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mapping_table_example.cu
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mapping_table_example.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.
*/
#include <cuco/static_set.cuh>
#include <cuda/std/array>
#include <cuda/std/tuple>
#include <thrust/device_vector.h>
#include <iostream>
/**
* @file mapping_table_example.cu
*
* @brief Demonstrates how to use hash set as a lookup table of the original data
*
* `cuco` hash tables such as `cuco::static_set` or `cuco::static_map` support only 4/8 byte keys.
* This limitation arises because `cuco` hash tables rely on atomic Compare-And-Swap (CAS)
* operations for key insertions (or queries), and the hardware natively supports only 4-byte and
* 8-byte CAS. To enable support for larger keys, one approach is to implement atomic lock tables at
* the software level. However, this approach would lead to a notable performance decrease due to
* the high runtime cost of atomic lock tables.
*
* Additionally, `cuco` hash tables use open addressing as the hash collision resolution method.
* This approach requires users to provide a sentinel that indicates unused slots in the data
* structure. The sentinel value is a reserved value that must be never present in the problem. Note
* that inserting or querying a sentinel value is undefined behavior. This can be problematic,
* especially when the input data type is complex and determining a valid sentinel value is not
* straightforward.
*
* This sample code demonstrates a solution to address these issues by using hash set as an
* indirection mapping table to the original data:
* - The keys inserted in the hash table are indices of the original data array.
* - Using `-1` as a sentinel value is safe because accessing `data[-1]` is invalid.
* - Custom hashers and key equality comparators are required to hash and compare original keys
* based on indices.
*
* @note This example is for demonstration purposes only. It is not intended to show the most
* performant way to do the example algorithm.
*/
/**
* @brief User-defined key equal to compare two keys
*
* @tparam T Original key type
*/
template <typename T>
struct my_equal {
my_equal(T const* data) : _data{data} {}
/**
* @brief Checks if two keys are identical based on their indices in the
* original data array
*
* @param lhs The left hand side index
* @param rhs The right hand side index
* @return 'true' if two tuples are identical
*/
__device__ constexpr bool operator()(int32_t lhs, int32_t rhs) const
{
// Check all 4 elements of a tuple to determine if two tuples are identical
return cuda::std::get<0>(_data[lhs]) == cuda::std::get<0>(_data[rhs]) and
cuda::std::get<1>(_data[lhs]) == cuda::std::get<1>(_data[rhs]) and
cuda::std::get<2>(_data[lhs]) == cuda::std::get<2>(_data[rhs]) and
cuda::std::get<3>(_data[lhs]) == cuda::std::get<3>(_data[rhs]);
}
T const* _data;
};
/**
* @brief User-defined hash function to hash the original data based on its index
*
* @tparam T Original key type
*/
template <typename T>
struct my_hasher {
my_hasher(T const* data) : _data{data} {}
__device__ auto operator()(int32_t index) const
{
// Only hashes the first element of a tuple for demonstration purposes
return cuda::std::get<0>(_data[index]);
}
T const* _data;
};
/**
* @brief Utility to print the content of a given `tuple`
*
* @tparam T Type of the tuple
*/
template <typename T>
void print(T const& tuple)
{
std::cout << "[" << cuda::std::get<0>(tuple) << ", " << cuda::std::get<1>(tuple) << ", "
<< cuda::std::get<2>(tuple) << ", "
<< "[" << cuda::std::get<3>(tuple)[0] << ", " << cuda::std::get<3>(tuple)[1] << ", "
<< cuda::std::get<3>(tuple)[2] << ", " << cuda::std::get<3>(tuple)[3] << "]]\n";
}
int main(void)
{
// The original key type is larger than 8-byte and complex to spell the full type name
using Key = cuda::std::tuple<uint32_t, char, bool, cuda::std::array<double, 4UL>>;
// Imagine the array size is huge or the key type is more complex, it becomes impossible to
// determine a valid sentinel value without introspecting the data
auto const h_data =
std::vector<Key>{cuda::std::tuple{11u, 'a', true, cuda::std::array{1., 2., 3., 4.}},
cuda::std::tuple{11u, 'a', true, cuda::std::array{1., 2., 3., 4.}},
cuda::std::tuple{22u, 'b', true, cuda::std::array{5., 6., 7., 8.}},
cuda::std::tuple{11u, 'a', true, cuda::std::array{5., 6., 7., 8.}},
cuda::std::tuple{11u, 'a', false, cuda::std::array{1., 2., 3., 4.}}};
auto const size = h_data.size();
thrust::device_vector<Key> d_data{h_data};
// The actual key type is an index type, `int32_t` is large enough to cover the whole input range
// and 4-byte atomic CAS is more efficient than the 8-byte one.
using ActualKey = int32_t;
// `-1` is a valid sentinel value since one will never access `data[-1]`
ActualKey constexpr empty_key_sentinel = -1;
auto const data_ptr = d_data.data().get();
auto set = cuco::static_set{cuco::extent<std::size_t>{size * 2}, // about 50% load factor
cuco::empty_key{empty_key_sentinel},
my_equal{data_ptr},
cuco::linear_probing<1, my_hasher<Key>>{my_hasher<Key>{data_ptr}}};
// The actual keys are indices of 5 elements
auto const actual_keys = thrust::device_vector<ActualKey>{0, 1, 2, 3, 4};
set.insert(actual_keys.begin(), actual_keys.end());
auto unique_keys = thrust::device_vector<ActualKey>(size);
auto const unique_keys_end = set.retrieve_all(unique_keys.begin());
auto const num = std::distance(unique_keys.begin(), unique_keys_end);
std::cout << "There are " << num << " distinct input elements:\n";
for (auto i = 0; i < num; ++i) {
// Retrieve query output based on indices
print(h_data[unique_keys[i]]);
}
return 0;
}