Refactor contains_table with cuco::static_set

cpp/benchmarks/CMakeLists.txt

@@ -173,7 +173,7 @@ ConfigureBench(ITERATOR_BENCH iterator/iterator.cu)
 # ##################################################################################################
 # * search benchmark ------------------------------------------------------------------------------
 ConfigureBench(SEARCH_BENCH search/search.cpp)
-ConfigureNVBench(SEARCH_NVBENCH search/contains.cpp)
+ConfigureNVBench(SEARCH_NVBENCH search/contains_scalar.cpp search/contains_table.cpp)
 
 # ##################################################################################################
 # * sort benchmark --------------------------------------------------------------------------------

cpp/benchmarks/search/contains_table.cpp

@@ -0,0 +1,73 @@
+/*
+ * Copyright (c) 2023, 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>
+
+#include <cudf/detail/search.hpp>
+#include <cudf/lists/list_view.hpp>
+#include <cudf/types.hpp>
+
+#include <rmm/mr/device/per_device_resource.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+auto constexpr num_unique_elements = 1000;
+
+template <typename Type>
+static void nvbench_contains_table(nvbench::state& state, nvbench::type_list<Type>)
+{
+  auto const size               = state.get_int64("table_size");
+  auto const dtype              = cudf::type_to_id<Type>();
+  double const null_probability = state.get_float64("null_probability");
+
+  auto builder = data_profile_builder().null_probability(null_probability);
+  if (dtype == cudf::type_id::LIST) {
+    builder.distribution(dtype, distribution_id::UNIFORM, 0, num_unique_elements)
+      .distribution(cudf::type_id::INT32, distribution_id::UNIFORM, 0, num_unique_elements)
+      .list_depth(1);
+  } else {
+    builder.distribution(dtype, distribution_id::UNIFORM, 0, num_unique_elements);
+  }
+
+  auto const haystack = create_random_table(
+    {dtype}, table_size_bytes{static_cast<size_t>(size)}, data_profile{builder}, 0);
+  auto const needles = create_random_table(
+    {dtype}, table_size_bytes{static_cast<size_t>(size)}, data_profile{builder}, 1);
+
+  auto mem_stats_logger = cudf::memory_stats_logger();
+
+  state.exec(nvbench::exec_tag::sync, [&](nvbench::launch& launch) {
+    auto const stream_view = rmm::cuda_stream_view{launch.get_stream()};
+    [[maybe_unused]] auto const result =
+      cudf::detail::contains(haystack->view(),
+                             needles->view(),
+                             cudf::null_equality::EQUAL,
+                             cudf::nan_equality::ALL_EQUAL,
+                             stream_view,
+                             rmm::mr::get_current_device_resource());
+  });
+
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+}
+
+NVBENCH_BENCH_TYPES(nvbench_contains_table,
+                    NVBENCH_TYPE_AXES(nvbench::type_list<int32_t, cudf::list_view>))
+  .set_name("contains_table")
+  .set_type_axes_names({"type"})
+  .add_float64_axis("null_probability", {0.0, 0.1})
+  .add_int64_axis("table_size", {10'000, 100'000, 1'000'000, 10'000'000});