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Add a libcudf/thrust-based TPC-H derived datagen (#16294)
This PR adds a TPC-H (according to spec 3.0.1) inspired datagen written using `libcudf` and `thrust` ### Implementation Status - [x] lineitem - [x] orders - [x] region - [x] nation - [x] supplier - [x] customer - [x] part - [x] partsupp Authors: - Jayjeet Chakraborty (https://github.com/JayjeetAtGithub) - Karthikeyan (https://github.com/karthikeyann) Approvers: - Mark Harris (https://github.com/harrism) - Yunsong Wang (https://github.com/PointKernel) - Karthikeyan (https://github.com/karthikeyann) URL: #16294
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cpp/benchmarks/common/tpch_data_generator/random_column_generator.cu
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/* | ||
* 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. | ||
*/ | ||
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#include "random_column_generator.hpp" | ||
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#include <cudf_test/column_wrapper.hpp> | ||
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#include <cudf/binaryop.hpp> | ||
#include <cudf/detail/iterator.cuh> | ||
#include <cudf/detail/nvtx/ranges.hpp> | ||
#include <cudf/filling.hpp> | ||
#include <cudf/strings/detail/strings_children.cuh> | ||
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#include <rmm/exec_policy.hpp> | ||
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#include <thrust/iterator/counting_iterator.h> | ||
#include <thrust/random.h> | ||
#include <thrust/transform.h> | ||
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#include <string> | ||
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namespace cudf::datagen { | ||
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namespace { | ||
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// Functor for generating random strings | ||
struct random_string_generator { | ||
char* chars; | ||
thrust::default_random_engine engine; | ||
thrust::uniform_int_distribution<unsigned char> char_dist; | ||
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CUDF_HOST_DEVICE random_string_generator(char* c) : chars(c), char_dist(44, 122) {} | ||
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__device__ void operator()(thrust::tuple<int64_t, int64_t> str_begin_end) | ||
{ | ||
auto begin = thrust::get<0>(str_begin_end); | ||
auto end = thrust::get<1>(str_begin_end); | ||
engine.discard(begin); | ||
for (auto i = begin; i < end; ++i) { | ||
auto ch = char_dist(engine); | ||
if (i == end - 1 && ch >= '\x7F') ch = ' '; // last element ASCII only. | ||
if (ch >= '\x7F') // x7F is at the top edge of ASCII | ||
chars[i++] = '\xC4'; // these characters are assigned two bytes | ||
chars[i] = static_cast<char>(ch + (ch >= '\x7F')); | ||
} | ||
} | ||
}; | ||
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// Functor for generating random numbers | ||
template <typename T> | ||
struct random_number_generator { | ||
T lower; | ||
T upper; | ||
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CUDF_HOST_DEVICE random_number_generator(T lower, T upper) : lower(lower), upper(upper) {} | ||
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__device__ T operator()(const int64_t idx) const | ||
{ | ||
if constexpr (cudf::is_integral<T>()) { | ||
thrust::default_random_engine engine; | ||
thrust::uniform_int_distribution<T> dist(lower, upper); | ||
engine.discard(idx); | ||
return dist(engine); | ||
} else { | ||
thrust::default_random_engine engine; | ||
thrust::uniform_real_distribution<T> dist(lower, upper); | ||
engine.discard(idx); | ||
return dist(engine); | ||
} | ||
} | ||
}; | ||
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} // namespace | ||
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std::unique_ptr<cudf::column> generate_random_string_column(cudf::size_type lower, | ||
cudf::size_type upper, | ||
cudf::size_type num_rows, | ||
rmm::cuda_stream_view stream, | ||
rmm::device_async_resource_ref mr) | ||
{ | ||
CUDF_FUNC_RANGE(); | ||
auto offsets_begin = cudf::detail::make_counting_transform_iterator( | ||
0, random_number_generator<cudf::size_type>(lower, upper)); | ||
auto [offsets_column, computed_bytes] = cudf::strings::detail::make_offsets_child_column( | ||
offsets_begin, offsets_begin + num_rows, stream, mr); | ||
rmm::device_uvector<char> chars(computed_bytes, stream); | ||
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auto const offset_itr = | ||
cudf::detail::offsetalator_factory::make_input_iterator(offsets_column->view()); | ||
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// We generate the strings in parallel into the `chars` vector using the | ||
// offsets vector generated above. | ||
thrust::for_each_n(rmm::exec_policy(stream), | ||
thrust::make_zip_iterator(offset_itr, offset_itr + 1), | ||
num_rows, | ||
random_string_generator(chars.data())); | ||
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return cudf::make_strings_column( | ||
num_rows, std::move(offsets_column), chars.release(), 0, rmm::device_buffer{}); | ||
} | ||
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template <typename T> | ||
std::unique_ptr<cudf::column> generate_random_numeric_column(T lower, | ||
T upper, | ||
cudf::size_type num_rows, | ||
rmm::cuda_stream_view stream, | ||
rmm::device_async_resource_ref mr) | ||
