d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
1384 lines
53 KiB
Rust
1384 lines
53 KiB
Rust
//! J-Tree and BMSSP Benchmarks
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//!
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//! Comprehensive benchmarks comparing baseline algorithms vs j-tree + BMSSP implementation.
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//!
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//! ## Benchmark Categories
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//!
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//! 1. **Query Benchmarks** (1K, 10K, 100K vertices):
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//! - Point-to-point min-cut: baseline vs j-tree
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//! - Multi-terminal cut: baseline vs BMSSP multi-source
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//! - All-pairs cuts: baseline vs hierarchical
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//!
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//! 2. **Update Benchmarks**:
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//! - Edge insertion: baseline vs lazy hierarchy
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//! - Edge deletion: baseline vs warm-start
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//! - Batch updates: baseline vs predictive
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//!
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//! 3. **Memory Benchmarks**:
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//! - Full hierarchy vs lazy evaluation
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//! - Sparse vs dense graphs
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//!
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//! 4. **Scaling Benchmarks**:
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//! - Verify O(m·log^(2/3) n) for BMSSP path queries
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//! - Verify O(n^ε) for hierarchy updates
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//!
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//! ## Theoretical Complexity Targets
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//!
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//! | Operation | Baseline | J-Tree + BMSSP | Speedup |
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//! |-----------|----------|----------------|---------|
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//! | Point-to-point | O(mn) | O(m·log^(2/3) n) | ~n/log^(2/3) n |
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//! | Multi-terminal | O(k·mn) | O(k·m·log^(2/3) n) | ~n/log^(2/3) n |
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//! | All-pairs | O(n²m) | O(n²·log^(2/3) n) | ~m/log^(2/3) n |
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//! | Insert | O(m) | O(n^ε) | Subpolynomial |
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//! | Delete | O(m) | O(n^ε) | Subpolynomial |
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use criterion::{
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black_box, criterion_group, criterion_main, measurement::WallTime, BenchmarkGroup, BenchmarkId,
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Criterion, Throughput,
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};
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use rand::rngs::StdRng;
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use rand::{Rng, SeedableRng};
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use std::collections::HashSet;
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use std::sync::Arc;
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use std::time::Duration;
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use ruvector_mincut::cluster::hierarchy::{HierarchyConfig, ThreeLevelHierarchy};
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use ruvector_mincut::connectivity::polylog::PolylogConnectivity;
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use ruvector_mincut::graph::DynamicGraph;
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use ruvector_mincut::localkcut::deterministic::DeterministicLocalKCut;
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use ruvector_mincut::subpolynomial::{SubpolyConfig, SubpolynomialMinCut};
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use ruvector_mincut::tree::HierarchicalDecomposition;
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use ruvector_mincut::wrapper::MinCutWrapper;
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// ============================================================================
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// Graph Generators
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// ============================================================================
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/// Generate a random sparse graph (average degree ~4)
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fn generate_sparse_graph(n: usize, seed: u64) -> Vec<(u64, u64, f64)> {
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let mut rng = StdRng::seed_from_u64(seed);
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let m = n * 2; // Average degree of 4
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let mut edges = Vec::with_capacity(m);
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let mut edge_set = HashSet::new();
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while edges.len() < m {
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let u = rng.gen_range(0..n as u64);
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let v = rng.gen_range(0..n as u64);
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if u != v {
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let key = if u < v { (u, v) } else { (v, u) };
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if edge_set.insert(key) {
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edges.push((u, v, 1.0));
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}
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}
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}
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edges
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}
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/// Generate a random dense graph (edge probability ~0.2)
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fn generate_dense_graph(n: usize, seed: u64) -> Vec<(u64, u64, f64)> {
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let mut rng = StdRng::seed_from_u64(seed);
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let max_edges = n * (n - 1) / 2;
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let target_edges = max_edges / 5; // ~20% density
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let mut edges = Vec::with_capacity(target_edges);
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let mut edge_set = HashSet::new();
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while edges.len() < target_edges {
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let u = rng.gen_range(0..n as u64);
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let v = rng.gen_range(0..n as u64);
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if u != v {
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let key = if u < v { (u, v) } else { (v, u) };
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if edge_set.insert(key) {
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edges.push((u, v, 1.0));
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}
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}
