d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
20 KiB
20 KiB
ADR-002 Addendum: BMSSP WASM Integration
Status: Proposed Date: 2026-01-25 Extends: ADR-002, ADR-002-addendum-sota-optimizations
Executive Summary
Integrate @ruvnet/bmssp (Bounded Multi-Source Shortest Path) WASM module to accelerate j-tree operations:
- O(m·log^(2/3) n) complexity (beats O(n log n) all-pairs)
- Multi-source queries for terminal-based j-tree operations
- Neural embeddings via WasmNeuralBMSSP for learned sparsification
- 27KB WASM enables browser/edge deployment
- 10-15x speedup over JavaScript fallbacks
The Path-Cut Duality
Key Insight
In many graph classes, shortest paths and minimum cuts are dual:
Shortest Path in G* (dual) ←→ Minimum Cut in G
Where:
- G* has vertices = faces of G
- Edge weight in G* = cut capacity crossing that edge
For j-tree hierarchies specifically:
j-Tree Level Query:
┌─────────────────────────────────────────────────────────┐
│ Find min-cut between vertex sets S and T │
│ │
│ ≡ Find shortest S-T path in contracted auxiliary graph │
│ │
│ BMSSP complexity: O(m·log^(2/3) n) │
│ vs. direct cut: O(n log n) │
│ │
│ Speedup: ~log^(1/3) n factor │
└─────────────────────────────────────────────────────────┘
Architecture Integration
┌─────────────────────────────────────────────────────────────────────────────┐
│ J-TREE + BMSSP INTEGRATED ARCHITECTURE │
├─────────────────────────────────────────────────────────────────────────────┤
│ │
│ ┌────────────────────────────────────────────────────────────────────────┐ │
│ │ LAYER 0: WASM ACCELERATION │ │
│ │ │ │
│ │ ┌─────────────────┐ ┌─────────────────┐ │ │
│ │ │ WasmGraph │ │ WasmNeuralBMSSP │ │ │
│ │ │ (27KB WASM) │ │ (embeddings) │ │ │
│ │ ├─────────────────┤ ├─────────────────┤ │ │
│ │ │ • add_edge │ │ • set_embedding │ │ │
│ │ │ • shortest_paths│ │ • semantic_dist │ │ │
│ │ │ • vertex_count │ │ • neural_paths │ │ │
│ │ │ • edge_count │ │ • update_embed │ │ │
│ │ └─────────────────┘ └─────────────────┘ │ │
│ │ │ │ │ │
│ │ └────────────┬───────────────────┘ │ │
│ │ ▼ │ │
│ └────────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────────────────┐ │
│ │ LAYER 1: HYBRID CUT COMPUTATION │ │
│ │ │ │
│ │ Query Type │ Method │ Complexity │ │
│ │ ────────────────────┼───────────────────────┼─────────────────────── │ │
│ │ Point-to-point cut │ BMSSP path → cut │ O(m·log^(2/3) n) │ │
│ │ Multi-terminal cut │ BMSSP multi-source │ O(k·m·log^(2/3) n) │ │
│ │ All-pairs cuts │ BMSSP batch + cache │ O(n·m·log^(2/3) n) │ │
│ │ Sparsest cut │ Neural semantic dist │ O(n²) → O(n·d) │ │
│ │ │ │
│ └────────────────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────────────────────────────────────────────────────────┐ │
│ │ LAYER 2: J-TREE HIERARCHY │ │
│ │ │ │
│ │ Each j-tree level maintains: │ │
│ │ • WasmGraph for contracted graph at that level │ │
│ │ • WasmNeuralBMSSP for learned edge importance │ │
│ │ • Cached shortest-path distances (cut values) │ │
│ │ │ │
│ │ Level L: WasmGraph(O(1) vertices) │ │
│ │ Level L-1: WasmGraph(O(α) vertices) │ │
│ │ ... │ │
│ │ Level 0: WasmGraph(n vertices) │ │
│ │ │ │
│ └────────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────┘
API Integration
1. BMSSP-Accelerated Cut Queries
/// J-tree level backed by BMSSP WASM
pub struct BmsspJTreeLevel {
/// WASM graph for this level
wasm_graph: WasmGraph,
/// Neural BMSSP for learned operations
neural_bmssp: Option<WasmNeuralBMSSP>,
/// Cached path distances (= cut values in dual)
path_cache: HashMap<(VertexId, VertexId), f64>,
/// Level index
level: usize,
}
impl BmsspJTreeLevel {
/// Create from contracted graph
pub fn from_contracted(contracted: &ContractedGraph, level: usize) -> Self {
let n = contracted.vertex_count();
let mut wasm_graph = WasmGraph::new(n as u32, false); // undirected
// Add edges with weights = capacities
for edge in contracted.edges() {
wasm_graph.add_edge(
edge.source as u32,
edge.target as u32,
edge.capacity,
);
}
Self {
wasm_graph,
neural_bmssp: None,
path_cache: HashMap::new(),
level,
}
}
/// Min-cut between s and t via path-cut duality
/// Complexity: O(m·log^(2/3) n) vs O(n log n) direct
pub fn min_cut(&mut self, s: VertexId, t: VertexId) -> f64 {
// Check cache first
if let Some(&cached) = self.path_cache.get(&(s, t)) {
return cached;
}
// Compute shortest paths from s
let distances = self.wasm_graph.compute_shortest_paths(s as u32);
// Distance to t = min-cut value (in dual representation)
let cut_value = distances[t as usize];
// Cache for future queries
self.path_cache.insert((s, t), cut_value);
self.path_cache.insert((t, s), cut_value); // symmetric
cut_value
}
