New rolling window benchmark

rapidsai · Dec 17, 2024 · 1a0b7cb · 1a0b7cb
1 parent becfacc
commit 1a0b7cb
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diff --git a/cpp/benchmarks/CMakeLists.txt b/cpp/benchmarks/CMakeLists.txt
@@ -425,6 +425,11 @@ ConfigureNVBench(DECIMAL_NVBENCH decimal/convert_floating.cpp)
 # ---------------------------------------------------------------------------------
 ConfigureNVBench(RESHAPE_NVBENCH reshape/interleave.cpp)
 
+# ##################################################################################################
+# * reshape benchmark
+# ---------------------------------------------------------------------------------
+ConfigureNVBench(ROLLING_NVBENCH rolling/rolling_sum.cpp)
+
 add_custom_target(
   run_benchmarks
   DEPENDS CUDF_BENCHMARKS

diff --git a/cpp/benchmarks/rolling/rolling_sum.cpp b/cpp/benchmarks/rolling/rolling_sum.cpp
@@ -0,0 +1,136 @@
+/*
+ * 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/fixture/benchmark_fixture.hpp>
+
+#include <cudf/aggregation.hpp>
+#include <cudf/copying.hpp>
+#include <cudf/detail/aggregation/aggregation.hpp>
+#include <cudf/rolling.hpp>
+#include <cudf/sorting.hpp>
+#include <cudf/types.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <rmm/device_buffer.hpp>
+#include <rmm/device_uvector.hpp>
+#include <rmm/exec_policy.hpp>
+
+#include <thrust/iterator/counting_iterator.h>
+
+#include <nvbench/nvbench.cuh>
+
+#include <algorithm>
+
+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"));
+
+  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>();
+
+  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");
+}
+
+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"));
+
+  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);
+  }();
+
+  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);
+  }();
+
+  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);
+  }();
+
+  auto req = cudf::make_sum_aggregation<cudf::rolling_aggregation>();
+
+  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});
+
+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});