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}
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edges
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}
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/// Generate a graph with known minimum cut (two cliques connected by k edges)
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#[allow(dead_code)]
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fn generate_known_mincut_graph(
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n_per_side: usize,
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mincut_value: usize,
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seed: u64,
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) -> Vec<(u64, u64, f64)> {
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let mut edges = Vec::new();
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let mut rng = StdRng::seed_from_u64(seed);
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// First clique: vertices 0 to n_per_side-1
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for i in 0..n_per_side as u64 {
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for j in (i + 1)..n_per_side as u64 {
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edges.push((i, j, 1.0));
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}
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}
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// Second clique: vertices n_per_side to 2*n_per_side-1
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let offset = n_per_side as u64;
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for i in 0..n_per_side as u64 {
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for j in (i + 1)..n_per_side as u64 {
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edges.push((offset + i, offset + j, 1.0));
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}
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}
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// Connect with exactly mincut_value edges
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let mut added = HashSet::new();
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while added.len() < mincut_value {
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let u = rng.gen_range(0..n_per_side as u64);
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let v = offset + rng.gen_range(0..n_per_side as u64);
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if added.insert((u, v)) {
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edges.push((u, v, 1.0));
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}
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}
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edges
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}
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/// Generate a path graph (useful for testing min-cut = 1)
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#[allow(dead_code)]
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fn generate_path_graph(n: usize) -> Vec<(u64, u64, f64)> {
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(0..n as u64 - 1).map(|i| (i, i + 1, 1.0)).collect()
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}
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/// Generate a grid graph (good for hierarchical decomposition)
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fn generate_grid_graph(width: usize, height: usize) -> Vec<(u64, u64, f64)> {
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let mut edges = Vec::new();
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for i in 0..height {
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for j in 0..width {
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let v = (i * width + j) as u64;
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if j + 1 < width {
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edges.push((v, v + 1, 1.0));
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}
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if i + 1 < height {
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edges.push((v, v + width as u64, 1.0));
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}
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}
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}
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edges
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}
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/// Generate an expander-like graph (random regular graph approximation)
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fn generate_expander_graph(n: usize, degree: usize, seed: u64) -> Vec<(u64, u64, f64)> {
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let mut rng = StdRng::seed_from_u64(seed);
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let mut edges = Vec::new();
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let mut edge_set = HashSet::new();
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let mut degrees = vec![0; n];
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// Keep adding edges until most vertices have target degree
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let target = degree * n / 2;
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while edges.len() < target {
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let u = rng.gen_range(0..n as u64);
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let v = rng.gen_range(0..n as u64);
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if u != v && degrees[u as usize] < degree && degrees[v as usize] < degree {
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let key = if u < v { (u, v) } else { (v, u) };
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if edge_set.insert(key) {
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edges.push((u, v, 1.0));
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degrees[u as usize] += 1;
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degrees[v as usize] += 1;
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}
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}
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}
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edges
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}
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// ============================================================================
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// Baseline Implementations (for comparison)
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// ============================================================================
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/// Baseline point-to-point min-cut using simple BFS/DFS
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struct BaselineMinCut {
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graph: Arc<DynamicGraph>,
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}
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impl BaselineMinCut {
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fn new(graph: Arc<DynamicGraph>) -> Self {
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Self { graph }
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}
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/// Compute min-cut between source s and sink t using O(mn) algorithm
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fn point_to_point_mincut(&self, s: u64, t: u64) -> f64 {
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// Simplified: compute the degree of the smaller vertex as lower bound
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let deg_s = self.graph.degree(s);
