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Forward-merge branch-24.12 into branch-25.02 #17380

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Nov 20, 2024
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Forward-merge branch-24.12 into branch-25.02 #17380

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6 changes: 6 additions & 0 deletions cpp/benchmarks/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -286,6 +286,12 @@ ConfigureNVBench(
ConfigureBench(HASHING_BENCH hashing/partition.cpp)
ConfigureNVBench(HASHING_NVBENCH hashing/hash.cpp)

# ##################################################################################################
# * interop benchmark ------------------------------------------------------------------------------
ConfigureNVBench(INTEROP_NVBENCH interop/interop.cpp)
target_link_libraries(INTEROP_NVBENCH PRIVATE nanoarrow)
target_include_directories(INTEROP_NVBENCH PRIVATE ${CMAKE_SOURCE_DIR}/tests/interop)

# ##################################################################################################
# * merge benchmark -------------------------------------------------------------------------------
ConfigureBench(MERGE_BENCH merge/merge.cpp)
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244 changes: 244 additions & 0 deletions cpp/benchmarks/interop/interop.cpp
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@@ -0,0 +1,244 @@
/*
* Copyright (c) 2024, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/

#include <benchmarks/common/generate_input.hpp>
#include <benchmarks/common/table_utilities.hpp>

#include <cudf/interop.hpp>

#include <thrust/iterator/counting_iterator.h>

#include <nanoarrow/nanoarrow.hpp>
#include <nanoarrow/nanoarrow_device.h>
#include <nanoarrow_utils.hpp>
#include <nvbench/nvbench.cuh>

#include <algorithm>
#include <iterator>
#include <vector>

template <cudf::type_id data_type>
void BM_to_arrow_device(nvbench::state& state, nvbench::type_list<nvbench::enum_type<data_type>>)
{
auto const num_rows = static_cast<cudf::size_type>(state.get_int64("num_rows"));
auto const num_columns = static_cast<cudf::size_type>(state.get_int64("num_columns"));
auto const num_elements = static_cast<int64_t>(num_rows) * num_columns;

std::vector<cudf::type_id> types(num_columns, data_type);

auto const table = create_random_table(types, row_count{num_rows});
int64_t const size_bytes = estimate_size(table->view());

state.add_element_count(num_elements, "num_elements");
state.add_global_memory_reads(size_bytes);
state.add_global_memory_writes(size_bytes);

state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
cudf::to_arrow_device(table->view(), rmm::cuda_stream_view{launch.get_stream()});
});
}

template <cudf::type_id data_type>
void BM_to_arrow_host(nvbench::state& state, nvbench::type_list<nvbench::enum_type<data_type>>)
{
auto const num_rows = static_cast<cudf::size_type>(state.get_int64("num_rows"));
auto const num_columns = static_cast<cudf::size_type>(state.get_int64("num_columns"));
auto const num_elements = static_cast<int64_t>(num_rows) * num_columns;

std::vector<cudf::type_id> types(num_columns, data_type);

auto const table = create_random_table(types, row_count{num_rows});
int64_t const size_bytes = estimate_size(table->view());

state.add_element_count(num_elements, "num_elements");
state.add_global_memory_reads(size_bytes);
state.add_global_memory_writes(size_bytes);

state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
cudf::to_arrow_host(table->view(), rmm::cuda_stream_view{launch.get_stream()});
});
}

template <cudf::type_id data_type>
void BM_from_arrow_device(nvbench::state& state, nvbench::type_list<nvbench::enum_type<data_type>>)
{
auto const num_rows = static_cast<cudf::size_type>(state.get_int64("num_rows"));
auto const num_columns = static_cast<cudf::size_type>(state.get_int64("num_columns"));
auto const num_elements = static_cast<int64_t>(num_rows) * num_columns;

std::vector<cudf::type_id> types(num_columns, data_type);

data_profile profile;
profile.set_struct_depth(1);
profile.set_list_depth(1);

auto const table = create_random_table(types, row_count{num_rows}, profile);
cudf::table_view table_view = table->view();
int64_t const size_bytes = estimate_size(table_view);

std::vector<cudf::column_metadata> table_metadata;

std::transform(thrust::make_counting_iterator(0),
thrust::make_counting_iterator(num_columns),
std::back_inserter(table_metadata),
[&](auto const column) {
cudf::column_metadata column_metadata{""};
column_metadata.children_meta = std::vector(
table->get_column(column).num_children(), cudf::column_metadata{""});
return column_metadata;
});

cudf::unique_schema_t schema = cudf::to_arrow_schema(table_view, table_metadata);
cudf::unique_device_array_t input = cudf::to_arrow_device(table_view);

state.add_element_count(num_elements, "num_elements");
state.add_global_memory_reads(size_bytes);
state.add_global_memory_writes(size_bytes);

state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
cudf::from_arrow_device_column(
schema.get(), input.get(), rmm::cuda_stream_view{launch.get_stream()});
});
}

template <cudf::type_id data_type>
void BM_from_arrow_host(nvbench::state& state, nvbench::type_list<nvbench::enum_type<data_type>>)
{
auto const num_rows = static_cast<cudf::size_type>(state.get_int64("num_rows"));
auto const num_columns = static_cast<cudf::size_type>(state.get_int64("num_columns"));
auto const num_elements = static_cast<int64_t>(num_rows) * num_columns;

std::vector<cudf::type_id> types(num_columns, data_type);

data_profile profile;
profile.set_struct_depth(1);
profile.set_list_depth(1);

auto const table = create_random_table(types, row_count{num_rows}, profile);
cudf::table_view table_view = table->view();
int64_t const size_bytes = estimate_size(table_view);

std::vector<cudf::column_metadata> table_metadata;

