text: /*
   * Copyright (c) 2020-2022, 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/fixture/benchmark_fixture.hpp>
  #include <benchmarks/synchronization/synchronization.hpp>
  #include <cudf_test/column_wrapper.hpp>
  
  #include <cudf/column/column.hpp>
  #include <cudf/merge.hpp>
  #include <cudf/table/table.hpp>
  #include <cudf/table/table_view.hpp>
  
  #include <thrust/iterator/constant_iterator.h>
  
  #include <random>
  
  // to enable, run cmake with -DBUILD_BENCHMARKS=ON
  
  // Fixture that enables RMM pool mode
  class Merge : public cudf::benchmark {
  };
  
  using IntColWrap = cudf::test::fixed_width_column_wrapper<int32_t>;
  
  void BM_merge(benchmark::State& state)
  {
    cudf::size_type const avg_rows = 1 << 19;  // 512K rows
    int const num_tables           = state.range(0);
  
    // Content is irrelevant for the benchmark
    auto data_sequence = thrust::make_constant_iterator(0);
  
    // Using 0 seed to ensure consistent pseudo-numbers on each run
    std::mt19937 rand_gen(0);
    // Gaussian distribution with 98% of elements are in range [0, avg_rows*2]
    std::normal_distribution<> table_size_dist(avg_rows, avg_rows / 2);
    // Used to generate a random monotonic sequence for each table key column
    std::uniform_int_distribution<> key_dist(0, 10);
  
    std::vector<std::pair<IntColWrap, IntColWrap>> columns;
    size_t total_rows = 0;
    std::vector<cudf::table_view> tables;
    for (int i = 0; i < num_tables; ++i) {
      cudf::size_type const rows = std::round(table_size_dist(rand_gen));
      // Ensure size in range [0, avg_rows*2]
      auto const clamped_rows = std::clamp(rows, 0, avg_rows * 2);
  
      int32_t prev_key  = 0;
      auto key_sequence = cudf::detail::make_counting_transform_iterator(0, [&](auto row) {
        prev_key += key_dist(rand_gen);
        return prev_key;
      });
  
      columns.emplace_back(
        std::pair<IntColWrap, IntColWrap>{IntColWrap(key_sequence, key_sequence + clamped_rows),
                                          IntColWrap(data_sequence, data_sequence + clamped_rows)});
      tables.push_back(cudf::table_view{{columns.back().first, columns.back().second}});
      total_rows += clamped_rows;
    }
    std::vector<cudf::size_type> const key_cols{0};
    std::vector<cudf::order> const column_order{cudf::order::ASCENDING};
    std::vector<cudf::null_order> const null_precedence{};
  
    for (auto _ : state) {
      cuda_event_timer raii(state, true);  // flush_l2_cache = true, stream = 0
      auto result = cudf::merge(tables, key_cols, column_order, null_precedence);
    }
  
    state.SetBytesProcessed(state.iterations() * 2 * sizeof(int32_t) * total_rows);
  }
  
  #define MBM_BENCHMARK_DEFINE(name)                                                 \
    BENCHMARK_DEFINE_F(Merge, name)(::benchmark::State & state) { BM_merge(state); } \
    BENCHMARK_REGISTER_F(Merge, name)                                                \
      ->Unit(benchmark::kMillisecond)                                                \
      ->UseManualTime()                                                              \
      ->RangeMultiplier(2)                                                           \
      ->Ranges({{2, 128}});
  
  MBM_BENCHMARK_DEFINE(pow2tables);
  