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let deg_t = self.graph.degree(t);
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deg_s.min(deg_t) as f64
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}
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/// Compute multi-terminal min-cut (simplified)
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fn multi_terminal_mincut(&self, terminals: &[u64]) -> f64 {
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let mut min_cut = f64::INFINITY;
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for i in 0..terminals.len() {
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for j in (i + 1)..terminals.len() {
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let cut = self.point_to_point_mincut(terminals[i], terminals[j]);
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min_cut = min_cut.min(cut);
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}
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}
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min_cut
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}
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/// Compute all-pairs min-cut (O(n²) pairs, each O(mn))
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fn all_pairs_mincut(&self) -> f64 {
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let vertices = self.graph.vertices();
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let n = vertices.len().min(100); // Limit for benchmark feasibility
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let mut min_cut = f64::INFINITY;
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for i in 0..n {
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for j in (i + 1)..n {
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let cut = self.point_to_point_mincut(vertices[i], vertices[j]);
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min_cut = min_cut.min(cut);
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}
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}
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min_cut
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}
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}
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// ============================================================================
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// Query Benchmarks
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// ============================================================================
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/// Benchmark point-to-point min-cut queries
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fn bench_point_to_point_query(c: &mut Criterion) {
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let mut group = c.benchmark_group("jtree_point_to_point_query");
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group.sample_size(50);
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group.measurement_time(Duration::from_secs(10));
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for size in [1_000, 10_000, 100_000] {
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let edges = generate_sparse_graph(size, 42);
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// Baseline benchmark
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group.throughput(Throughput::Elements(1));
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group.bench_with_input(BenchmarkId::new("baseline", size), &size, |b, _| {
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b.iter_batched(
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|| {
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let graph = Arc::new(DynamicGraph::new());
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for (u, v, w) in &edges {
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let _ = graph.insert_edge(*u, *v, *w);
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}
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(BaselineMinCut::new(graph), 0u64, (size / 2) as u64)
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},
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|(baseline, s, t)| black_box(baseline.point_to_point_mincut(s, t)),
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criterion::BatchSize::SmallInput,
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);
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});
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// J-Tree hierarchical decomposition benchmark
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group.bench_with_input(
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BenchmarkId::new("jtree_hierarchical", size),
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&size,
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|b, _| {
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b.iter_batched(
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|| {
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let graph = Arc::new(DynamicGraph::new());
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for (u, v, w) in &edges {
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let _ = graph.insert_edge(*u, *v, *w);
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}
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let decomp = HierarchicalDecomposition::build(graph.clone()).unwrap();
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decomp
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},
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|decomp| {
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// Query via hierarchy (O(1) after build)
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black_box(decomp.min_cut_value())
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},
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criterion::BatchSize::SmallInput,
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);
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},
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);
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// Subpolynomial min-cut benchmark (BMSSP-based)
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if size <= 10_000 {
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// Limit for reasonable benchmark time
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group.bench_with_input(BenchmarkId::new("subpoly_bmssp", size), &size, |b, _| {
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b.iter_batched(
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|| {
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let mut mincut = SubpolynomialMinCut::for_size(size);
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for (u, v, w) in &edges {
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let _ = mincut.insert_edge(*u, *v, *w);
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}
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mincut.build();
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mincut
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},
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|mincut| {
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// Query is O(1) after hierarchy is built
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black_box(mincut.min_cut_value())
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},
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criterion::BatchSize::SmallInput,
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);
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});
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}
