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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,24 +1,34 @@ | ||
| # Copyright (c) 2024, NVIDIA CORPORATION. | ||
| # Copyright (c) 2024-2025, NVIDIA CORPORATION. | ||
|
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| import argparse | ||
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| import requests | ||
| from packaging.version import Version | ||
| from packaging.specifiers import SpecifierSet | ||
| import argparse | ||
| from packaging.version import Version | ||
|
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| def get_pandas_versions(pandas_range): | ||
| url = "https://pypi.org/pypi/pandas/json" | ||
| response = requests.get(url) | ||
| data = response.json() | ||
| versions = [Version(v) for v in data['releases']] | ||
| versions = [Version(v) for v in data["releases"]] | ||
| specifier = SpecifierSet(pandas_range.lstrip("pandas")) | ||
| matching_versions = [v for v in versions if v in specifier] | ||
| matching_minors = sorted(set(".".join((str(v.major), str(v.minor))) for v in matching_versions), key=Version) | ||
| matching_minors = sorted( | ||
| set(".".join((str(v.major), str(v.minor))) for v in matching_versions), | ||
| key=Version, | ||
| ) | ||
| return matching_minors | ||
|
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| if __name__ == "__main__": | ||
| parser = argparse.ArgumentParser(description="Filter pandas versions by prefix.") | ||
| parser.add_argument("pandas_range", type=str, help="The version prefix to filter by.") | ||
| parser = argparse.ArgumentParser( | ||
| description="Filter pandas versions by prefix." | ||
| ) | ||
| parser.add_argument( | ||
| "pandas_range", type=str, help="The version prefix to filter by." | ||
| ) | ||
| args = parser.parse_args() | ||
|
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| versions = get_pandas_versions(args.pandas_range) | ||
| print(','.join(versions)) | ||
| print(",".join(versions)) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,135 @@ | ||
| /* | ||
| * Copyright (c) 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. | ||
| */ | ||
|
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| #include <benchmarks/common/generate_input.hpp> | ||
| #include <benchmarks/fixture/benchmark_fixture.hpp> | ||
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| #include <cudf/aggregation.hpp> | ||
| #include <cudf/binaryop.hpp> | ||
| #include <cudf/column/column_factories.hpp> | ||
| #include <cudf/rolling.hpp> | ||
| #include <cudf/rolling/range_window_bounds.hpp> | ||
| #include <cudf/scalar/scalar_factories.hpp> | ||
| #include <cudf/sorting.hpp> | ||
| #include <cudf/types.hpp> | ||
| #include <cudf/utilities/default_stream.hpp> | ||
| #include <cudf/utilities/memory_resource.hpp> | ||
| #include <cudf/utilities/type_dispatcher.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> | ||
| #include <thrust/tabulate.h> | ||
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| #include <nvbench/nvbench.cuh> | ||
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| #include <algorithm> | ||
| #include <cstdint> | ||
| #include <optional> | ||
|
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| void bench_grouped_range_rolling_sum(nvbench::state& state) | ||
| { | ||
| 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")); | ||
| // Configurable parameter is window range. | ||
| // Since orderby column is approximately equally spaced at unit | ||
| // intervals, this approximately controls the number of entries in | ||
| // the window. | ||
| auto const preceding_range = cudf::numeric_scalar<cudf::size_type>{ | ||
| static_cast<cudf::size_type>(state.get_int64("preceding_range") * 1000), true}; | ||
| auto const following_range = cudf::numeric_scalar<cudf::size_type>{ | ||
| static_cast<cudf::size_type>(state.get_int64("preceding_range") * 1000), true}; | ||
| auto const has_nulls = static_cast<bool>(state.get_int64("has_nulls")); | ||
|
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| auto vals = [&] { | ||
| data_profile const profile = data_profile_builder().cardinality(0).no_validity().distribution( | ||
| cudf::type_to_id<std::int32_t>(), distribution_id::UNIFORM, 0, 100); | ||
| return create_random_column(cudf::type_to_id<std::int32_t>(), row_count{num_rows}, profile); | ||
| }(); | ||
| auto const keys = [&] { | ||
| data_profile const profile = | ||
| data_profile_builder() | ||
| .cardinality(cardinality) | ||
| .no_validity() | ||
| .distribution(cudf::type_to_id<cudf::size_type>(), distribution_id::UNIFORM, 0, num_rows); | ||
| auto keys = | ||
| create_random_column(cudf::type_to_id<cudf::size_type>(), row_count{num_rows}, profile); | ||
| return cudf::sort(cudf::table_view{{keys->view()}}); | ||
| }(); | ||
| auto orderby = [&] { | ||
| auto seq = | ||
| cudf::make_numeric_column(cudf::data_type{cudf::type_to_id<cudf::size_type>()}, num_rows); | ||
| // Equally spaced rows separated by 1000 unit intervals | ||
| thrust::tabulate( | ||
| rmm::exec_policy(cudf::get_default_stream()), | ||
| seq->mutable_view().begin<cudf::size_type>(), | ||
| seq->mutable_view().end<cudf::size_type>(), | ||
| [] __device__(cudf::size_type i) { return static_cast<cudf::size_type>(i) * 1000; }); | ||
| // Add some units of noise | ||
| data_profile profile = data_profile_builder().cardinality(0).distribution( | ||
| cudf::type_to_id<cudf::duration_ms>(), distribution_id::NORMAL, -2000, 2000); | ||
| profile.set_null_probability(has_nulls ? std::optional<double>{400.0 / num_rows} | ||
| : std::nullopt); | ||
| auto noise = | ||
| create_random_column(cudf::type_to_id<cudf::size_type>(), row_count{num_rows}, profile); | ||
| auto result = | ||
| cudf::binary_operation(seq->view(), noise->view(), cudf::binary_operator::ADD, seq->type()); | ||
| auto columns = cudf::sort_by_key(cudf::table_view{{result->view()}}, | ||
| cudf::table_view{{keys->get_column(0).view(), result->view()}}, | ||
| {cudf::order::ASCENDING, cudf::order::ASCENDING}, | ||
| {cudf::null_order::AFTER, cudf::null_order::AFTER}) | ||
| ->release(); | ||
| return std::move(columns[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::grouped_range_rolling_window(keys->view(), | ||
| orderby->view(), | ||
| cudf::order::ASCENDING, | ||
| vals->view(), | ||
| cudf::range_window_bounds::get(preceding_range), | ||
| cudf::range_window_bounds::get(following_range), | ||
| 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"); | ||
| } | ||
|
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| NVBENCH_BENCH(bench_grouped_range_rolling_sum) | ||
| .set_name("range_grouped_rolling_sum") | ||
| .add_int64_power_of_two_axis("num_rows", {14, 22, 28}) | ||
| .add_int64_axis("preceding_range", {100}) | ||
| .add_int64_axis("following_range", {100}) | ||
| .add_int64_axis("has_nulls", {0, 1}) | ||
| .add_int64_axis("cardinality", {10, 100, 1'000'000, 100'000'000}); | ||
|
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| NVBENCH_BENCH(bench_grouped_range_rolling_sum) | ||
| .set_name("range_grouped_rolling_sum_large_windows") | ||
| .add_int64_power_of_two_axis("num_rows", {28}) | ||
| .add_int64_axis("preceding_range", {10'000, 40'000}) | ||
| .add_int64_axis("following_range", {0}) | ||
| .add_int64_axis("has_nulls", {0, 1}) | ||
| .add_int64_axis("cardinality", {100}); |
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