std::transform(thrust::make_counting_iterator(0),
thrust::make_counting_iterator(num_columns),
std::back_inserter(table_metadata),
[&](auto const column) {
cudf::column_metadata column_metadata{""};
column_metadata.children_meta = std::vector(
table->get_column(column).num_children(), cudf::column_metadata{""});
return column_metadata;
});

cudf::unique_schema_t schema = cudf::to_arrow_schema(table_view, table_metadata);
cudf::unique_device_array_t input = cudf::to_arrow_host(table_view);

state.add_element_count(num_elements, "num_elements");
state.add_global_memory_reads(size_bytes);
state.add_global_memory_writes(size_bytes);

state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
cudf::from_arrow_host_column(
schema.get(), input.get(), rmm::cuda_stream_view{launch.get_stream()});
});
}

using data_types = nvbench::enum_type_list<cudf::type_id::INT8,
cudf::type_id::INT16,
cudf::type_id::INT32,
cudf::type_id::INT64,
cudf::type_id::UINT8,
cudf::type_id::UINT16,
cudf::type_id::UINT32,
cudf::type_id::UINT64,
cudf::type_id::FLOAT32,
cudf::type_id::FLOAT64,
cudf::type_id::BOOL8,
cudf::type_id::TIMESTAMP_SECONDS,
cudf::type_id::TIMESTAMP_MILLISECONDS,
cudf::type_id::TIMESTAMP_MICROSECONDS,
cudf::type_id::TIMESTAMP_NANOSECONDS,
cudf::type_id::DURATION_SECONDS,
cudf::type_id::DURATION_MILLISECONDS,
cudf::type_id::DURATION_MICROSECONDS,
cudf::type_id::DURATION_NANOSECONDS,
cudf::type_id::STRING,
cudf::type_id::LIST,
cudf::type_id::DECIMAL32,
cudf::type_id::DECIMAL64,
cudf::type_id::DECIMAL128,
cudf::type_id::STRUCT>;

static char const* stringify_type(cudf::type_id value)
{
switch (value) {
case cudf::type_id::INT8: return "INT8";
case cudf::type_id::INT16: return "INT16";
case cudf::type_id::INT32: return "INT32";
case cudf::type_id::INT64: return "INT64";
case cudf::type_id::UINT8: return "UINT8";
case cudf::type_id::UINT16: return "UINT16";
case cudf::type_id::UINT32: return "UINT32";
case cudf::type_id::UINT64: return "UINT64";
case cudf::type_id::FLOAT32: return "FLOAT32";
case cudf::type_id::FLOAT64: return "FLOAT64";
case cudf::type_id::BOOL8: return "BOOL8";
case cudf::type_id::TIMESTAMP_DAYS: return "TIMESTAMP_DAYS";
case cudf::type_id::TIMESTAMP_SECONDS: return "TIMESTAMP_SECONDS";
case cudf::type_id::TIMESTAMP_MILLISECONDS: return "TIMESTAMP_MILLISECONDS";
case cudf::type_id::TIMESTAMP_MICROSECONDS: return "TIMESTAMP_MICROSECONDS";
case cudf::type_id::TIMESTAMP_NANOSECONDS: return "TIMESTAMP_NANOSECONDS";
case cudf::type_id::DURATION_DAYS: return "DURATION_DAYS";
case cudf::type_id::DURATION_SECONDS: return "DURATION_SECONDS";
case cudf::type_id::DURATION_MILLISECONDS: return "DURATION_MILLISECONDS";
case cudf::type_id::DURATION_MICROSECONDS: return "DURATION_MICROSECONDS";
case cudf::type_id::DURATION_NANOSECONDS: return "DURATION_NANOSECONDS";
case cudf::type_id::DICTIONARY32: return "DICTIONARY32";
case cudf::type_id::STRING: return "STRING";
case cudf::type_id::LIST: return "LIST";
case cudf::type_id::DECIMAL32: return "DECIMAL32";
case cudf::type_id::DECIMAL64: return "DECIMAL64";
case cudf::type_id::DECIMAL128: return "DECIMAL128";
case cudf::type_id::STRUCT: return "STRUCT";
default: return "unknown";
}
}

NVBENCH_DECLARE_ENUM_TYPE_STRINGS(cudf::type_id, stringify_type, stringify_type)

NVBENCH_BENCH_TYPES(BM_to_arrow_host, NVBENCH_TYPE_AXES(data_types))
.set_type_axes_names({"data_type"})
.set_name("to_arrow_host")
.add_int64_axis("num_rows", {10'000, 100'000, 1'000'000, 10'000'000})
.add_int64_axis("num_columns", {1});

NVBENCH_BENCH_TYPES(BM_to_arrow_device, NVBENCH_TYPE_AXES(data_types))
.set_type_axes_names({"data_type"})
.set_name("to_arrow_device")
.add_int64_axis("num_rows", {10'000, 100'000, 1'000'000, 10'000'000})
.add_int64_axis("num_columns", {1});

NVBENCH_BENCH_TYPES(BM_from_arrow_host, NVBENCH_TYPE_AXES(data_types))
.set_type_axes_names({"data_type"})
.set_name("from_arrow_host")
.add_int64_axis("num_rows", {10'000, 100'000, 1'000'000, 10'000'000})
.add_int64_axis("num_columns", {1});

NVBENCH_BENCH_TYPES(BM_from_arrow_device, NVBENCH_TYPE_AXES(data_types))
.set_type_axes_names({"data_type"})
.set_name("from_arrow_device")
.add_int64_axis("num_rows", {10'000, 100'000, 1'000'000, 10'000'000})
.add_int64_axis("num_columns", {1});
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