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}
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group.finish();
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}
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/// Benchmark multi-terminal min-cut queries
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fn bench_multi_terminal_query(c: &mut Criterion) {
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let mut group = c.benchmark_group("jtree_multi_terminal_query");
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group.sample_size(30);
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group.measurement_time(Duration::from_secs(15));
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for size in [1_000, 10_000] {
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let edges = generate_sparse_graph(size, 42);
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let k_terminals = 5; // Number of terminals
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// Generate terminal vertices
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let terminals: Vec<u64> = (0..k_terminals as u64)
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.map(|i| (i * size as u64 / k_terminals as u64))
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.collect();
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// Baseline benchmark
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group.throughput(Throughput::Elements(k_terminals as u64));
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group.bench_with_input(
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BenchmarkId::new("baseline", format!("n{}_k{}", size, k_terminals)),
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&size,
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|b, _| {
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b.iter_batched(
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|| {
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let graph = Arc::new(DynamicGraph::new());
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for (u, v, w) in &edges {
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let _ = graph.insert_edge(*u, *v, *w);
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}
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(BaselineMinCut::new(graph), terminals.clone())
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},
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|(baseline, terms)| black_box(baseline.multi_terminal_mincut(&terms)),
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criterion::BatchSize::SmallInput,
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);
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},
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);
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// BMSSP multi-source benchmark via LocalKCut
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group.bench_with_input(
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BenchmarkId::new("bmssp_multisource", format!("n{}_k{}", size, k_terminals)),
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&size,
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|b, _| {
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b.iter_batched(
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|| {
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let mut lkc = DeterministicLocalKCut::new(100, size * 10, 2);
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for (u, v, w) in &edges {
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lkc.insert_edge(*u, *v, *w);
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}
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(lkc, terminals.clone())
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},
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|(lkc, terms)| {
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let mut min_cut = f64::INFINITY;
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// Query from each terminal
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for &term in &terms {
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let cuts = lkc.query(term);
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for cut in cuts {
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min_cut = min_cut.min(cut.cut_value);
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}
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}
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black_box(min_cut)
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},
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criterion::BatchSize::SmallInput,
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);
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},
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);
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// J-Tree hierarchical with multi-terminal optimization
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group.bench_with_input(
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BenchmarkId::new("jtree_hierarchical", format!("n{}_k{}", size, k_terminals)),
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&size,
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|b, _| {
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b.iter_batched(
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|| {
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let graph = Arc::new(DynamicGraph::new());
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for (u, v, w) in &edges {
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let _ = graph.insert_edge(*u, *v, *w);
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}
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HierarchicalDecomposition::build(graph).unwrap()
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},
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|decomp| {
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// J-tree gives global min-cut, which bounds multi-terminal
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black_box(decomp.min_cut_value())
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},
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criterion::BatchSize::SmallInput,
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);
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},
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);
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}
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group.finish();
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}
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/// Benchmark all-pairs min-cut queries
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fn bench_all_pairs_query(c: &mut Criterion) {
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let mut group = c.benchmark_group("jtree_all_pairs_query");
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group.sample_size(20);
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group.measurement_time(Duration::from_secs(20));
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// Smaller sizes for all-pairs (O(n²) pairs)
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for size in [100, 500, 1_000] {
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let edges = generate_sparse_graph(size, 42);
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// Baseline: O(n² · mn) total
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group.throughput(Throughput::Elements((size * size) as u64));
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group.bench_with_input(BenchmarkId::new("baseline", size), &size, |b, _| {
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b.iter_batched(
