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#include "../../hnswlib/hnswlib.h"
#include <thread>
// Multithreaded executor
// The helper function copied from python_bindings/bindings.cpp (and that itself is copied from nmslib)
// An alternative is using #pragme omp parallel for or any other C++ threading
template<class Function>
inline void ParallelFor(size_t start, size_t end, size_t numThreads, Function fn) {
if (numThreads <= 0) {
numThreads = std::thread::hardware_concurrency();
}
if (numThreads == 1) {
for (size_t id = start; id < end; id++) {
fn(id, 0);
}
} else {
std::vector<std::thread> threads;
std::atomic<size_t> current(start);
// keep track of exceptions in threads
// https://stackoverflow.com/a/32428427/1713196
std::exception_ptr lastException = nullptr;
std::mutex lastExceptMutex;
for (size_t threadId = 0; threadId < numThreads; ++threadId) {
threads.push_back(std::thread([&, threadId] {
while (true) {
size_t id = current.fetch_add(1);
if (id >= end) {
break;
}
try {
fn(id, threadId);
} catch (...) {
std::unique_lock<std::mutex> lastExcepLock(lastExceptMutex);
lastException = std::current_exception();
/*
* This will work even when current is the largest value that
* size_t can fit, because fetch_add returns the previous value
* before the increment (what will result in overflow
* and produce 0 instead of current + 1).
*/
current = end;
break;
}
}
}));
}
for (auto &thread : threads) {
thread.join();
}
if (lastException) {
std::rethrow_exception(lastException);
}
}
}
int main() {
int dim = 16; // Dimension of the elements
int max_elements = 10000; // Maximum number of elements, should be known beforehand
int M = 16; // Tightly connected with internal dimensionality of the data
// strongly affects the memory consumption
int ef_construction = 200; // Controls index search speed/build speed tradeoff
int num_threads = 20; // Number of threads for operations with index
// Initing index
hnswlib::L2Space space(dim);
hnswlib::HierarchicalNSW<float>* alg_hnsw = new hnswlib::HierarchicalNSW<float>(&space, max_elements, M, ef_construction);
// Generate random data
std::mt19937 rng;
rng.seed(47);
std::uniform_real_distribution<> distrib_real;
float* data = new float[dim * max_elements];
for (int i = 0; i < dim * max_elements; i++) {
data[i] = distrib_real(rng);
}
// Add data to index
ParallelFor(0, max_elements, num_threads, [&](size_t row, size_t threadId) {
alg_hnsw->addPoint((void*)(data + dim * row), row);
});
// Query the elements for themselves and measure recall
std::vector<hnswlib::labeltype> neighbors(max_elements);
ParallelFor(0, max_elements, num_threads, [&](size_t row, size_t threadId) {
std::priority_queue<std::pair<float, hnswlib::labeltype>> result = alg_hnsw->searchKnn(data + dim * row, 1);
hnswlib::labeltype label = result.top().second;
neighbors[row] = label;
});
float correct = 0;
for (int i = 0; i < max_elements; i++) {
hnswlib::labeltype label = neighbors[i];
if (label == i) correct++;
}
float recall = correct / max_elements;
std::cout << "Recall: " << recall << "\n";
delete[] data;
delete alg_hnsw;
return 0;
}
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