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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,70 @@ | ||
| /* | ||
| * Copyright (c) 2024-2025, 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/fixture/benchmark_fixture.hpp> | ||
|
|
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| #include <cudf/aggregation.hpp> | ||
| #include <cudf/rolling.hpp> | ||
| #include <cudf/sorting.hpp> | ||
| #include <cudf/utilities/default_stream.hpp> | ||
|
|
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| #include <nvbench/nvbench.cuh> | ||
|
|
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| template <typename Type> | ||
| void bench_row_grouped_rolling_sum(nvbench::state& state, nvbench::type_list<Type>) | ||
| { | ||
| auto const num_rows = static_cast<cudf::size_type>(state.get_int64("num_rows")); | ||
| auto const cardinality = static_cast<cudf::size_type>(state.get_int64("cardinality")); | ||
| auto const preceding_size = static_cast<cudf::size_type>(state.get_int64("preceding_size")); | ||
| auto const following_size = static_cast<cudf::size_type>(state.get_int64("following_size")); | ||
| auto const min_periods = static_cast<cudf::size_type>(state.get_int64("min_periods")); | ||
|
|
||
| auto const keys = [&] { | ||
| data_profile const profile = | ||
| data_profile_builder() | ||
| .cardinality(cardinality) | ||
| .no_validity() | ||
| .distribution(cudf::type_to_id<int32_t>(), distribution_id::UNIFORM, 0, num_rows); | ||
| auto keys = create_random_column(cudf::type_to_id<int32_t>(), row_count{num_rows}, profile); | ||
| return cudf::sort(cudf::table_view{{keys->view()}}); | ||
| }(); | ||
| data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution( | ||
| cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, 100); | ||
| auto vals = create_random_column(cudf::type_to_id<Type>(), row_count{num_rows}, profile); | ||
|
|
||
| auto req = cudf::make_sum_aggregation<cudf::rolling_aggregation>(); | ||
|
|
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| auto const mem_stats_logger = cudf::memory_stats_logger(); | ||
| state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value())); | ||
| state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) { | ||
| auto const result = cudf::grouped_rolling_window( | ||
| keys->view(), vals->view(), preceding_size, following_size, min_periods, *req); | ||
| }); | ||
| auto const elapsed_time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value"); | ||
| state.add_element_count(static_cast<double>(num_rows) / elapsed_time / 1'000'000., "Mrows/s"); | ||
| state.add_buffer_size( | ||
| mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage"); | ||
| } | ||
|
|
||
| NVBENCH_BENCH_TYPES(bench_row_grouped_rolling_sum, | ||
| NVBENCH_TYPE_AXES(nvbench::type_list<std::int32_t, double>)) | ||
| .set_name("row_grouped_rolling_sum") | ||
| .add_int64_power_of_two_axis("num_rows", {14, 28}) | ||
| .add_int64_axis("preceding_size", {1, 10}) | ||
| .add_int64_axis("following_size", {2}) | ||
| .add_int64_axis("min_periods", {1}) | ||
| .add_int64_axis("cardinality", {10, 100, 1'000'000, 100'000'000}); |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,134 @@ | ||
| /* | ||
| * Copyright (c) 2024-2025, 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/fixture/benchmark_fixture.hpp> | ||
|
|
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| #include <cudf/aggregation.hpp> | ||
| #include <cudf/rolling.hpp> | ||
| #include <cudf/sorting.hpp> | ||
| #include <cudf/types.hpp> | ||
| #include <cudf/utilities/default_stream.hpp> | ||
|
|
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| #include <rmm/device_buffer.hpp> | ||
| #include <rmm/device_uvector.hpp> | ||
| #include <rmm/exec_policy.hpp> | ||
|
|
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| #include <thrust/iterator/counting_iterator.h> | ||
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| #include <nvbench/nvbench.cuh> | ||
|
|
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| #include <algorithm> | ||
|
|
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| template <typename Type> | ||
| void bench_row_fixed_rolling_sum(nvbench::state& state, nvbench::type_list<Type>) | ||
| { | ||
| auto const num_rows = static_cast<cudf::size_type>(state.get_int64("num_rows")); | ||
| auto const preceding_size = static_cast<cudf::size_type>(state.get_int64("preceding_size")); | ||
| auto const following_size = static_cast<cudf::size_type>(state.get_int64("following_size")); | ||
| auto const min_periods = static_cast<cudf::size_type>(state.get_int64("min_periods")); | ||
|
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| data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution( | ||
| cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, 100); | ||
| auto vals = create_random_column(cudf::type_to_id<Type>(), row_count{num_rows}, profile); | ||