{ | ||
CUDF_FUNC_RANGE(); | ||
auto col = cudf::make_numeric_column( | ||
cudf::data_type{cudf::type_to_id<T>()}, num_rows, cudf::mask_state::UNALLOCATED, stream, mr); | ||
cudf::size_type begin = 0; | ||
cudf::size_type end = num_rows; | ||
thrust::transform(rmm::exec_policy(stream), | ||
thrust::make_counting_iterator(begin), | ||
thrust::make_counting_iterator(end), | ||
col->mutable_view().begin<T>(), | ||
random_number_generator<T>(lower, upper)); | ||
return col; | ||
} | ||
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template std::unique_ptr<cudf::column> generate_random_numeric_column<int8_t>( | ||
int8_t lower, | ||
int8_t upper, | ||
cudf::size_type num_rows, | ||
rmm::cuda_stream_view stream, | ||
rmm::device_async_resource_ref mr); | ||
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template std::unique_ptr<cudf::column> generate_random_numeric_column<int16_t>( | ||
int16_t lower, | ||
int16_t upper, | ||
cudf::size_type num_rows, | ||
rmm::cuda_stream_view stream, | ||
rmm::device_async_resource_ref mr); | ||
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template std::unique_ptr<cudf::column> generate_random_numeric_column<cudf::size_type>( | ||
cudf::size_type lower, | ||
cudf::size_type upper, | ||
cudf::size_type num_rows, | ||
rmm::cuda_stream_view stream, | ||
rmm::device_async_resource_ref mr); | ||
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template std::unique_ptr<cudf::column> generate_random_numeric_column<double>( | ||
double lower, | ||
double upper, | ||
cudf::size_type num_rows, | ||
rmm::cuda_stream_view stream, | ||
rmm::device_async_resource_ref mr); | ||
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std::unique_ptr<cudf::column> generate_primary_key_column(cudf::scalar const& start, | ||
cudf::size_type num_rows, | ||
rmm::cuda_stream_view stream, | ||
rmm::device_async_resource_ref mr) | ||
{ | ||
CUDF_FUNC_RANGE(); | ||
return cudf::sequence(num_rows, start, stream, mr); | ||
} | ||
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std::unique_ptr<cudf::column> generate_repeat_string_column(std::string const& value, | ||
cudf::size_type num_rows, | ||
rmm::cuda_stream_view stream, | ||
rmm::device_async_resource_ref mr) | ||
{ | ||
CUDF_FUNC_RANGE(); | ||
auto const scalar = cudf::string_scalar(value); | ||
return cudf::make_column_from_scalar(scalar, num_rows, stream, mr); | ||
} | ||
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std::unique_ptr<cudf::column> generate_random_string_column_from_set( | ||
cudf::host_span<const char* const> set, | ||
cudf::size_type num_rows, | ||
rmm::cuda_stream_view stream, | ||
rmm::device_async_resource_ref mr) | ||
{ | ||
CUDF_FUNC_RANGE(); | ||
// Build a gather map of random strings to choose from | ||
// The size of the string sets always fits within 16-bit integers | ||
auto const indices = | ||
generate_primary_key_column(cudf::numeric_scalar<int16_t>(0), set.size(), stream, mr); | ||
auto const keys = cudf::test::strings_column_wrapper(set.begin(), set.end()).release(); | ||
auto const gather_map = cudf::table_view({indices->view(), keys->view()}); | ||
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// Build a column of random keys to gather from the set | ||
auto const gather_keys = | ||
generate_random_numeric_column<int16_t>(0, set.size() - 1, num_rows, stream, mr); | ||
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// Perform the gather operation | ||
auto const gathered_table = cudf::gather( | ||
gather_map, gather_keys->view(), cudf::out_of_bounds_policy::DONT_CHECK, stream, mr); | ||
auto gathered_table_columns = gathered_table->release(); | ||
return std::move(gathered_table_columns[1]); | ||
} | ||
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template <typename T> | ||
std::unique_ptr<cudf::column> generate_repeat_sequence_column(T seq_length, | ||
bool zero_indexed, | ||
cudf::size_type num_rows, | ||
rmm::cuda_stream_view stream, | ||
rmm::device_async_resource_ref mr) | ||
{ | ||
CUDF_FUNC_RANGE(); | ||
auto pkey = | ||
generate_primary_key_column(cudf::numeric_scalar<cudf::size_type>(0), num_rows, stream, mr); | ||
auto repeat_seq_zero_indexed = cudf::binary_operation(pkey->view(), | ||
cudf::numeric_scalar<T>(seq_length), | ||
cudf::binary_operator::MOD, | ||
cudf::data_type{cudf::type_to_id<T>()}, | ||
stream, | ||
mr); | ||
if (zero_indexed) { return repeat_seq_zero_indexed; } | ||
return cudf::binary_operation(repeat_seq_zero_indexed->view(), | ||
cudf::numeric_scalar<T>(1), | ||
cudf::binary_operator::ADD, | ||
cudf::data_type{cudf::type_to_id<T>()}, | ||
stream, | ||
mr); | ||
} | ||
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template std::unique_ptr<cudf::column> generate_repeat_sequence_column<int8_t>( | ||
int8_t seq_length, | ||
bool zero_indexed, | ||
cudf::size_type num_rows, | ||
rmm::cuda_stream_view stream, | ||
rmm::device_async_resource_ref mr); | ||
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template std::unique_ptr<cudf::column> generate_repeat_sequence_column<cudf::size_type>( | ||
cudf::size_type seq_length, | ||
bool zero_indexed, | ||
cudf::size_type num_rows, | ||
rmm::cuda_stream_view stream, | ||
rmm::device_async_resource_ref mr); | ||
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} // namespace cudf::datagen |
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