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Templatization of fixed-width parquet decoding kernels. #15911

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merged 5 commits into from
Jun 28, 2024
Merged

Templatization of fixed-width parquet decoding kernels. #15911

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e40e26d
Refactor all fixed width kernels in decode_fixed.cu into a single tem…
nvdbaranec Jun 3, 2024
899c2e9
Formattiing pass.
nvdbaranec Jun 3, 2024
78f25ee
Merge branch 'branch-24.08' into fw_microkernel_template
nvdbaranec Jun 3, 2024
d98b6aa
Merge branch 'branch-24.08' into fw_microkernel_template
nvdbaranec Jun 27, 2024
7f09ba3
Add some comments around specific kernel launches to match existing c…
nvdbaranec Jun 27, 2024
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50 changes: 42 additions & 8 deletions cpp/benchmarks/io/parquet/parquet_reader_input.cpp
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
/*
* Copyright (c) 2022-2023, NVIDIA CORPORATION.
* Copyright (c) 2022-2024, NVIDIA CORPORATION.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
Expand Down Expand Up @@ -59,20 +59,18 @@ void parquet_read_common(cudf::size_type num_rows_to_read,
}

template <data_type DataType>
void BM_parquet_read_data(nvbench::state& state, nvbench::type_list<nvbench::enum_type<DataType>>)
void BM_parquet_read_data_common(nvbench::state& state,
data_profile const& profile,
nvbench::type_list<nvbench::enum_type<DataType>>)
{
auto const d_type = get_type_or_group(static_cast<int32_t>(DataType));
auto const cardinality = static_cast<cudf::size_type>(state.get_int64("cardinality"));
auto const run_length = static_cast<cudf::size_type>(state.get_int64("run_length"));
auto const source_type = retrieve_io_type_enum(state.get_string("io_type"));
auto const compression = cudf::io::compression_type::SNAPPY;
cuio_source_sink_pair source_sink(source_type);

auto const num_rows_written = [&]() {
auto const tbl = create_random_table(
cycle_dtypes(d_type, num_cols),
table_size_bytes{data_size},
data_profile_builder().cardinality(cardinality).avg_run_length(run_length));
auto const tbl =
create_random_table(cycle_dtypes(d_type, num_cols), table_size_bytes{data_size}, profile);
auto const view = tbl->view();

cudf::io::parquet_writer_options write_opts =
Expand All @@ -85,6 +83,32 @@ void BM_parquet_read_data(nvbench::state& state, nvbench::type_list<nvbench::enu
parquet_read_common(num_rows_written, num_cols, source_sink, state);
}

template <data_type DataType>
void BM_parquet_read_data(nvbench::state& state,
nvbench::type_list<nvbench::enum_type<DataType>> type_list)
{
auto const cardinality = static_cast<cudf::size_type>(state.get_int64("cardinality"));
auto const run_length = static_cast<cudf::size_type>(state.get_int64("run_length"));
BM_parquet_read_data_common<DataType>(
state, data_profile_builder().cardinality(cardinality).avg_run_length(run_length), type_list);
}

template <data_type DataType>
void BM_parquet_read_fixed_width_struct(nvbench::state& state,
nvbench::type_list<nvbench::enum_type<DataType>> type_list)
{
auto const cardinality = static_cast<cudf::size_type>(state.get_int64("cardinality"));
auto const run_length = static_cast<cudf::size_type>(state.get_int64("run_length"));
std::vector<cudf::type_id> s_types{
cudf::type_id::INT32, cudf::type_id::FLOAT32, cudf::type_id::INT64};
BM_parquet_read_data_common<DataType>(state,
data_profile_builder()
.cardinality(cardinality)
.avg_run_length(run_length)
.struct_types(s_types),
type_list);
}

void BM_parquet_read_io_compression(nvbench::state& state)
{
auto const d_type = get_type_or_group({static_cast<int32_t>(data_type::INTEGRAL),
Expand Down Expand Up @@ -247,3 +271,13 @@ NVBENCH_BENCH(BM_parquet_read_io_small_mixed)
.add_int64_axis("cardinality", {0, 1000})
.add_int64_axis("run_length", {1, 32})
.add_int64_axis("num_string_cols", {1, 2, 3});

// a benchmark for structs that only contain fixed-width types
using d_type_list_struct_only = nvbench::enum_type_list<data_type::STRUCT>;
NVBENCH_BENCH_TYPES(BM_parquet_read_fixed_width_struct, NVBENCH_TYPE_AXES(d_type_list_struct_only))
.set_name("parquet_read_fixed_width_struct")
.set_type_axes_names({"data_type"})
.add_string_axis("io_type", {"DEVICE_BUFFER"})
.set_min_samples(4)
.add_int64_axis("cardinality", {0, 1000})
.add_int64_axis("run_length", {1, 32});
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