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Merged
imreddyTeja merged 2 commits into from
May 4, 2026
+29 −46
Merged

Tr/cudaconvert #2491

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39fbb93
Use minimum time for latency benchmarks
imreddyTeja May 4, 2026
0934bff
Explicitly convert kernel args
imreddyTeja Apr 13, 2026
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47 changes: 15 additions & 32 deletions ext/cuda/data_layouts_copyto.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -52,14 +52,9 @@ if VERSION ≥ v"1.11.0-beta"
else
cartesian_indices_mask(us, mask)
end
args = (dest, bc, us, mask, cart_inds)
threads = threads_via_occupancy(knl_copyto!, args)
n_max_threads = min(threads, get_N(us))
p = if mask isa NoMask
linear_partition(prod(size(dest)), n_max_threads)
else
masked_partition(mask, n_max_threads, us)
end
args = cudaconvert((dest, bc, us, mask, cart_inds))
nitems = length(cart_inds)
p = config_via_occupancy(knl_copyto!, nitems, args)
auto_launch!(
knl_copyto!,
args;
Expand All @@ -81,10 +76,9 @@ else
bc′ = Base.Broadcast.instantiate(
DataLayouts.to_non_extruded_broadcasted(bc),
)
args = (dest, bc′, us)
threads = threads_via_occupancy(knl_copyto_linear!, args)
n_max_threads = min(threads, get_N(us))
p = linear_partition(prod(size(dest)), n_max_threads)
args = cudaconvert((dest, bc′, us))
nitems = prod(size(dest))
p = config_via_occupancy(knl_copyto_linear!, nitems, args)
auto_launch!(
knl_copyto_linear!,
args;
Expand All @@ -97,14 +91,9 @@ else
else
cartesian_indices_mask(us, mask)
end
args = (dest, bc, us, mask, cart_inds)
threads = threads_via_occupancy(knl_copyto!, args)
n_max_threads = min(threads, get_N(us))
p = if mask isa NoMask
linear_partition(prod(size(dest)), n_max_threads)
else
masked_partition(mask, n_max_threads, us)
end
args = cudaconvert((dest, bc, us, mask, cart_inds))
nitems = length(cart_inds)
p = config_via_occupancy(knl_copyto!, nitems, args)
auto_launch!(
knl_copyto!,
args;
Expand Down Expand Up @@ -189,10 +178,8 @@ function DataLayouts.copyto_per_field!(
# leverage linear indexing:
nitems = prod(size(array))
N = prod(size(array))
args = (array, bc′, N)
threads = threads_via_occupancy(copyto_per_field_kernel!, args)
n_max_threads = min(threads, nitems)
p = linear_partition(nitems, n_max_threads)
args = cudaconvert((array, bc′, N))
p = config_via_occupancy(copyto_per_field_kernel!, nitems, args)
auto_launch!(
copyto_per_field_kernel!,
args;
Expand Down Expand Up @@ -225,10 +212,8 @@ function DataLayouts.copyto_per_field_scalar!(
# leverage linear indexing:
nitems = prod(size(array))
N = prod(size(array))
args = (array, bc′, N)
threads = threads_via_occupancy(copyto_per_field_kernel_0D!, args)
n_max_threads = min(threads, nitems)
p = linear_partition(nitems, n_max_threads)
args = cudaconvert((array, bc′, N))
p = config_via_occupancy(copyto_per_field_kernel_0D!, nitems, args)
auto_launch!(
copyto_per_field_kernel_0D!,
args;
Expand All @@ -249,10 +234,8 @@ function DataLayouts.copyto_per_field_scalar!(
# leverage linear indexing:
nitems = prod(size(array))
N = prod(size(array))
args = (array, bc′, N)
threads = threads_via_occupancy(copyto_per_field_kernel_0D!, args)
n_max_threads = min(threads, nitems)
p = linear_partition(nitems, n_max_threads)
args = cudaconvert((array, bc′, N))
p = config_via_occupancy(copyto_per_field_kernel_0D!, nitems, args)
auto_launch!(
copyto_per_field_kernel_0D!,
args;
Expand Down
4 changes: 2 additions & 2 deletions ext/cuda/data_layouts_fill.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ function Base.fill!(dest::AbstractData, bc, to::ToCUDA, mask = NoMask())
if !(VERSION ≥ v"1.11.0-beta") &&
dest isa DataLayouts.EndsWithField &&
mask isa NoMask
args = (dest, bc, us)
args = cudaconvert((dest, bc, us))
threads = threads_via_occupancy(knl_fill_linear!, args)
n_max_threads = min(threads, get_N(us))
p = linear_partition(prod(size(dest)), n_max_threads)
Expand All @@ -42,7 +42,7 @@ function Base.fill!(dest::AbstractData, bc, to::ToCUDA, mask = NoMask())
else
cartesian_indices_mask(us, mask)
end
args = (dest, bc, us, mask, cart_inds)
args = cudaconvert((dest, bc, us, mask, cart_inds))
threads = threads_via_occupancy(knl_fill!, args)
n_max_threads = min(threads, get_N(us))
p = if mask isa NoMask
Expand Down
4 changes: 2 additions & 2 deletions ext/cuda/operators_finite_difference.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -111,15 +111,15 @@ function Base.copyto!(
cartesian_indices_mask(us, mask)
end

