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Add bytes_per_second to transpose benchmark #14170

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merged 15 commits into from
Oct 10, 2023
Merged

Add bytes_per_second to transpose benchmark #14170

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66f43e4
Add `bytes_per_second` to transpose benchmark
Blonck Sep 22, 2023
d2676b5
Run clang-format on transpose benchmark
Blonck Sep 22, 2023
f594a81
Refactor column type in transpose benchmark
Blonck Sep 27, 2023
dcd9ef1
Merge branch 'branch-23.12' into processed_bytes_transpose_bench
Blonck Sep 27, 2023
60845b7
Fix transpose benchmark type issue
Blonck Sep 27, 2023
05e1afa
Merge branch 'branch-23.12' into processed_bytes_transpose_bench
Blonck Sep 28, 2023
e9280cc
Change way column type is handled in transpose bench
Blonck Sep 28, 2023
4187495
Merge branch 'branch-23.12' into processed_bytes_transpose_bench
Blonck Sep 30, 2023
c50a5ef
Fix code style in transpose benchmark
Blonck Sep 30, 2023
8083756
Avoid potential integer overflow in transpose benchmark.
Blonck Oct 1, 2023
e6d9f24
Update cpp/benchmarks/transpose/transpose.cpp
Blonck Oct 2, 2023
82cbcf9
Fix code style in transpose benchmark
Blonck Oct 3, 2023
ed8aa1e
Merge branch 'branch-23.12' into processed_bytes_transpose_bench
harrism Oct 3, 2023
943b9f3
Merge branch 'branch-23.12' into processed_bytes_transpose_bench
Blonck Oct 4, 2023
09f10d8
Merge branch 'branch-23.12' into processed_bytes_transpose_bench
harrism Oct 4, 2023
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29 changes: 22 additions & 7 deletions cpp/benchmarks/transpose/transpose.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -20,17 +20,19 @@
#include <cudf/column/column_factories.hpp>
#include <cudf/table/table.hpp>
#include <cudf/transpose.hpp>
#include <cudf/types.hpp>

#include <thrust/iterator/counting_iterator.h>
#include <thrust/iterator/transform_iterator.h>

static void BM_transpose(benchmark::State& state)
{
auto count = state.range(0);
constexpr auto column_type_id = cudf::type_id::INT32;
auto int_column_generator =
thrust::make_transform_iterator(thrust::counting_iterator(0), [count](int i) {
return cudf::make_numeric_column(
cudf::data_type{cudf::type_id::INT32}, count, cudf::mask_state::ALL_VALID);
cudf::data_type{column_type_id}, count, cudf::mask_state::ALL_VALID);
});

auto input_table = cudf::table(std::vector(int_column_generator, int_column_generator + count));
Expand All @@ -40,16 +42,29 @@ static void BM_transpose(benchmark::State& state)
cuda_event_timer raii(state, true);
auto output = cudf::transpose(input);
}

// Collect memory statistics.
auto const bytes_read = input.num_columns() * input.num_rows() * sizeof(cudf::id_to_type<column_type_id>);
auto const bytes_written = bytes_read;
// Account for nullability in input and output.
auto const null_bytes =
2 * input.num_columns() * cudf::bitmask_allocation_size_bytes(input.num_rows());
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suggestion: ‏This one could also overflow, I think, perhaps:

Suggested change
2 * input.num_columns() * cudf::bitmask_allocation_size_bytes(input.num_rows());
2 * static_cast<uint64_t>(input.num_columns()) * cudf::bitmask_allocation_size_bytes(input.num_rows());

?

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Are you sure about this one? Since the return type of cudf::bitmask_allocation_size_bytes is std::size_t which is either unsigned long or unsigned long long so for reasonable input sizes the integer type promotion will avoid the overflow (https://cppinsights.io/s/26f977cb).

That said, just having this discussion indicates I should've included an explicit cast upfront to clear up any potential confusion.

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Left-to-right associativity means that this is evaluated as (2 * ncol) * nrow, the first multiplication is performed in size_type (AKA, int32_t), so that could overflow, no? Although I think these benchmarks are generally run with fewer than $2^{30}$ rows, there's in general no reason why they couldn't be (although the transpose performance will be terrible I grant you).

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Why not just always put the thing that returns size_t (sizeof or bitmask_allocation_size_bytes) first in the arithmetic in all of these PRs?

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Personally, I would keep the cast explicit to make visible what is happening, but I don't have a strong stance on it.


state.SetBytesProcessed(static_cast<int64_t>(state.iterations()) *
(bytes_read + bytes_written + null_bytes));
}

class Transpose : public cudf::benchmark {};

#define TRANSPOSE_BM_BENCHMARK_DEFINE(name) \
BENCHMARK_DEFINE_F(Transpose, name)(::benchmark::State & state) { BM_transpose(state); } \
BENCHMARK_REGISTER_F(Transpose, name) \
->RangeMultiplier(4) \
->Range(4, 4 << 13) \
->UseManualTime() \
#define TRANSPOSE_BM_BENCHMARK_DEFINE(name) \
BENCHMARK_DEFINE_F(Transpose, name)(::benchmark::State & state) \
{ \
BM_transpose(state); \
} \
BENCHMARK_REGISTER_F(Transpose, name) \
->RangeMultiplier(4) \
->Range(4, 4 << 13) \
->UseManualTime() \
->Unit(benchmark::kMillisecond);

TRANSPOSE_BM_BENCHMARK_DEFINE(transpose_simple);
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