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Merge pull request #1926 from CliMA/ck/offset_benchmark
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Add offset benchmark
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@charleskawczynski
charleskawczynski committed Aug 12, 2024
2 parents c64bb71 + 5b98d9d commit 7ab0acf
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10 changes: 10 additions & 0 deletions .buildkite/pipeline.yml
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Expand Up @@ -1242,6 +1242,16 @@ steps:
agents:
slurm_gpus: 1

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

- group: "Perf: Operators"
steps:

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309 changes: 309 additions & 0 deletions benchmarks/scripts/benchmark_offset.jl
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#=
julia --project=.buildkite
using Revise; include(joinpath("benchmarks", "scripts", "benchmark_offset.jl"))
# Info
- This benchmark demos the performance for different offset styles:
- Array of structs with Cartesian offsets
- Array of structs with Linear offsets
- Struct of arrays with no offsets
# Benchmark results:
Clima A100:
```
[ Info: ArrayType = CuArray
Problem size: (63, 4, 4, 1, 5400), float_type = Float32, device_bandwidth_GBs=2039
┌────────────────────────────────────────────────────────────────────┬──────────────────────────────────┬─────────┬─────────────┬────────────────┬────────┐
│ funcs │ time per call │ bw % │ achieved bw │ n-reads/writes │ n-reps │
├────────────────────────────────────────────────────────────────────┼──────────────────────────────────┼─────────┼─────────────┼────────────────┼────────┤
│ BO.aos_cart_offset!(X_aos_ref, Y_aos_ref, us; bm, nreps = 100) │ 68 microseconds, 834 nanoseconds │ 57.7908 │ 1178.35 │ 4 │ 100 │
│ BO.aos_lin_offset!(X_aos, Y_aos, us; bm, nreps = 100) │ 58 microseconds, 153 nanoseconds │ 68.4046 │ 1394.77 │ 4 │ 100 │
│ BO.soa_linear_index!(X_soa, Y_soa, us; bm, nreps = 100) │ 56 microseconds, 576 nanoseconds │ 70.3113 │ 1433.65 │ 4 │ 100 │
│ BO.soa_cart_index!(X_soa, Y_soa, us; bm, nreps = 100) │ 67 microseconds, 185 nanoseconds │ 59.2089 │ 1207.27 │ 4 │ 100 │
└────────────────────────────────────────────────────────────────────┴──────────────────────────────────┴─────────┴─────────────┴────────────────┴────────┘
[ Info: ArrayType = CuArray
Problem size: (63, 4, 4, 1, 5400), float_type = Float32, device_bandwidth_GBs=2039
┌────────────────────────────────────────────────────────────────────┬──────────────────────────────────┬─────────┬─────────────┬────────────────┬────────┐
│ funcs │ time per call │ bw % │ achieved bw │ n-reads/writes │ n-reps │
├────────────────────────────────────────────────────────────────────┼──────────────────────────────────┼─────────┼─────────────┼────────────────┼────────┤
│ BO.aos_cart_offset!(X_aos_ref, Y_aos_ref, us; bm, nreps = 100) │ 68 microseconds, 967 nanoseconds │ 57.6793 │ 1176.08 │ 4 │ 100 │
│ BO.aos_lin_offset!(X_aos, Y_aos, us; bm, nreps = 100) │ 58 microseconds, 82 nanoseconds │ 68.489 │ 1396.49 │ 4 │ 100 │
│ BO.soa_linear_index!(X_soa, Y_soa, us; bm, nreps = 100) │ 56 microseconds, 597 nanoseconds │ 70.2858 │ 1433.13 │ 4 │ 100 │
│ BO.soa_cart_index!(X_soa, Y_soa, us; bm, nreps = 100) │ 67 microseconds, 288 nanoseconds │ 59.1188 │ 1205.43 │ 4 │ 100 │
└────────────────────────────────────────────────────────────────────┴──────────────────────────────────┴─────────┴─────────────┴────────────────┴────────┘
```
=#

#! format: off
module BenchmarkOffset

include("benchmark_utils.jl")

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

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), 1, get_Nh(us)))
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, 1, 0))
CI3 = CI1 + CartesianIndex((0, 0, 0, 2, 0))
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; e, nreps, caller = @caller_name(@__FILE__),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), 1, get_Nh(us))
CI1 = CartesianIndices(map(x -> Base.OneTo(x), n))[I]
CI2 = CI1 + CartesianIndex((0, 0, 0, 1, 0))
CI3 = CI1 + CartesianIndex((0, 0, 0, 2, 0))
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)
CI = CartesianIndices((get_Nv(us), get_Nij(us), get_Nij(us), 1, get_Nh(us)))
LI1 = LinearIndices((get_Nv(us), get_Nij(us), get_Nij(us), 1, get_Nh(us)))
LI3 = LinearIndices((get_Nv(us), get_Nij(us), get_Nij(us), 3, get_Nh(us)))
CI1 = CI[I]
CI2 = CI1 + CartesianIndex((0, 0, 0, 1, 0))
CI3 = CI1 + CartesianIndex((0, 0, 0, 2, 0))
IY1 = LI1[CI1]
IX1 = LI3[CI1]
IX2 = LI3[CI2]
IX3 = LI3[CI3]
Y[IY1] = add3(X[IX1], X[IX2], X[IX3])
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; e, nreps, caller = @caller_name(@__FILE__),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)
CI = CartesianIndices((get_Nv(us), get_Nij(us), get_Nij(us), 1, get_Nh(us)))
LI1 = LinearIndices((get_Nv(us), get_Nij(us), get_Nij(us), 1, get_Nh(us)))
LI3 = LinearIndices((get_Nv(us), get_Nij(us), get_Nij(us), 3, get_Nh(us)))
CI1 = CI[I]
CI2 = CI1 + CartesianIndex((0, 0, 0, 1, 0))
CI3 = CI1 + CartesianIndex((0, 0, 0, 2, 0))
IY1 = LI1[CI1]
IX1 = LI3[CI1]
IX2 = LI3[CI2]
IX3 = LI3[CI3]
Y[IY1] = add3(X[IX1], X[IX2], X[IX3])
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; e, nreps, caller = @caller_name(@__FILE__),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; e, nreps, caller = @caller_name(@__FILE__),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 .BenchmarkOffset as BO

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 "Offset benchmark" begin
bm = BO.Benchmark(;problem_size=(63,4,4,1,5400), 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...))

FT = Float64;
s = (63,4,4,3,5400);
sY = (63,4,4,1,5400);
st = (63,4,4,5400);
ndofs = prod(st);
us = BO.UniversalSizesStatic(s[1], s[2], s[end]);

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"

BO.aos_cart_offset!(X_aos_ref, Y_aos_ref, us; n_trials = 1, nreps = 1)
BO.aos_lin_offset!(X_aos, Y_aos, us; n_trials = 1, nreps = 1)
BO.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

BO.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

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

BO.tabulate_benchmark(bm)
end

# #! format: on

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