#include #include #include #include #include #include #include #include #include #include #include #include #include #include "velographx/incremental/triangles.hpp" #include "velographx/storage/dynamic_graph.hpp" namespace { using Edge = std::pair; using Adj = std::vector>; using Clock = std::chrono::steady_clock; std::vector load_edges(const std::string& path, std::size_t& vertex_count) { std::ifstream in(path); if (!in) throw std::runtime_error("failed to open edge list: " + path); std::vector edges; velographx::VertexId max_vertex = 0; bool saw_edge = false; std::string line; while (std::getline(in, line)) { const auto first = line.find_first_not_of(" \t\r\n"); if (first == std::string::npos || line[first] == '#') continue; std::istringstream row(line); velographx::VertexId u = 0, v = 0; if (!(row >> u >> v)) throw std::runtime_error("malformed edge-list row"); if (u == v) continue; if (u > v) std::swap(u, v); edges.emplace_back(u, v); max_vertex = std::max(max_vertex, std::max(u, v)); saw_edge = true; } std::sort(edges.begin(), edges.end()); edges.erase(std::unique(edges.begin(), edges.end()), edges.end()); vertex_count = saw_edge ? static_cast(max_vertex) + 1 : 0; return edges; } std::uint64_t edge_key(velographx::VertexId u, velographx::VertexId v) { if (u > v) std::swap(u, v); return (static_cast(u) << 32U) | static_cast(v); } velographx::DynamicGraph make_vx_graph(std::size_t n, const std::vector& edges) { velographx::DynamicGraph graph(n, false); velographx::UpdateBatch seed; seed.updates.reserve(edges.size()); for (const auto& [u, v] : edges) seed.add(u, v); graph.apply(seed); graph.compact(); return graph; } velographx::UpdateBatch make_updates(const velographx::DynamicGraph& graph, std::size_t requested, std::uint64_t seed) { velographx::UpdateBatch batch; batch.updates.reserve(requested); std::unordered_set selected; selected.reserve(requested * 2 + 1); const auto n = static_cast(graph.vertex_count()); std::uint64_t state = seed | 1ULL; const std::size_t max_attempts = std::max(requested * 200, 10000); std::size_t attempts = 0; while (batch.updates.size() < requested && attempts++ < max_attempts) { state = state * 6364136223846793005ULL + 1442695040888963407ULL; const auto u = static_cast((state >> 17) % n); state = state * 6364136223846793005ULL + 1442695040888963407ULL; const auto v = static_cast((state >> 17) % n); if (u == v || graph.has_edge(u, v)) continue; const auto key = edge_key(u, v); if (selected.insert(key).second) batch.add(u, v); } if (batch.updates.size() != requested) { throw std::runtime_error("could not generate requested missing-edge batch"); } return batch; } Adj build_adjacency(std::size_t n, const std::vector& edges) { Adj adj(n); for (const auto& [u, v] : edges) { adj[u].push_back(v); adj[v].push_back(u); } for (auto& list : adj) { std::sort(list.begin(), list.end()); list.erase(std::unique(list.begin(), list.end()), list.end()); } return adj; } Adj build_update_adjacency(std::size_t n, const velographx::UpdateBatch& batch) { Adj delta(n); for (const auto& op : batch.updates) { if (!op.add) throw std::runtime_error("HiPC insertion comparator accepts insertions only"); delta[op.src].push_back(op.dst); delta[op.dst].push_back(op.src); } for (auto& list : delta) std::sort(list.begin(), list.end()); return delta; } void insert_sorted(std::vector& list, velographx::VertexId value) { auto it = std::lower_bound(list.begin(), list.end(), value); if (it == list.end() || *it != value) list.insert(it, value); } void apply_batch_to_adjacency(Adj& post, const velographx::UpdateBatch& batch) { for (const auto& op : batch.updates) { insert_sorted(post[op.src], op.dst); insert_sorted(post[op.dst], op.src); } } std::uint64_t intersection_count(const std::vector& a, const std::vector& b) { std::size_t i = 0, j = 0; std::uint64_t count = 0; while (i < a.size() && j < b.size()) { if (a[i] == b[j]) { ++count; ++i; ++j; } else if (a[i] < b[j]) { ++i; } else { ++j; } } return count; } // CPU reimplementation of the HiPC 2017 insertion inclusion-exclusion formulation. // The paper's Algorithm 2 computes three phases over the update edges: // S1: post/post intersections // S2: post/update intersections // S3: update/update intersections // and returns 1/2 * (S1 - S2 + S3/3) in its directed-edge accounting. // Here each undirected update edge is stored once, so S1 is half the paper's // directed representation