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CUDA early exit on empty grid #1729

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merged 9 commits into from
Aug 17, 2022
Merged

CUDA early exit on empty grid #1729

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aafed48
early exit instead of launching cuda kernel on empty grid
petiaccja Aug 12, 2022
923fd89
moved domain size check one level up
petiaccja Aug 15, 2022
140f6d2
replaced check on block with check on sizes argument, moved check to top
petiaccja Aug 15, 2022
db5dd93
messed up the predicate
petiaccja Aug 15, 2022
670777e
early return
petiaccja Aug 16, 2022
f9750b7
moved empty domain tests to regression
petiaccja Aug 16, 2022
30ef04b
forgot this one include
petiaccja Aug 16, 2022
5c9e6e5
forgot cassert
petiaccja Aug 16, 2022
be5679a
Merge remote-tracking branch 'origin/master' into fix-empty-domain
petiaccja Aug 17, 2022
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1 change: 1 addition & 0 deletions include/gridtools/common/cuda_util.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@
*/
#pragma once

#include <cassert>
#include <memory>
#include <sstream>
#include <stdexcept>
Expand Down
58 changes: 32 additions & 26 deletions include/gridtools/fn/backend/gpu.hpp
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Expand Up @@ -149,19 +149,22 @@ namespace gridtools::fn::backend {
auto strides = sid::get_strides(std::forward<Composite>(composite));

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Maybe something like the following (as the first thing in the function) would be more readable. What do you think?

Suggested change
if empty_domain(sizes)
return;

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@havogt havogt Aug 15, 2022 •
edited
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and maybe even better to move it outside of gpu.hpp to the common part where we are dispatching to the backends (but didn't check where that is and if it really makes sense).

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I created an is_domain_empty function. I usually use an if block instead of an early return when the code is simple, but I'm happy with either.

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I prefer Hannes early return in this case as I my eyes don’t have to track any braces opening and closing. And my proposed changes would be even a bit more minimalistic, just before the kernel launch:

if (blocks.x ==0 || blocks.y == 0 || blocks.z == 0)
    return;

(of course this does not include the optimization of skipping unnecessary computation of ptr_holder, strides, etc., but I don’t think we need any optimization of such trivial computations for an empty domain)

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Well, uhh, it was with the blocks before Hannes suggested to make it a function :) Now that I wrote the empty domain function, I'd rather keep it, because that satisfies the requirements independent of how you calculate the blocks.

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I'll also put an early return

auto [blocks, threads] = blocks_and_threads<BlockSizes>(sizes);
cuda_util::launch(blocks,
threads,
0,
g.stream,
kernel<BlockSizes,
Sizes,
decltype(ptr_holder),
decltype(strides),
stencil_fun_f<StencilStage, MakeIterator>>,
sizes,
ptr_holder,
strides,
stencil_fun_f<StencilStage, MakeIterator>{std::move(make_iterator)});
if (blocks.x > 0 && blocks.y > 0 && blocks.z > 0) {
assert(threads.x > 0 && threads.y > 0 && threads.z > 0);
cuda_util::launch(blocks,
threads,
0,
g.stream,
kernel<BlockSizes,
Sizes,
decltype(ptr_holder),
decltype(strides),
stencil_fun_f<StencilStage, MakeIterator>>,
sizes,
ptr_holder,
strides,
stencil_fun_f<StencilStage, MakeIterator>{std::move(make_iterator)});
}
}

template <class ColumnStage, class MakeIterator, class Seed>
Expand Down Expand Up @@ -196,19 +199,22 @@ namespace gridtools::fn::backend {
int v_size = at_key<Vertical>(sizes);