args = (
args = cudaconvert((
strip_space(out, space),
strip_space(bc′, space),
axes(out),
bounds,
us,
mask,
cart_inds,
)
))

threads = threads_via_occupancy(copyto_stencil_kernel!, args)
n_max_threads = min(threads, get_N(us))
Expand Down
20 changes: 10 additions & 10 deletions test/gpu/latency_benchmarks.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -27,25 +27,25 @@ import LazyBroadcast: lazy
scalar_field_2 = fill(1.0f0, space)
# basic expression
# intentionally benchmark without a sync between each trial
# CUDA.synchronize()
latency = median(@benchmark $scalar_field_1 .= $scalar_field_1 .+ $scalar_field_2).time
CUDA.synchronize()
latency = minimum(@benchmark $scalar_field_1 .= $scalar_field_1 .+ $scalar_field_2).time
# update this value if the kernel launch time changes significantly and it is expected
baseline_latency = 20500
@test latency ≈ baseline_latency atol = 4000
baseline_latency = 12000
@test latency ≈ baseline_latency atol = 2000
percent_change_latency =
round(Int, (latency - baseline_latency) / baseline_latency * 100)
@info "Latency: $latency ns, Percent change from baseline: $percent_change_latency%"

# repeated args expression
CUDA.synchronize()
latency =
median(
minimum(
@benchmark $scalar_field_1 .=
$scalar_field_1 .+ $scalar_field_2 .+ $scalar_field_1 .+ $scalar_field_2
).time
# update this value if the kernel launch time changes significantly and it is expected
baseline_latency = 22500
@test latency ≈ baseline_latency atol = 4000
baseline_latency = 13300
@test latency ≈ baseline_latency atol = 2000
percent_change_latency =
round(Int, (latency - baseline_latency) / baseline_latency * 100)
@info "Latency: $latency ns, Percent change from baseline: $percent_change_latency%"
Expand All @@ -55,10 +55,10 @@ import LazyBroadcast: lazy
lazy_sum_2 = @. lazy(lazy_sum_1 + lazy_sum_1)
lazy_sum_3 = @. lazy(lazy_sum_2 + lazy_sum_2)
CUDA.synchronize()
latency = median(@benchmark $scalar_field_1 .= $lazy_sum_3).time
latency = minimum(@benchmark $scalar_field_1 .= $lazy_sum_3).time
# update this value if the kernel launch time changes significantly and it is expected
baseline_latency = 29000
@test latency ≈ baseline_latency atol = 4000
baseline_latency = 16000
@test latency ≈ baseline_latency atol = 2000
percent_change_latency =
round(Int, (latency - baseline_latency) / baseline_latency * 100)
@info "Latency: $latency ns, Percent change from baseline: $percent_change_latency%"
Expand Down
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