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| auto req = cudf::make_sum_aggregation<cudf::rolling_aggregation>(); | ||
|
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| auto const mem_stats_logger = cudf::memory_stats_logger(); | ||
| state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value())); | ||
| state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) { | ||
| auto const result = | ||
| cudf::rolling_window(vals->view(), preceding_size, following_size, min_periods, *req); | ||
| }); | ||
| auto const elapsed_time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value"); | ||
| state.add_element_count(static_cast<double>(num_rows) / elapsed_time / 1'000'000., "Mrows/s"); | ||
| state.add_buffer_size( | ||
| mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage"); | ||
| } | ||
|
|
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| template <typename Type> | ||
| void bench_row_variable_rolling_sum(nvbench::state& state, nvbench::type_list<Type>) | ||
| { | ||
| auto const num_rows = static_cast<cudf::size_type>(state.get_int64("num_rows")); | ||
| auto const preceding_size = static_cast<cudf::size_type>(state.get_int64("preceding_size")); | ||
| auto const following_size = static_cast<cudf::size_type>(state.get_int64("following_size")); | ||
|
|
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| auto vals = [&]() { | ||
| data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution( | ||
| cudf::type_to_id<Type>(), distribution_id::UNIFORM, 0, 100); | ||
| return create_random_column(cudf::type_to_id<Type>(), row_count{num_rows}, profile); | ||
| }(); | ||
|
|
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| auto preceding = [&]() { | ||
| auto data = std::vector<cudf::size_type>(num_rows); | ||
| auto it = thrust::make_counting_iterator<cudf::size_type>(0); | ||
| std::transform(it, it + num_rows, data.begin(), [num_rows, preceding_size](auto i) { | ||
| return std::min(i + 1, std::max(preceding_size, i + 1 - num_rows)); | ||
| }); | ||
| auto buf = rmm::device_buffer( | ||
| data.data(), num_rows * sizeof(cudf::size_type), cudf::get_default_stream()); | ||
| cudf::get_default_stream().synchronize(); | ||
| return std::make_unique<cudf::column>(cudf::data_type(cudf::type_to_id<cudf::size_type>()), | ||
| num_rows, | ||
| std::move(buf), | ||
| rmm::device_buffer{}, | ||
| 0); | ||
| }(); | ||
|
|
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| auto following = [&]() { | ||
| auto data = std::vector<cudf::size_type>(num_rows); | ||
| auto it = thrust::make_counting_iterator<cudf::size_type>(0); | ||
| std::transform(it, it + num_rows, data.begin(), [num_rows, following_size](auto i) { | ||
| return std::max(-i - 1, std::min(following_size, num_rows - i - 1)); | ||
| }); | ||
| auto buf = rmm::device_buffer( | ||
| data.data(), num_rows * sizeof(cudf::size_type), cudf::get_default_stream()); | ||
| cudf::get_default_stream().synchronize(); | ||
| return std::make_unique<cudf::column>(cudf::data_type(cudf::type_to_id<cudf::size_type>()), | ||
| num_rows, | ||
| std::move(buf), | ||
| rmm::device_buffer{}, | ||
| 0); | ||
| }(); | ||
|
|
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| auto req = cudf::make_sum_aggregation<cudf::rolling_aggregation>(); | ||
|
|
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| auto const mem_stats_logger = cudf::memory_stats_logger(); | ||
| state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::get_default_stream().value())); | ||
| state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) { | ||
| auto const result = | ||
| cudf::rolling_window(vals->view(), preceding->view(), following->view(), 1, *req); | ||
| }); | ||
| auto const elapsed_time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value"); | ||
| state.add_element_count(static_cast<double>(num_rows) / elapsed_time / 1'000'000., "Mrows/s"); | ||
| state.add_buffer_size( | ||
| mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage"); | ||
| } | ||
|
|
||
| NVBENCH_BENCH_TYPES(bench_row_fixed_rolling_sum, | ||
| NVBENCH_TYPE_AXES(nvbench::type_list<std::int32_t, double>)) | ||
| .set_name("row_fixed_rolling_sum") | ||
| .add_int64_power_of_two_axis("num_rows", {14, 22, 28}) | ||
| .add_int64_axis("preceding_size", {1, 10, 100}) | ||
| .add_int64_axis("following_size", {2}) | ||
| .add_int64_axis("min_periods", {1, 20}); | ||
|
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| NVBENCH_BENCH_TYPES(bench_row_variable_rolling_sum, | ||
| NVBENCH_TYPE_AXES(nvbench::type_list<std::int32_t, double>)) | ||
| .set_name("row_variable_rolling_sum") | ||
| .add_int64_power_of_two_axis("num_rows", {14, 22, 28}) | ||
| .add_int64_axis("preceding_size", {10, 100}) | ||
| .add_int64_axis("following_size", {2}); |
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