps
(y1,) = Y
(x1, x2, x3) = X
@inbounds @simd for I in 1:get_N(us)
y1[CI[I]] = add3(x1[CI[I]], x2[CI[I]], x3[CI[I]])
end
end
end
e = min(e, et)
end
else
kernel = CUDA.@cuda always_inline = true launch = false soa_cart_index_kernel!(X,Y,us)
config = CUDA.launch_configuration(kernel.fun)
threads = min(get_N(us), config.threads)
blocks = cld(get_N(us), threads)
for t in 1:n_trials
et = CUDA.@elapsed begin
for i in 1:nreps # reduce variance / impact of launch latency
kernel(X,Y,us; threads, blocks)
end
end
e = min(e, et)
end
end
push_info(bm; kernel_time_s=e/nreps, nreps, caller = @caller_name(@__FILE__), problem_size = size(us), n_reads_writes=4)
return nothing
end;
function soa_cart_index_kernel!(X, Y, us)
@inbounds begin
I = (CUDA.blockIdx().x - Int32(1)) * CUDA.blockDim().x + CUDA.threadIdx().x
if I ≤ get_N(us)
CI = CartesianIndices((get_Nv(us), get_Nij(us), get_Nij(us), get_Nh(us)))
(y1,) = Y
(x1, x2, x3) = X
y1[CI[I]] = add3(x1[CI[I]], x2[CI[I]], x3[CI[I]])
end
end
return nothing
end;

function soa_linear_index!(X, Y, us; nreps = 1, bm=nothing, n_trials = 30)
e = Inf
if first(Y) isa Array
for t in 1:n_trials
et = Base.@elapsed begin
for i in 1:nreps
(y1,) = Y
(x1, x2, x3) = X
@inbounds @simd for I in 1:get_N(us)
y1[I] = add3(x1[I], x2[I], x3[I])
end
end
end
e = min(e, et)
end
else
kernel = CUDA.@cuda always_inline = true launch = false soa_linear_index_kernel!(X,Y,us)
config = CUDA.launch_configuration(kernel.fun)
threads = min(get_N(us), config.threads)
blocks = cld(get_N(us), threads)
for t in 1:n_trials
et = CUDA.@elapsed begin
for i in 1:nreps # reduce variance / impact of launch latency
kernel(X,Y,us; threads, blocks)
end
end
e = min(e, et)
end
end
push_info(bm; kernel_time_s=e/nreps, nreps, caller = @caller_name(@__FILE__), problem_size = size(us), n_reads_writes=4)
return nothing
end;
function soa_linear_index_kernel!(X, Y, us)
@inbounds begin
I = (CUDA.blockIdx().x - Int32(1)) * CUDA.blockDim().x + CUDA.threadIdx().x
if I ≤ get_N(us)
(y1,) = Y
(x1, x2, x3) = X
y1[I] = add3(x1[I], x2[I], x3[I])
end
end
return nothing
end;

end # module

import .BenchmarkFieldLastIndex as FLD

function fill_with_rand!(arr)
FT = eltype(arr)
T = typeof(arr)
s = size(arr)
arr .= T(rand(FT, s))
end

using CUDA
using Test
@testset "Field last dim benchmark" begin
bm = FLD.Benchmark(;problem_size=(63,4,4,5400,1), float_type=Float32) # size(problem_size, 4) == 1 to avoid double counting reads/writes
ArrayType = CUDA.CuArray;
# ArrayType = Base.identity;
arr(float_type, problem_size, T) = T(zeros(float_type, problem_size...))

s = (63,4,4,5400,3);
sY = (63,4,4,5400,1);
st = (63,4,4,5400);
ndofs = prod(st);
us = FLD.UniversalSizesStatic(s[1], s[2], s[end-1]);

X_aos = arr(bm.float_type, s, ArrayType);
Y_aos = arr(bm.float_type, sY, ArrayType);
X_aos_ref = arr(bm.float_type, s, ArrayType);
Y_aos_ref = arr(bm.float_type, sY, ArrayType);
X_soa = ntuple(_ -> arr(bm.float_type, st, ArrayType), 3);
Y_soa = ntuple(_ -> arr(bm.float_type, st, ArrayType), 1);
fill_with_rand!(X_aos)
fill_with_rand!(Y_aos)
X_aos_ref .= X_aos
Y_aos_ref .= Y_aos
for i in 1:3; X_soa[i] .= X_aos[:,:,:,:, i]; end
for i in 1:1; Y_soa[i] .= Y_aos[:,:,:,:, i]; end
@info "ArrayType = $ArrayType"

FLD.aos_cart_offset!(X_aos_ref, Y_aos_ref, us; n_trials = 1, nreps = 1)
FLD.aos_lin_offset!(X_aos, Y_aos, us; n_trials = 1, nreps = 1)
FLD.soa_linear_index!(X_soa, Y_soa, us; n_trials = 1, nreps = 1)

@test all(X_aos .== X_aos_ref)
@test all(Y_aos .== Y_aos_ref)
for i in 1:3; @test all(X_soa[i] .== X_aos_ref[:,:,:,:,i]); end
for i in 1:1; @test all(Y_soa[i] .== Y_aos_ref[:,:,:,:,i]); end

FLD.soa_cart_index!(X_soa, Y_soa, us; n_trials = 1, nreps = 1)

for i in 1:3; @test all(X_soa[i] .== X_aos_ref[:,:,:,:,i]); end
for i in 1:1; @test all(Y_soa[i] .== Y_aos_ref[:,:,:,:,i]); end

FLD.aos_cart_offset!(X_aos_ref, Y_aos_ref, us; bm, nreps = 100)
FLD.aos_lin_offset!(X_aos, Y_aos, us; bm, nreps = 100)
FLD.soa_linear_index!(X_soa, Y_soa, us; bm, nreps = 100)
FLD.soa_cart_index!(X_soa, Y_soa, us; bm, nreps = 100)

FLD.tabulate_benchmark(bm)
end

# #! format: on
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