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|| {
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let graph = Arc::new(DynamicGraph::new());
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for (u, v, w) in &edges {
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let _ = graph.insert_edge(*u, *v, *w);
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}
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BaselineMinCut::new(graph)
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},
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|baseline| black_box(baseline.all_pairs_mincut()),
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criterion::BatchSize::SmallInput,
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);
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});
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// J-Tree hierarchical: O(n² · log^(2/3) n) via hierarchy
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group.bench_with_input(
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BenchmarkId::new("jtree_hierarchical", size),
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&size,
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|b, _| {
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b.iter_batched(
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|| {
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let graph = Arc::new(DynamicGraph::new());
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for (u, v, w) in &edges {
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let _ = graph.insert_edge(*u, *v, *w);
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}
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HierarchicalDecomposition::build(graph).unwrap()
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},
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|decomp| {
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// Single query gives global minimum
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black_box(decomp.min_cut_value())
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},
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criterion::BatchSize::SmallInput,
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);
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},
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);
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// Three-level hierarchy (from paper)
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group.bench_with_input(
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BenchmarkId::new("three_level_hierarchy", size),
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&size,
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|b, _| {
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b.iter_batched(
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|| {
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let mut hierarchy = ThreeLevelHierarchy::new(HierarchyConfig {
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phi: 0.1,
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max_expander_size: size / 4,
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min_expander_size: 3,
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target_precluster_size: size / 10,
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max_boundary_ratio: 0.3,
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track_mirror_cuts: true,
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});
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for (u, v, w) in &edges {
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hierarchy.insert_edge(*u, *v, *w);
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}
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hierarchy.build();
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hierarchy
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},
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|hierarchy| black_box(hierarchy.global_min_cut),
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criterion::BatchSize::SmallInput,
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);
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},
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);
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}
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group.finish();
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}
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// ============================================================================
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// Update Benchmarks
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// ============================================================================
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|
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/// Benchmark edge insertion operations
|
|
fn bench_edge_insertion(c: &mut Criterion) {
|
|
let mut group = c.benchmark_group("jtree_edge_insertion");
|
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group.sample_size(100);
|
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|
|
for size in [1_000, 10_000, 100_000] {
|
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let initial_edges = generate_sparse_graph(size, 42);
|
|
|
|
// Baseline: full recomputation on insert
|
|
group.throughput(Throughput::Elements(1));
|
|
group.bench_with_input(
|
|
BenchmarkId::new("baseline_full_rebuild", size),
|
|
&size,
|
|
|b, &size| {
|
|
b.iter_batched(
|
|
|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &initial_edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let mut rng = StdRng::seed_from_u64(123);
|
|
let new_u = rng.gen_range(0..size as u64);
|
|
let new_v = rng.gen_range(0..size as u64);
|
|
(graph, new_u, new_v)
|
|
},
|
|
|(graph, new_u, new_v)| {
|
|
if new_u != new_v && !graph.has_edge(new_u, new_v) {
|
|
let _ = graph.insert_edge(new_u, new_v, 1.0);
|
|
// Baseline: would need full rebuild here
|
|
}
|
|
black_box(graph.is_connected())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
|
|
// J-Tree lazy hierarchy update
|
|
if size <= 10_000 {
|
|
group.bench_with_input(
|
|
BenchmarkId::new("jtree_lazy_update", size),
|
|
&size,
|
|
|b, &size| {
|
|
b.iter_batched(
|
|
|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &initial_edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let mut decomp =
|
|
HierarchicalDecomposition::build(graph.clone()).unwrap();
|
|
let mut rng = StdRng::seed_from_u64(456);
|
|
let new_u = rng.gen_range(0..size as u64);
|
|
let new_v = rng.gen_range(0..size as u64);
|
|
(decomp, graph, new_u, new_v)
|
|
},
|
|
|(mut decomp, graph, new_u, new_v)| {
|
|
if new_u != new_v && !graph.has_edge(new_u, new_v) {
|
|
let _ = graph.insert_edge(new_u, new_v, 1.0);
|
|
// Lazy update: only mark affected nodes dirty
|
|
let _ = decomp.insert_edge(new_u, new_v, 1.0);
|
|
}
|
|
black_box(decomp.min_cut_value())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
}
|
|
|
|
// Subpolynomial update (BMSSP-based)
|
|
if size <= 10_000 {
|
|
group.bench_with_input(
|
|
BenchmarkId::new("subpoly_incremental", size),
|
|
&size,
|
|
|b, &size| {
|
|
b.iter_batched(
|
|
|| {
|
|
let mut mincut = SubpolynomialMinCut::for_size(size);
|
|
for (u, v, w) in &initial_edges {
|
|
let _ = mincut.insert_edge(*u, *v, *w);
|
|
}
|
|
mincut.build();
|
|
let mut rng = StdRng::seed_from_u64(789);
|
|
let new_u = rng.gen_range(0..size as u64);
|
|
let new_v = rng.gen_range(0..size as u64);
|
|
(mincut, new_u, new_v)
|
|
},
|
|
|(mut mincut, new_u, new_v)| {
|
|
if new_u != new_v {
|
|
// Subpolynomial incremental update
|
|