auto [blocks, threads] = blocks_and_threads<BlockSizes>(h_sizes);
cuda_util::launch(blocks,
threads,
0,
g.stream,
kernel<BlockSizes,
decltype(h_sizes),
decltype(ptr_holder),
decltype(strides),
column_fun_f<ColumnStage, MakeIterator, Seed>>,
h_sizes,
ptr_holder,
strides,
column_fun_f<ColumnStage, MakeIterator, Seed>{std::move(make_iterator), std::move(seed), v_size});
if (blocks.x > 0 && blocks.y > 0 && blocks.z > 0) {
assert(threads.x > 0 && threads.y > 0 && threads.z > 0);
cuda_util::launch(blocks,
threads,
0,
g.stream,
kernel<BlockSizes,
decltype(h_sizes),
decltype(ptr_holder),
decltype(strides),
column_fun_f<ColumnStage, MakeIterator, Seed>>,
h_sizes,
ptr_holder,
strides,
column_fun_f<ColumnStage, MakeIterator, Seed>{std::move(make_iterator), std::move(seed), v_size});
}
}

template <class BlockSizes>
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60 changes: 60 additions & 0 deletions tests/unit_tests/fn/test_fn_backend_gpu.cu
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Expand Up @@ -12,6 +12,7 @@
#include <gtest/gtest.h>
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I would prefer having this test as a regression test, running for all available backends. You could even just put a new variant of the copy stencil test into tests/regression/fn/fn_copy.cpp I guess. There are already versions with and without domain offsets, so the one with zero domain size would actually fit well.

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…or add a separate one as you also check the scan. I don’t care much, but enabling it for all backends makes sense for sure.

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I made a separate regression test (and removed the unit test).


#include <gridtools/fn/column_stage.hpp>
#include <gridtools/fn/unstructured.hpp>
#include <gridtools/sid/composite.hpp>
#include <gridtools/sid/synthetic.hpp>

Expand Down Expand Up @@ -207,5 +208,64 @@ namespace gridtools::fn::backend {
bool success = on_device::exec(global_tmp_check_fun(), ptr_holder, strides);
EXPECT_TRUE(success);
}

struct empty_stencil {
GT_FUNCTION constexpr auto operator()() const {
return []() { return 0.0f; };
}
};

TEST(backend_gpu, empty_domain_stencil) {
constexpr size_t nvertices = 0;
constexpr size_t nlevels = 0;

using block_sizes_t = meta::list<meta::list<unstructured::dim::horizontal, int_t<32>>,
meta::list<unstructured::dim::vertical, int_t<1>>>;

auto out = cuda_util::cuda_malloc<int>(nvertices * nlevels);
auto as_synthetic = [](int *x) {
return sid::synthetic()
.set<property::origin>(sid::host_device::simple_ptr_holder(x))
.set<property::strides>(
hymap::keys<unstructured::dim::horizontal, unstructured::dim::vertical>::make_values(5_c, 1_c));
};
auto out_s = as_synthetic(out.get());

auto domain = unstructured_domain(tuple{nvertices, nlevels}, tuple{0, 0});
auto backend = make_backend(backend::gpu<block_sizes_t>(), domain);
backend.stencil_executor()().arg(out_s).assign(0_c, empty_stencil{}).execute();
}

struct empty_column : fwd {
static GT_FUNCTION constexpr auto prologue() {
return tuple(scan_pass([](auto acc) { return acc; }, host_device::identity()));
}

static GT_FUNCTION constexpr auto body() {
return scan_pass([](auto acc) { return acc; }, host_device::identity());
}
};

TEST(backend_gpu, empty_domain_column) {
constexpr size_t nvertices = 0;
constexpr size_t nlevels = 0;

using block_sizes_t = meta::list<meta::list<unstructured::dim::horizontal, int_t<32>>,
meta::list<unstructured::dim::vertical, int_t<1>>>;

auto out = cuda_util::cuda_malloc<int>(nvertices * nlevels);
auto as_synthetic = [](int *x) {
return sid::synthetic()
.set<property::origin>(sid::host_device::simple_ptr_holder(x))
.set<property::strides>(
hymap::keys<unstructured::dim::horizontal, unstructured::dim::vertical>::make_values(5_c, 1_c));
};
auto out_s = as_synthetic(out.get());

auto domain = unstructured_domain(tuple{nvertices, nlevels}, tuple{0, 0});
auto backend = make_backend(backend::gpu<block_sizes_t>(), domain);
backend.vertical_executor()().arg(out_s).assign(0_c, empty_column{}, 0.0f).execute();
}

} // namespace
} // namespace gridtools::fn::backend