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device_subsets_example.cu
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device_subsets_example.cu
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/*
* Copyright (c) 2023-2024, 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_ref.cuh>
#include <cuco/storage.cuh>
#include <cuda/std/array>
#include <thrust/device_vector.h>
#include <thrust/reduce.h>
#include <thrust/scan.h>
#include <cooperative_groups.h>
#include <algorithm>
#include <cstddef>
#include <iostream>
#include <numeric>
/**
* @file device_subsets_example.cu
* @brief Demonstrates how to use one bulk set storage to create multiple subsets and perform
* individual operations via device-side ref APIs.
*
* To optimize memory usage, especially when dealing with expensive data allocation and multiple
* hashsets, a practical solution involves employing a single bulk storage for generating subsets.
* This eliminates the need for separate memory allocation and deallocation for each container. This
* can be achieved by using the lightweight non-owning ref type.
*
* @note This example is for demonstration purposes only. It is not intended to show the most
* performant way to do the example algorithm.
*/
auto constexpr cg_size = 8; ///< A CUDA Cooperative Group of 8 threads to handle each subset
auto constexpr window_size = 1; ///< Number of concurrent slots handled by each thread
auto constexpr N = 10; ///< Number of elements to insert and query
using key_type = int; ///< Key type
using probing_scheme_type =
cuco::linear_probing<cg_size,
cuco::default_hash_function<key_type>>; ///< Type controls CG granularity
///< and probing scheme (linear
///< probing v.s. double hashing)
/// Type of bulk allocation storage
using storage_type = cuco::aow_storage<key_type, window_size>;
/// Lightweight non-owning storage ref type
using storage_ref_type = typename storage_type::ref_type;
using ref_type = cuco::static_set_ref<key_type,
cuda::thread_scope_device,
thrust::equal_to<key_type>,
probing_scheme_type,
storage_ref_type>; ///< Set ref type
/// Sample data to insert and query
__device__ constexpr cuda::std::array<key_type, N> data = {1, 3, 5, 7, 9, 11, 13, 15, 17, 19};
/// Empty slots are represented by reserved "sentinel" values. These values should be selected such
/// that they never occur in your input data.
key_type constexpr empty_key_sentinel = -1;
/**
* @brief Inserts sample data into subsets by using cooperative group
*
* Each Cooperative Group creates its own subset and inserts `N` sample data.
*
* @param set_refs Pointer to the array of subset objects
*/
__global__ void insert(ref_type* set_refs)
{
namespace cg = cooperative_groups;
auto const tile = cg::tiled_partition<cg_size>(cg::this_thread_block());
// Get subset (or CG) index
auto const idx = (blockDim.x * blockIdx.x + threadIdx.x) / cg_size;
auto raw_set_ref = *(set_refs + idx);
auto insert_set_ref = raw_set_ref.rebind_operators(cuco::insert);
// Insert `N` elemtns into the set with CG insert
for (int i = 0; i < N; i++) {
insert_set_ref.insert(tile, data[i]);
}
}
/**
* @brief All inserted data can be found
*
* Each Cooperative Group reconstructs its own subset ref based on the storage parameters and
* verifies all inserted data can be found.
*
* @param set_refs Pointer to the array of subset objects
*/
__global__ void find(ref_type* set_refs)
{
namespace cg = cooperative_groups;
auto const tile = cg::tiled_partition<cg_size>(cg::this_thread_block());
auto const idx = (blockDim.x * blockIdx.x + threadIdx.x) / cg_size;
auto raw_set_ref = *(set_refs + idx);
auto find_set_ref = raw_set_ref.rebind_operators(cuco::find);
// Result denoting if any of the inserted data is not found
__shared__ int result;
if (threadIdx.x == 0) { result = 0; }
__syncthreads();
for (int i = 0; i < N; i++) {
// Query the set with inserted data
auto const found = find_set_ref.find(tile, data[i]);
// Record if the inserted data has been found
atomicOr(&result, *found != data[i]);
}
__syncthreads();
if (threadIdx.x == 0) {
// If the result is still 0, all inserted data are found.
if (result == 0) { printf("Success! Found all inserted elements.\n"); }
}
}
int main()
{
// Number of subsets to be created
auto constexpr num = 16;
// Each subset may have a different requested size
auto constexpr subset_sizes =
std::array<std::size_t, num>{20, 20, 20, 20, 30, 30, 30, 30, 40, 40, 40, 40, 50, 50, 50, 50};
auto valid_sizes = std::vector<std::size_t>();
valid_sizes.reserve(num);
for (size_t i = 0; i < num; ++i) {
valid_sizes.emplace_back(
static_cast<std::size_t>(cuco::make_window_extent<ref_type>(subset_sizes[i])));
}
std::vector<std::size_t> offsets(num + 1, 0);
// prefix sum to compute offsets and total number of windows
std::size_t current_sum = 0;
for (std::size_t i = 0; i < valid_sizes.size(); ++i) {
current_sum += valid_sizes[i];
offsets[i + 1] = current_sum;
}
// total number of windows is located at the back of the offsets array
auto const total_num_windows = offsets.back();
// Create a single bulk storage used by all subsets
auto set_storage = storage_type{total_num_windows};
// Initializes the storage with the given sentinel
set_storage.initialize(empty_key_sentinel);
std::vector<ref_type> set_refs;
// create subsets
for (std::size_t i = 0; i < num; ++i) {
storage_ref_type storage_ref{valid_sizes[i], set_storage.data() + offsets[i]};
set_refs.emplace_back(
ref_type{cuco::empty_key<key_type>{empty_key_sentinel}, {}, {}, {}, storage_ref});
}
thrust::device_vector<ref_type> d_set_refs(set_refs);
// Insert sample data
insert<<<1, 128>>>(d_set_refs.data().get());
// Find all inserted data
find<<<1, 128>>>(d_set_refs.data().get());
return 0;
}