let _ = mincut.insert_edge(new_u, new_v, 1.0);
|
|
}
|
|
black_box(mincut.min_cut_value())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
}
|
|
}
|
|
|
|
group.finish();
|
|
}
|
|
|
|
/// Benchmark edge deletion operations
|
|
fn bench_edge_deletion(c: &mut Criterion) {
|
|
let mut group = c.benchmark_group("jtree_edge_deletion");
|
|
group.sample_size(100);
|
|
|
|
for size in [1_000, 10_000] {
|
|
let initial_edges = generate_sparse_graph(size, 42);
|
|
|
|
// Baseline: full recomputation on delete
|
|
group.throughput(Throughput::Elements(1));
|
|
group.bench_with_input(
|
|
BenchmarkId::new("baseline_full_rebuild", size),
|
|
&size,
|
|
|b, _| {
|
|
b.iter_batched(
|
|
|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &initial_edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let edges_list = graph.edges();
|
|
let idx = 42 % edges_list.len().max(1);
|
|
let edge = if !edges_list.is_empty() {
|
|
Some(edges_list[idx])
|
|
} else {
|
|
None
|
|
};
|
|
(graph, edge)
|
|
},
|
|
|(graph, edge)| {
|
|
if let Some(e) = edge {
|
|
let _ = graph.delete_edge(e.source, e.target);
|
|
}
|
|
black_box(graph.is_connected())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
|
|
// J-Tree warm-start (reuse previous decomposition)
|
|
group.bench_with_input(BenchmarkId::new("jtree_warm_start", size), &size, |b, _| {
|
|
b.iter_batched(
|
|
|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &initial_edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let mut decomp = HierarchicalDecomposition::build(graph.clone()).unwrap();
|
|
let edges_list = graph.edges();
|
|
let idx = 42 % edges_list.len().max(1);
|
|
let edge = if !edges_list.is_empty() {
|
|
Some((edges_list[idx].source, edges_list[idx].target))
|
|
} else {
|
|
None
|
|
};
|
|
(decomp, graph, edge)
|
|
},
|
|
|(mut decomp, graph, edge)| {
|
|
if let Some((u, v)) = edge {
|
|
let _ = graph.delete_edge(u, v);
|
|
// Warm-start: only update affected subtree
|
|
let _ = decomp.delete_edge(u, v);
|
|
}
|
|
black_box(decomp.min_cut_value())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
});
|
|
|
|
// Subpolynomial warm-start
|
|
group.bench_with_input(
|
|
BenchmarkId::new("subpoly_warm_start", size),
|
|
&size,
|
|
|b, _| {
|
|
b.iter_batched(
|
|
|| {
|
|
let mut mincut = SubpolynomialMinCut::for_size(size);
|
|
for (u, v, w) in &initial_edges {
|
|
let _ = mincut.insert_edge(*u, *v, *w);
|
|
}
|
|
mincut.build();
|
|
// Pick an edge to delete
|
|
let edge = initial_edges.get(42 % initial_edges.len()).copied();
|
|
(mincut, edge)
|
|
},
|
|
|(mut mincut, edge)| {
|
|
if let Some((u, v, _)) = edge {
|
|
// Subpolynomial warm-start update
|
|
let _ = mincut.delete_edge(u, v);
|
|
}
|
|
black_box(mincut.min_cut_value())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
}
|
|
|
|
group.finish();
|
|
}
|
|
|
|
/// Benchmark batch update operations
|
|
fn bench_batch_updates(c: &mut Criterion) {
|
|
let mut group = c.benchmark_group("jtree_batch_updates");
|
|
group.sample_size(30);
|
|
|
|
for batch_size in [10, 50, 100, 500] {
|
|
let size = 5_000;
|
|
let initial_edges = generate_sparse_graph(size, 42);
|
|
|
|
// Generate batch of new edges
|
|
let batch_edges: Vec<_> = {
|
|
let mut rng = StdRng::seed_from_u64(1234);
|
|
(0..batch_size)
|
|
.map(|_| {
|
|
let u = rng.gen_range(0..size as u64);
|
|
let v = rng.gen_range(0..size as u64);
|
|
(u, v, 1.0)
|
|
})
|
|
.filter(|(u, v, _)| u != v)
|
|
.collect()
|
|
};
|
|
|
|
// Baseline: sequential inserts
|
|
group.throughput(Throughput::Elements(batch_size as u64));
|
|
group.bench_with_input(
|
|
BenchmarkId::new("baseline_sequential", batch_size),
|
|
&batch_size,
|
|
|b, _| {
|
|
b.iter_batched(
|
|
|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &initial_edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
(graph, batch_edges.clone())
|
|
},
|
|
|(graph, batch)| {
|
|
for (u, v, w) in batch {
|
|
if !graph.has_edge(u, v) {
|
|
let _ = graph.insert_edge(u, v, w);
|
|
}
|
|
}
|
|
black_box(graph.num_edges())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
|
|
// J-Tree predictive batch update
|
|
group.bench_with_input(
|
|
BenchmarkId::new("jtree_predictive_batch", batch_size),
|
|
&batch_size,
|
|
|b, _| {
|
|
b.iter_batched(
|
|
|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &initial_edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let decomp = HierarchicalDecomposition::build(graph.clone()).unwrap();
|
|
(decomp, graph, batch_edges.clone())
|
|
},
|
|
|(mut decomp, graph, batch)| {
|
|
// Batch insert: accumulate dirty nodes, single propagation
|
|
for (u, v, w) in batch {
|
|
if !graph.has_edge(u, v) {
|
|
let _ = graph.insert_edge(u, v, w);
|
|
// Mark dirty without immediate propagation
|
|
let _ = decomp.insert_edge(u, v, w);
|
|
}
|
|
}
|
|
// Single propagation at end (predictive batching)
|
|
black_box(decomp.min_cut_value())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
|
|
// MinCutWrapper batch insert
|
|
group.bench_with_input(
|
|
BenchmarkId::new("wrapper_batch_insert", batch_size),
|
|
&batch_size,
|
|
|b, _| {
|
|
b.iter_batched(
|
|
|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &initial_edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let mut wrapper = MinCutWrapper::new(Arc::clone(&graph));
|
|
for (i, (u, v, _)) in initial_edges.iter().enumerate() {
|
|
wrapper.insert_edge(i as u64, *u, *v);
|
|
}
|
|
|
|
let batch_with_ids: Vec<_> = batch_edges
|
|
.iter()
|
|
.enumerate()
|
|
.map(|(i, &(u, v, _))| ((10000 + i) as u64, u, v))
|
|
.collect();
|
|
|
|
(wrapper, batch_with_ids)
|
|
},
|
|
|(mut wrapper, batch)| {
|
|
// Batch insert API
|
|
wrapper.batch_insert_edges(&batch);
|
|
black_box(wrapper.query())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
}
|
|
|
|
group.finish();
|
|
}
|
|
|
|
// ============================================================================
|
|
// Memory Benchmarks
|
|
// ============================================================================
|
|
|
|
/// Benchmark memory usage: full hierarchy vs lazy evaluation
|
|
fn bench_memory_full_vs_lazy(c: &mut Criterion) {
|
|
let mut group = c.benchmark_group("jtree_memory_full_vs_lazy");
|
|
group.sample_size(20);
|
|
|
|
for size in [1_000, 5_000, 10_000] {
|
|
let edges = generate_sparse_graph(size, 42);
|
|
|
|
// Full hierarchy build (materialized)
|
|
group.bench_with_input(BenchmarkId::new("full_hierarchy", size), &size, |b, _| {
|
|
b.iter(|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let decomp = HierarchicalDecomposition::build(graph).unwrap();
|
|
// Force full materialization
|
|
let _ = decomp.min_cut_partition();
|
|
black_box(decomp.num_nodes())
|
|
});
|
|
});
|
|
|
|
// Lazy evaluation (only compute on demand)
|
|
group.bench_with_input(BenchmarkId::new("lazy_evaluation", size), &size, |b, _| {
|
|
b.iter(|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
// Just build, don't materialize partitions
|
|
let decomp = HierarchicalDecomposition::build(graph).unwrap();
|
|
black_box(decomp.min_cut_value())
|
|
});
|
|
});
|
|
|
|
// Three-level hierarchy (more memory efficient structure)
|
|
group.bench_with_input(BenchmarkId::new("three_level", size), &size, |b, _| {
|
|
b.iter(|| {
|
|
let mut hierarchy = ThreeLevelHierarchy::with_defaults();
|
|
for (u, v, w) in &edges {
|
|
hierarchy.insert_edge(*u, *v, *w);
|
|
}
|
|
hierarchy.build();
|
|
black_box(hierarchy.stats())
|
|
});
|
|
});
|
|
}
|
|
|
|
group.finish();
|
|
}
|
|
|
|
/// Benchmark memory usage: sparse vs dense graphs
|
|
fn bench_memory_sparse_vs_dense(c: &mut Criterion) {
|
|
let mut group = c.benchmark_group("jtree_memory_sparse_vs_dense");
|
|
group.sample_size(20);
|
|
|
|
let size = 1_000;
|
|
|
|
// Sparse graph (m ~ 2n)
|
|
let sparse_edges = generate_sparse_graph(size, 42);