while S2/S3 explicitly sum both orientations. The // equivalent undirected formula is: delta = S1 - S2/2 + S3/6. std::uint64_t hipc2017_insert_delta(const Adj& post, const Adj& delta, const velographx::UpdateBatch& batch, std::uint64_t& s1, std::uint64_t& s2, std::uint64_t& s3) { s1 = s2 = s3 = 0; for (const auto& op : batch.updates) { const auto u = op.src; const auto v = op.dst; s1 += intersection_count(post[u], post[v]); s2 += intersection_count(post[u], delta[v]); s2 += intersection_count(post[v], delta[u]); s3 += intersection_count(delta[u], delta[v]); s3 += intersection_count(delta[v], delta[u]); } if ((s2 % 2U) != 0U || (s3 % 6U) != 0U) { throw std::runtime_error("HiPC inclusion-exclusion accounting invariant failed"); } return s1 - s2 / 2U + s3 / 6U; } long long ns(Clock::time_point begin, Clock::time_point end) { return std::chrono::duration_cast(end - begin).count(); } } // namespace int main(int argc, char** argv) { if (argc < 2 || argc > 4) { std::cerr << "usage: hipc2017_cpu_reimplementation [repeats] [fractions-csv]\n"; return 2; } const std::string dataset = argv[1]; const int repeats = argc >= 3 ? std::stoi(argv[2]) : 5; if (repeats <= 0) return 2; std::vector fractions{0.01, 0.05, 0.10}; if (argc == 4) { fractions.clear(); std::istringstream input(argv[3]); std::string token; while (std::getline(input, token, ',')) { const double f = std::stod(token); if (!(f > 0.0) || !std::isfinite(f)) return 2; fractions.push_back(f); } } std::size_t vertex_count = 0; const auto edges = load_edges(dataset, vertex_count); if (edges.empty()) return 2; const Adj base_adj = build_adjacency(vertex_count, edges); std::cout << "dataset,vertices,base_edges,update_fraction,requested_edges,repeat," "velographx_update_ns,velographx_full_recompute_ns," "hipc_graph_update_ns,hipc_analytic_ns,hipc_answer_ready_ns," "velographx_updates_per_s,hipc_answer_ready_updates_per_s," "initial_triangles,velographx_triangles,hipc_triangles,recomputed_triangles," "hipc_s1,hipc_s2,hipc_s3,correct\n"; for (double fraction : fractions) { const std::size_t requested = std::max( 1, static_cast(std::ceil(static_cast(edges.size()) * fraction))); for (int repeat = 0; repeat < repeats; ++repeat) { auto graph = make_vx_graph(vertex_count, edges); velographx::IncrementalTriangleCount triangles(graph); const auto initial_value = triangles.value(); const auto update = make_updates( graph, requested, 0x9e3779b97f4a7c15ULL ^ (static_cast(repeat + 1) * 0xbf58476d1ce4e5b9ULL) ^ static_cast(requested)); const auto vx_begin = Clock::now(); triangles.apply(update); const auto vx_end = Clock::now(); const auto vx_value = triangles.value(); const auto full_begin = Clock::now(); triangles.recompute(); const auto full_end = Clock::now(); const auto full_value = triangles.value(); Adj post = base_adj; const Adj delta = build_update_adjacency(vertex_count, update); const auto hipc_update_begin = Clock::now(); apply_batch_to_adjacency(post, update); const auto hipc_update_end = Clock::now(); std::uint64_t s1 = 0, s2 = 0, s3 = 0; const auto hipc_analytic_begin = Clock::now(); const auto added = hipc2017_insert_delta(post, delta, update, s1, s2, s3); const auto hipc_analytic_end = Clock::now(); const auto hipc_value = initial_value + added; const auto vx_ns = ns(vx_begin, vx_end); const auto full_ns = ns(full_begin, full_end); const auto hipc_update_ns = ns(hipc_update_begin, hipc_update_end); const auto hipc_analytic_ns = ns(hipc_analytic_begin, hipc_analytic_end); const auto hipc_ready_ns = hipc_update_ns + hipc_analytic_ns; const bool correct = vx_value == full_value && hipc_value == full_value; if (!correct) { std::cerr << "exact-count disagreement: VeloGraphX=" << vx_value << " HiPC-derived=" << hipc_value << " full=" << full_value << '\n'; return 1; } const double vx_rate = vx_ns > 0 ? static_cast(requested) * 1e9 / vx_ns : 0.0; const double hipc_rate = hipc_ready_ns > 0 ? static_cast(requested) * 1e9 / hipc_ready_ns : 0.0; std::cout << dataset << ',' << vertex_count << ',' << edges.size() << ',' << fraction << ',' << requested << ',' << repeat << ',' << vx_ns << ',' << full_ns << ',' << hipc_update_ns << ',' << hipc_analytic_ns << ',' << hipc_ready_ns << ',' << vx_rate << ',' << hipc_rate << ',' << initial_value << ',' << vx_value << ',' << hipc_value << ',' << full_value << ',' << s1 << ',' << s2 << ',' << s3 << ',' << (correct ? 1 : 0) << '\n'; } } return 0; }