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6b92ff7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | #pragma once
#include <gpu/common.h>
#include <gpu/gpu_memory.h>
namespace cubvh {
// --- N-dim integer static hash table ---
// This module implements a minimal open-addressed hash table for integer ND coordinates on CUDA.
// Note: this is a STATIC hashtable, only build it once and use it for queries!
//
// Shapes and conventions (all buffers are device pointers with int32 entries unless stated otherwise):
// - coords: [N * D] layout contiguous as D-tuple per row (default D=3 for 3D).
// - queries: [M * D] layout contiguous as D-tuple per row.
// - table_kvs: [capacity * 2]; for slot s:
// table_kvs[2*s + 0] = slot marker (-1 means empty; any other value means occupied)
// table_kvs[2*s + 1] = row index into coords (0..N-1), or -1 if not set
// - out_indices: [M]; contains row index into coords or -1 if not found.
// Table layout: flattened array of length 2 * capacity (int32)
// - table_kvs[2*slot + 0]: slot marker; -1 means empty; any other value means occupied
// - table_kvs[2*slot + 1]: index into coords (row index)
// --- Hash helper (CityHash-like mix for 32-bit ints) ---
// Used to map a small fixed-length integer key to a slot in [0, capacity).
__device__ inline uint32_t _hash_city32_step(uint32_t hash_val, uint32_t key) {
hash_val += key * 0x9E3779B9u;
hash_val ^= hash_val >> 16;
hash_val *= 0x85EBCA6Bu;
hash_val ^= hash_val >> 13;
hash_val *= 0xC2B2AE35u;
hash_val ^= hash_val >> 16;
return hash_val;
}
struct CityHash {
// key: pointer to first element; key_dim: number of ints (here 3 in practice)
// capacity: table capacity (number of slots)
__device__ inline static int hash(const int* key, int key_dim, int capacity) {
uint32_t h = 0u;
for (int i = 0; i < key_dim; ++i) {
h = _hash_city32_step(h, (uint32_t)key[i]);
}
int signed_h = (int)h;
// ensure non-negative modulo
int slot = signed_h % capacity;
if (slot < 0) slot += capacity;
return slot;
}
};
__device__ inline bool _vec_equal(const int* a, const int* b, int dim) {
for (int i = 0; i < dim; ++i) {
if (a[i] != b[i]) return false;
}
return true;
}
// N-D key search in the hash table (default D=3)
__device__ inline int _search_hash_table(
const int* __restrict__ table_kvs,
const int* __restrict__ coords,
const int* __restrict__ query_key,
int table_capacity,
int num_dims
) {
// query_key: [D]
// coords: [N * D]
// table_kvs: [capacity * 2]
int slot = CityHash::hash(query_key, num_dims, table_capacity);
const int begin = slot;
int attempts = 0;
while (attempts < table_capacity) {
const int marker = table_kvs[slot * 2 + 0];
if (marker == -1) {
return -1; // empty slot encountered => not present
}
const int vec_idx = table_kvs[slot * 2 + 1];
if (vec_idx != -1) {
const int* candidate = &coords[vec_idx * num_dims];
if (_vec_equal(candidate, query_key, num_dims)) {
return vec_idx;
}
}
slot = (slot + 1) % table_capacity;
if (slot == begin) return -1; // full cycle
++attempts;
}
return -1;
}
// --- Kernels ---
// Initialize table: set both key marker and value index to -1
// table_kvs: [capacity * 2]
__global__ void prepare_key_value_pairs_kernel(int* table_kvs, int capacity) {
const int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < capacity) {
table_kvs[2 * tid + 0] = -1;
table_kvs[2 * tid + 1] = -1;
}
}
// Insert a batch of N-D coords (default D=3)
// table_kvs: [capacity * 2]
// coords: [num_keys * num_dims]
// num_keys: N (rows in coords)