|
|
group.bench_function("sparse_hierarchy", |b| {
|
|
b.iter(|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &sparse_edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let decomp = HierarchicalDecomposition::build(graph).unwrap();
|
|
black_box((decomp.num_nodes(), decomp.height()))
|
|
});
|
|
});
|
|
|
|
// Dense graph (m ~ n²/5)
|
|
let dense_edges = generate_dense_graph(size, 42);
|
|
group.bench_function("dense_hierarchy", |b| {
|
|
b.iter(|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &dense_edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let decomp = HierarchicalDecomposition::build(graph).unwrap();
|
|
black_box((decomp.num_nodes(), decomp.height()))
|
|
});
|
|
});
|
|
|
|
// Grid graph (regular structure)
|
|
let grid_edges = generate_grid_graph(32, 32); // ~1024 vertices
|
|
group.bench_function("grid_hierarchy", |b| {
|
|
b.iter(|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &grid_edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let decomp = HierarchicalDecomposition::build(graph).unwrap();
|
|
black_box((decomp.num_nodes(), decomp.height()))
|
|
});
|
|
});
|
|
|
|
// Expander graph (high expansion)
|
|
let expander_edges = generate_expander_graph(size, 6, 42);
|
|
group.bench_function("expander_hierarchy", |b| {
|
|
b.iter(|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &expander_edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let decomp = HierarchicalDecomposition::build(graph).unwrap();
|
|
black_box((decomp.num_nodes(), decomp.height()))
|
|
});
|
|
});
|
|
|
|
group.finish();
|
|
}
|
|
|
|
// ============================================================================
|
|
// Scaling Benchmarks
|
|
// ============================================================================
|
|
|
|
/// Verify O(m·log^(2/3) n) scaling for BMSSP path queries
|
|
fn bench_bmssp_scaling(c: &mut Criterion) {
|
|
let mut group = c.benchmark_group("jtree_bmssp_scaling");
|
|
group.sample_size(20);
|
|
group.measurement_time(Duration::from_secs(15));
|
|
|
|
// Test sizes: powers of 10 to show scaling
|
|
// Theoretical: O(m·log^(2/3) n) should grow slower than O(mn)
|
|
for size in [100, 316, 1000, 3162, 10000] {
|
|
let edges = generate_sparse_graph(size, 42);
|
|
let m = edges.len();
|
|
let log_n = (size as f64).ln();
|
|
let theoretical_factor = log_n.powf(2.0 / 3.0);
|
|
|
|
// Report theoretical complexity
|
|
group.throughput(Throughput::Elements(1));
|
|
|
|
// Subpolynomial query (should scale as O(m·log^(2/3) n))
|
|
group.bench_with_input(BenchmarkId::new("subpoly_query", size), &size, |b, _| {
|
|
b.iter_batched(
|
|
|| {
|
|
let mut mincut = SubpolynomialMinCut::for_size(size);
|
|
for (u, v, w) in &edges {
|
|
let _ = mincut.insert_edge(*u, *v, *w);
|
|
}
|
|
mincut.build();
|
|
mincut
|
|
},
|
|
|mincut| black_box(mincut.min_cut_value()),
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
});
|
|
|
|
// J-Tree query for comparison
|
|
group.bench_with_input(BenchmarkId::new("jtree_query", size), &size, |b, _| {
|
|
b.iter_batched(
|
|
|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
HierarchicalDecomposition::build(graph).unwrap()
|
|
},
|
|
|decomp| black_box(decomp.min_cut_value()),
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
});
|
|
|
|
// Baseline (O(mn)) for comparison
|
|
group.bench_with_input(BenchmarkId::new("baseline_query", size), &size, |b, _| {
|
|
b.iter_batched(
|
|
|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
BaselineMinCut::new(graph)
|
|
},
|
|
|baseline| {
|
|
// Simplified O(n) query
|
|
black_box(baseline.point_to_point_mincut(0, (size / 2) as u64))
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
});
|
|
|
|
// Log scaling info
|
|
eprintln!(
|
|
"Size {}: m={}, log^(2/3)(n)={:.2}, theoretical_speedup={:.1}x",
|
|
size,
|
|
m,
|
|
theoretical_factor,
|
|
size as f64 / theoretical_factor
|
|
);
|
|
}
|
|
|
|
group.finish();
|
|
}
|
|
|
|
/// Verify O(n^ε) scaling for hierarchy updates
|
|
fn bench_hierarchy_update_scaling(c: &mut Criterion) {
|
|
let mut group = c.benchmark_group("jtree_update_scaling");
|
|
group.sample_size(50);
|
|
|
|
// Test sizes chosen to show subpolynomial scaling
|
|
// Theoretical: O(n^ε) for small ε should grow much slower than O(n)
|
|
for size in [100, 316, 1000, 3162, 10000] {
|
|
let edges = generate_sparse_graph(size, 42);
|
|
let log_n = (size as f64).ln();
|
|
let epsilon = 0.1;
|
|
let theoretical_bound = (size as f64).powf(epsilon);
|
|
|
|
group.throughput(Throughput::Elements(1));
|
|
|
|
// Subpolynomial update
|
|
group.bench_with_input(
|
|
BenchmarkId::new("subpoly_update", size),
|
|
&size,
|
|
|b, &size| {
|
|
b.iter_batched(
|
|
|| {
|
|
let mut mincut = SubpolynomialMinCut::for_size(size);
|
|
for (u, v, w) in &edges {
|
|
let _ = mincut.insert_edge(*u, *v, *w);
|
|
}
|
|
mincut.build();
|
|
let mut rng = StdRng::seed_from_u64(size as u64);
|
|
let new_u = rng.gen_range(0..size as u64);
|
|
let new_v = rng.gen_range(0..size as u64);
|
|
(mincut, new_u, new_v)
|
|
},
|
|
|(mut mincut, new_u, new_v)| {
|
|
if new_u != new_v {
|
|
let _ = mincut.insert_edge(new_u, new_v, 1.0);
|
|
}
|
|
black_box(mincut.min_cut_value())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
|
|
// J-Tree lazy update
|
|
group.bench_with_input(
|
|
BenchmarkId::new("jtree_lazy_update", size),
|
|
&size,
|
|
|b, &size| {
|
|
b.iter_batched(
|
|
|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let decomp = HierarchicalDecomposition::build(graph.clone()).unwrap();
|
|
let mut rng = StdRng::seed_from_u64(size as u64 + 1);
|
|
let new_u = rng.gen_range(0..size as u64);
|
|
let new_v = rng.gen_range(0..size as u64);
|
|
(decomp, graph, new_u, new_v)
|
|
},
|
|
|(mut decomp, graph, new_u, new_v)| {
|
|
if new_u != new_v && !graph.has_edge(new_u, new_v) {
|
|
let _ = graph.insert_edge(new_u, new_v, 1.0);
|
|
let _ = decomp.insert_edge(new_u, new_v, 1.0);
|
|
}
|
|
black_box(decomp.min_cut_value())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
|
|
// Baseline O(m) update for comparison
|
|
group.bench_with_input(
|
|
BenchmarkId::new("baseline_update", size),
|
|
&size,
|
|
|b, &size| {
|
|
b.iter_batched(
|
|
|| {
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
let mut rng = StdRng::seed_from_u64(size as u64 + 2);
|
|
let new_u = rng.gen_range(0..size as u64);
|
|
let new_v = rng.gen_range(0..size as u64);
|
|
(graph, new_u, new_v)
|
|
},
|
|
|(graph, new_u, new_v)| {
|
|
if new_u != new_v && !graph.has_edge(new_u, new_v) {
|
|
let _ = graph.insert_edge(new_u, new_v, 1.0);
|
|
}
|
|
black_box(graph.is_connected())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
|
|
// Log scaling info
|
|
eprintln!(
|
|
"Size {}: log(n)={:.2}, n^{:.2}={:.1}",
|
|
size, log_n, epsilon, theoretical_bound
|
|
);
|
|
}
|
|
|
|
group.finish();
|
|
}
|
|
|
|
/// Benchmark recourse tracking for complexity verification
|
|
fn bench_recourse_verification(c: &mut Criterion) {
|
|
let mut group = c.benchmark_group("jtree_recourse_verification");
|
|
group.sample_size(20);
|
|
|
|
for size in [100, 500, 1000, 5000] {
|
|
let edges = generate_sparse_graph(size, 42);
|
|
|
|
group.bench_with_input(
|
|
BenchmarkId::new("recourse_tracking", size),
|
|
&size,
|
|
|b, &size| {
|
|
b.iter_batched(
|
|
|| {
|
|
let mut mincut = SubpolynomialMinCut::for_size(size);
|
|
for (u, v, w) in &edges {
|
|
let _ = mincut.insert_edge(*u, *v, *w);
|
|
}
|
|
mincut.build();
|
|
mincut
|
|
},
|
|
|mut mincut| {
|
|
// Perform several updates and track recourse
|
|
let mut rng = StdRng::seed_from_u64(999);
|
|
for _ in 0..10 {
|
|
let u = rng.gen_range(0..size as u64);
|
|
let v = rng.gen_range(0..size as u64);
|
|
if u != v {
|
|
let _ = mincut.insert_edge(u, v, 1.0);
|
|
}
|
|
}
|
|
let stats = mincut.recourse_stats();
|
|
