Refactor CSV reader benchmarks with nvbench

cpp/benchmarks/CMakeLists.txt

@@ -234,7 +234,7 @@ ConfigureNVBench(ORC_READER_NVBENCH io/orc/orc_reader_input.cpp io/orc/orc_reade
 
 # ##################################################################################################
 # * csv reader benchmark --------------------------------------------------------------------------
-ConfigureBench(CSV_READER_BENCH io/csv/csv_reader.cpp)
+ConfigureNVBench(CSV_READER_NVBENCH io/csv/csv_reader_input.cpp io/csv/csv_reader_options.cpp)
 
 # ##################################################################################################
 # * parquet writer benchmark ----------------------------------------------------------------------

cpp/benchmarks/io/csv/csv_reader_input.cpp

@@ -0,0 +1,112 @@
+/*
+ * Copyright (c) 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/common/generate_input.hpp>
+#include <benchmarks/fixture/benchmark_fixture.hpp>
+#include <benchmarks/fixture/rmm_pool_raii.hpp>
+#include <benchmarks/io/cuio_common.hpp>
+#include <benchmarks/io/nvbench_helpers.hpp>
+
+#include <cudf/io/csv.hpp>
+#include <cudf/utilities/default_stream.hpp>
+
+#include <nvbench/nvbench.cuh>
+
+constexpr size_t data_size         = 256 << 20;
+constexpr cudf::size_type num_cols = 64;
+
+template <typename DataType>
+void csv_read_common(DataType const& data_types,
+                     cudf::io::io_type const& source_type,
+                     nvbench::state& state)
+{
+  auto const tbl =
+    create_random_table(cycle_dtypes(data_types, num_cols), table_size_bytes{data_size});
+  auto const view = tbl->view();
+
+  cuio_source_sink_pair source_sink(source_type);
+  cudf::io::csv_writer_options options =
+    cudf::io::csv_writer_options::builder(source_sink.make_sink_info(), view).include_header(true);
+
+  cudf::io::write_csv(options);
+
+  cudf::io::csv_reader_options const read_options =
+    cudf::io::csv_reader_options::builder(source_sink.make_source_info());
+
+  auto const mem_stats_logger = cudf::memory_stats_logger();  // init stats logger
+  state.set_cuda_stream(nvbench::make_cuda_stream_view(cudf::default_stream_value.value()));
+  state.exec(nvbench::exec_tag::sync | nvbench::exec_tag::timer,
+             [&](nvbench::launch& launch, auto& timer) {
+               try_drop_l3_cache();  // Drop L3 cache for accurate measurement
+
+               timer.start();
+               cudf::io::read_csv(read_options);
+               timer.stop();
+             });
+
+  auto const time = state.get_summary("nv/cold/time/gpu/mean").get_float64("value");
+  state.add_element_count(static_cast<double>(data_size) / time, "bytes_per_second");
+  state.add_buffer_size(
+    mem_stats_logger.peak_memory_usage(), "peak_memory_usage", "peak_memory_usage");
+  state.add_buffer_size(source_sink.size(), "encoded_file_size", "encoded_file_size");
+}
+
+template <data_type DataType>
+void BM_csv_read_input(nvbench::state& state, nvbench::type_list<nvbench::enum_type<DataType>>)
+{
+  cudf::rmm_pool_raii rmm_pool;
+
+  auto const d_type      = get_type_or_group(static_cast<int32_t>(DataType));
+  auto const source_type = io_type::FILEPATH;
+
+  csv_read_common(d_type, source_type, state);
+}
+
+template <cudf::io::io_type IO>
+void BM_csv_read_io(nvbench::state& state, nvbench::type_list<nvbench::enum_type<IO>>)
+{
+  cudf::rmm_pool_raii rmm_pool;
+
+  auto const d_type      = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL),
+                                         static_cast<int32_t>(data_type::FLOAT),
+                                         static_cast<int32_t>(data_type::DECIMAL),
+                                         static_cast<int32_t>(data_type::TIMESTAMP),
+                                         static_cast<int32_t>(data_type::DURATION),
+                                         static_cast<int32_t>(data_type::STRING)});
+  auto const source_type = IO;
+
+  csv_read_common(d_type, source_type, state);
+}
+
+using d_type_list = nvbench::enum_type_list<data_type::INTEGRAL,
+                                            data_type::FLOAT,
+                                            data_type::DECIMAL,
+                                            data_type::TIMESTAMP,
+                                            data_type::DURATION,
+                                            data_type::STRING>;
+
+using io_list =
+  nvbench::enum_type_list<cudf::io::io_type::FILEPATH, cudf::io::io_type::HOST_BUFFER>;
+
+NVBENCH_BENCH_TYPES(BM_csv_read_input, NVBENCH_TYPE_AXES(d_type_list))
+  .set_name("csv_read_data_type")
+  .set_type_axes_names({"data_type"})
+  .set_min_samples(4);
+
+NVBENCH_BENCH_TYPES(BM_csv_read_io, NVBENCH_TYPE_AXES(io_list))
+  .set_name("csv_read_io")
+  .set_type_axes_names({"io"})
+  .set_min_samples(4);