__global__ void insert_kernel(
int* __restrict__ table_kvs,
const int* __restrict__ coords,
int num_keys,
int num_dims,
int table_capacity
) {
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= num_keys) return;
const int* key = &coords[idx * num_dims];
int slot = CityHash::hash(key, num_dims, table_capacity);
const int begin = slot;
int attempts = 0;
while (attempts < table_capacity) {
int* marker_ptr = &table_kvs[slot * 2 + 0];
const int prev = atomicCAS(marker_ptr, -1, slot);
if (prev == -1) {
table_kvs[slot * 2 + 1] = idx; // publish index
return;
}
slot = (slot + 1) % table_capacity;
if (slot == begin) return; // table full or no free slot
++attempts;
}
}
// Search kernel for arbitrary queries (N-D; default D=3)
// table_kvs: [capacity * 2]
// coords: [N * num_dims]
// queries: [M * num_dims]
// out_indices: [M]
__global__ void search_kernel(
const int* __restrict__ table_kvs,
const int* __restrict__ coords,
const int* __restrict__ queries,
int num_queries,
int table_capacity,
int num_dims,
int* __restrict__ out_indices
) {
const int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= num_queries) return;
const int* q = &queries[idx * num_dims];
out_indices[idx] = _search_hash_table(table_kvs, coords, q, table_capacity, num_dims);
}
// Lightweight RAII helper for a device hash table backing storage
struct HashTableInt {
// Device-side storage for the hash table key-value slots.
// Layout: [capacity * 2] (see top-level description for meaning of each entry)
GPUMemory<int> table_kvs; // length = 2 * capacity
int capacity = 0;
const int* d_coords = nullptr; // external storage of keys [num_coords * num_dims]
int num_coords = 0;
int num_dims = 3; // default dimension
void resize(int new_capacity) {
capacity = new_capacity;
table_kvs.resize((size_t)capacity * 2);
}
// Set number of dimensions D (default 3)
void set_num_dims(int d) { num_dims = d; }
// Initialize/prepare table (set all slots to -1)
// table_kvs: [capacity * 2]
void prepare(cudaStream_t stream) {
if (capacity <= 0) return;
const uint32_t tpb = 256u;
const uint32_t blocks = (uint32_t)div_round_up(capacity, (int)tpb);
prepare_key_value_pairs_kernel<<<blocks, tpb, 0, stream>>>(table_kvs.data(), capacity);
}
// Insert a batch of coords
// d_coords_in: [n_keys * num_dims]
void insert(const int* d_coords_in, int n_keys, cudaStream_t stream) {
d_coords = d_coords_in;
num_coords = n_keys;
if (capacity <= 0 || n_keys <= 0) return;
const uint32_t tpb = 256u;
const uint32_t blocks = (uint32_t)div_round_up(n_keys, (int)tpb);
insert_kernel<<<blocks, tpb, 0, stream>>>(table_kvs.data(), d_coords, n_keys, num_dims, capacity);
}
// Build convenience: set capacity, prepare, then insert in one call
// d_coords_in: [n_keys * num_dims]
void build(const int* d_coords_in, int n_keys, cudaStream_t stream) {
// initialize capacity = max(16, 2 * n_keys)
int desired_capacity = n_keys * 2;
if (desired_capacity < 16) desired_capacity = 16;
resize(desired_capacity);
prepare(stream);
insert(d_coords_in, n_keys, stream);
}
// Search a batch of queries; writes index or -1 per query
// d_queries: [n_queries * num_dims]
// d_out_indices: [n_queries]
void search(const int* d_queries, int n_queries, int* d_out_indices, cudaStream_t stream) const {
if (capacity <= 0 || n_queries <= 0) return;
const uint32_t tpb = 256u;
const uint32_t blocks = (uint32_t)div_round_up(n_queries, (int)tpb);
search_kernel<<<blocks, tpb, 0, stream>>>(table_kvs.data(), d_coords, d_queries, n_queries, capacity, num_dims, d_out_indices);
}
};
} // namespace cubvh |