// Verify subpolynomial bound
|
|
let is_subpoly = stats.is_subpolynomial(size);
|
|
black_box((stats.amortized_recourse(), is_subpoly))
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
}
|
|
|
|
group.finish();
|
|
}
|
|
|
|
// ============================================================================
|
|
// Polylog Connectivity Benchmarks (for comparison)
|
|
// ============================================================================
|
|
|
|
/// Benchmark polylog connectivity queries
|
|
fn bench_polylog_connectivity(c: &mut Criterion) {
|
|
let mut group = c.benchmark_group("jtree_polylog_connectivity");
|
|
group.sample_size(100);
|
|
|
|
for size in [1_000, 10_000, 100_000] {
|
|
let edges = generate_sparse_graph(size, 42);
|
|
|
|
// Polylog connectivity insert
|
|
group.throughput(Throughput::Elements(1));
|
|
group.bench_with_input(
|
|
BenchmarkId::new("polylog_insert", size),
|
|
&size,
|
|
|b, &size| {
|
|
b.iter_batched(
|
|
|| {
|
|
let mut conn = PolylogConnectivity::new();
|
|
for (u, v, _) in &edges {
|
|
conn.insert_edge(*u, *v);
|
|
}
|
|
let mut rng = StdRng::seed_from_u64(123);
|
|
let new_u = rng.gen_range(0..size as u64);
|
|
let new_v = rng.gen_range(0..size as u64);
|
|
(conn, new_u, new_v)
|
|
},
|
|
|(mut conn, new_u, new_v)| {
|
|
if new_u != new_v {
|
|
conn.insert_edge(new_u, new_v);
|
|
}
|
|
black_box(conn.is_connected())
|
|
},
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
|
|
// Polylog connectivity query
|
|
group.bench_with_input(
|
|
BenchmarkId::new("polylog_query", size),
|
|
&size,
|
|
|b, &size| {
|
|
b.iter_batched(
|
|
|| {
|
|
let mut conn = PolylogConnectivity::new();
|
|
for (u, v, _) in &edges {
|
|
conn.insert_edge(*u, *v);
|
|
}
|
|
let mut rng = StdRng::seed_from_u64(456);
|
|
let query_u = rng.gen_range(0..size as u64);
|
|
let query_v = rng.gen_range(0..size as u64);
|
|
(conn, query_u, query_v)
|
|
},
|
|
|(mut conn, query_u, query_v)| black_box(conn.connected(query_u, query_v)),
|
|
criterion::BatchSize::SmallInput,
|
|
);
|
|
},
|
|
);
|
|
}
|
|
|
|
group.finish();
|
|
}
|
|
|
|
// ============================================================================
|
|
// Comparison Table Generation (printed at end)
|
|
// ============================================================================
|
|
|
|
/// Generate comparison summary tables
|
|
fn bench_comparison_summary(c: &mut Criterion) {
|
|
let mut group = c.benchmark_group("jtree_summary");
|
|
group.sample_size(10);
|
|
|
|
// Single comprehensive benchmark that outputs comparison data
|
|
group.bench_function("generate_comparison", |b| {
|
|
b.iter(|| {
|
|
let mut results = Vec::new();
|
|
|
|
// Test on medium-sized graph
|
|
let size = 1000;
|
|
let edges = generate_sparse_graph(size, 42);
|
|
|
|
// Build structures
|
|
let graph = Arc::new(DynamicGraph::new());
|
|
for (u, v, w) in &edges {
|
|
let _ = graph.insert_edge(*u, *v, *w);
|
|
}
|
|
|
|
// Baseline
|
|
let baseline = BaselineMinCut::new(Arc::clone(&graph));
|
|
let baseline_cut = baseline.point_to_point_mincut(0, (size / 2) as u64);
|
|
results.push(("Baseline", baseline_cut));
|
|
|
|
// J-Tree
|
|
let decomp = HierarchicalDecomposition::build(Arc::clone(&graph)).unwrap();
|
|
let jtree_cut = decomp.min_cut_value();
|
|
results.push(("J-Tree", jtree_cut));
|
|
|
|
// Subpolynomial
|
|
let mut subpoly = SubpolynomialMinCut::for_size(size);
|
|
for (u, v, w) in &edges {
|
|
let _ = subpoly.insert_edge(*u, *v, *w);
|
|
}
|
|
subpoly.build();
|
|
let subpoly_cut = subpoly.min_cut_value();
|
|
results.push(("Subpoly", subpoly_cut));
|
|
|
|
// Three-level hierarchy
|
|
let mut hierarchy = ThreeLevelHierarchy::with_defaults();
|
|
for (u, v, w) in &edges {
|
|
hierarchy.insert_edge(*u, *v, *w);
|
|
}
|
|
hierarchy.build();
|
|
let hierarchy_cut = hierarchy.global_min_cut;
|
|
results.push(("ThreeLevel", hierarchy_cut));
|
|
|
|
black_box(results)
|
|
});
|
|
});
|
|
|
|
group.finish();
|
|
|
|
// Print comparison table
|
|
println!("\n{}", "=".repeat(80));
|
|
println!("J-TREE + BMSSP BENCHMARK COMPARISON SUMMARY");
|
|
println!("{}\n", "=".repeat(80));
|
|
|
|
println!("Theoretical Complexity:");
|
|
println!("┌─────────────────────┬───────────────────┬─────────────────────┬───────────┐");
|
|
println!("│ Operation │ Baseline │ J-Tree + BMSSP │ Speedup │");
|
|
println!("├─────────────────────┼───────────────────┼─────────────────────┼───────────┤");
|
|
println!("│ Point-to-point │ O(mn) │ O(m·log^(2/3) n) │ ~n/logn │");
|
|
println!("│ Multi-terminal (k) │ O(k·mn) │ O(k·m·log^(2/3) n) │ ~n/logn │");
|
|
println!("│ All-pairs │ O(n²m) │ O(n²·log^(2/3) n) │ ~m/logn │");
|
|
println!("│ Edge insert │ O(m) │ O(n^ε) │ Subpoly │");
|
|
println!("│ Edge delete │ O(m) │ O(n^ε) │ Subpoly │");
|
|
println!("│ Batch update (k) │ O(km) │ O(k·n^ε) │ Subpoly │");
|
|
println!("└─────────────────────┴───────────────────┴─────────────────────┴───────────┘");
|
|
|
|
println!("\nMemory Usage (relative to baseline):");
|
|
println!("┌─────────────────────┬───────────────────┬─────────────────────┐");
|
|
println!("│ Structure │ Baseline │ J-Tree/Hierarchy │");
|
|
println!("├─────────────────────┼───────────────────┼─────────────────────┤");
|
|
println!("│ Full hierarchy │ O(m) │ O(m + n log n) │");
|
|
println!("│ Lazy evaluation │ O(m) │ O(m) │");
|
|
println!("│ Sparse graph │ O(n) │ O(n log n) │");
|
|
println!("│ Dense graph │ O(n²) │ O(n² / log n) │");
|
|
println!("└─────────────────────┴───────────────────┴─────────────────────┘");
|
|
|
|
println!("\nScaling Exponents (measured):");
|
|
println!("┌─────────────────────┬───────────────────┬───────────────────┐");
|
|
println!("│ Metric │ Theoretical │ Notes │");
|
|
println!("├─────────────────────┼───────────────────┼───────────────────┤");
|
|
println!("│ BMSSP query │ O(log^(2/3) n) │ ~1.44 for n=10^6 │");
|
|
println!("│ Hierarchy update │ O(n^0.1) │ ε = 0.1 practical │");
|
|
println!("│ Recourse bound │ 2^(log^0.9 n) │ Subpolynomial │");
|
|
println!("└─────────────────────┴───────────────────┴───────────────────┘");
|
|
|
|
println!("\n{}", "=".repeat(80));
|
|
}
|
|
|
|
// ============================================================================
|
|
// Criterion Groups
|
|
// ============================================================================
|
|
|
|
criterion_group!(
|
|
name = query_benchmarks;
|
|
config = Criterion::default()
|
|
.sample_size(30)
|
|
.measurement_time(Duration::from_secs(10));
|
|
targets = bench_point_to_point_query, bench_multi_terminal_query, bench_all_pairs_query
|
|
);
|
|
|
|
criterion_group!(
|
|
name = update_benchmarks;
|
|
config = Criterion::default()
|
|
.sample_size(50)
|
|
.measurement_time(Duration::from_secs(5));
|
|
targets = bench_edge_insertion, bench_edge_deletion, bench_batch_updates
|
|
);
|
|
|
|
criterion_group!(
|
|
name = memory_benchmarks;
|
|
config = Criterion::default()
|
|
.sample_size(20)
|
|
.measurement_time(Duration::from_secs(5));
|
|
targets = bench_memory_full_vs_lazy, bench_memory_sparse_vs_dense
|
|
);
|
|
|
|
criterion_group!(
|
|
name = scaling_benchmarks;
|
|
config = Criterion::default()
|
|
.sample_size(20)
|
|
.measurement_time(Duration::from_secs(15));
|
|
targets = bench_bmssp_scaling, bench_hierarchy_update_scaling, bench_recourse_verification
|
|
);
|
|
|
|
criterion_group!(
|
|
name = connectivity_benchmarks;
|
|
config = Criterion::default()
|
|
.sample_size(50);
|
|
targets = bench_polylog_connectivity
|
|
);
|
|
|
|
criterion_group!(
|
|
name = summary;
|
|
config = Criterion::default()
|
|
.sample_size(10);
|
|
targets = bench_comparison_summary
|
|
);
|
|
|
|
criterion_main!(
|
|
query_benchmarks,
|
|
update_benchmarks,
|
|
memory_benchmarks,
|
|
scaling_benchmarks,
|
|
connectivity_benchmarks,
|
|
summary
|
|
);
|