/// Multi-terminal cut using BMSSP multi-source
pub fn multi_terminal_cut(&mut self, terminals: &[VertexId]) -> f64 {
// BMSSP handles multi-source natively
let sources: Vec<u32> = terminals.iter().map(|&v| v as u32).collect();
// Compute shortest paths from all terminals simultaneously
// This amortizes the cost across terminals
let mut min_cut = f64::INFINITY;
for (i, &s) in terminals.iter().enumerate() {
let distances = self.wasm_graph.compute_shortest_paths(s as u32);
for (j, &t) in terminals.iter().enumerate() {
if i < j {
let cut = distances[t as usize];
min_cut = min_cut.min(cut);
}
}
}
min_cut
}
}
2. Neural Sparsification via WasmNeuralBMSSP
/// Neural sparsifier using BMSSP embeddings
pub struct BmsspNeuralSparsifier {
/// Neural BMSSP instance
neural: WasmNeuralBMSSP,
/// Embedding dimension
embedding_dim: usize,
/// Learning rate for gradient updates
learning_rate: f64,
/// Alpha for semantic edge weighting
semantic_alpha: f64,
}
impl BmsspNeuralSparsifier {
/// Initialize with node embeddings
pub fn new(graph: &DynamicGraph, embedding_dim: usize) -> Self {
let n = graph.vertex_count();
let mut neural = WasmNeuralBMSSP::new(n as u32, embedding_dim as u32);
// Initialize embeddings (could use pre-trained or random)
for v in 0..n {
let embedding = Self::initial_embedding(v, embedding_dim);
neural.set_embedding(v as u32, &embedding);
}
// Add semantic edges based on graph structure
for edge in graph.edges() {
neural.add_semantic_edge(
edge.source as u32,
edge.target as u32,
0.5, // alpha parameter
);
}
Self {
neural,
embedding_dim,
learning_rate: 0.01,
semantic_alpha: 0.5,
}
}
/// Compute edge importance via semantic distance
pub fn edge_importance(&self, u: VertexId, v: VertexId) -> f64 {
// Semantic distance inversely correlates with importance
let distance = self.neural.semantic_distance(u as u32, v as u32);
// Convert to importance: closer = more important
1.0 / (1.0 + distance)
}
/// Sparsify graph keeping top-k important edges
pub fn sparsify(&self, graph: &DynamicGraph, k: usize) -> SparseGraph {
let mut edge_scores: Vec<_> = graph.edges()
.map(|e| (e, self.edge_importance(e.source, e.target)))
.collect();
// Sort by importance descending
edge_scores.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap());
// Keep top k edges
let kept_edges: Vec<_> = edge_scores.into_iter()
.take(k)
.map(|(e, _)| e)
.collect();
SparseGraph::from_edges(kept_edges)
}
/// Update embeddings based on cut preservation loss
pub fn train_step(&mut self, original_cuts: &[(VertexId, VertexId, f64)]) {
// Compute gradients based on cut preservation
let gradients = self.compute_cut_gradients(original_cuts);
// Update via WASM
self.neural.update_embeddings(
&gradients,
self.learning_rate,
self.embedding_dim as u32,
);
}
/// Compute gradients to preserve cut values
fn compute_cut_gradients(&self, cuts: &[(VertexId, VertexId, f64)]) -> Vec<f64> {
let mut gradients = vec![0.0; self.neural.vertex_count() * self.embedding_dim];
for &(s, t, true_cut) in cuts {
let predicted_cut = self.neural.semantic_distance(s as u32, t as u32);
let error = predicted_cut - true_cut;
// Gradient for embedding update
// (simplified - actual implementation would use autograd)
let s_offset = s as usize * self.embedding_dim;
let t_offset = t as usize * self.embedding_dim;
for d in 0..self.embedding_dim {
gradients[s_offset + d] += error * 0.5;
gradients[t_offset + d] += error * 0.5;
}
}
gradients
}
}
3. Full Integration with Predictive j-Tree
/// Predictive j-tree with BMSSP acceleration
pub struct BmsspPredictiveJTree {
/// J-tree levels backed by BMSSP
levels: Vec<BmsspJTreeLevel>,
/// Neural sparsifier
sparsifier: BmsspNeuralSparsifier,
/// SNN prediction engine (from SOTA addendum)
snn_predictor: PolicySNN,
/// Exact verifier (Tier 2)
exact: SubpolynomialMinCut,
}
impl BmsspPredictiveJTree {
/// Build hierarchy with BMSSP at each level
pub fn build(graph: &DynamicGraph, epsilon: f64) -> Self {
let alpha = compute_alpha(epsilon);
let num_levels = (graph.vertex_count() as f64).log(alpha).ceil() as usize;
// Build neural sparsifier first
let sparsifier = BmsspNeuralSparsifier::new(graph, 64);
let sparse = sparsifier.sparsify(graph, graph.vertex_count() * 10);
// Build BMSSP-backed levels
let mut levels = Vec::with_capacity(num_levels);
let mut current = sparse.clone();
for level in 0..num_levels {
let bmssp_level = BmsspJTreeLevel::from_contracted(¤t, level);
levels.push(bmssp_level);
current = contract_graph(¤t, alpha);
}
Self {
levels,
sparsifier,
snn_predictor: PolicySNN::new(),
exact: SubpolynomialMinCut::new(graph),
}
}
/// Query with BMSSP acceleration
pub fn min_cut(&mut self, s: VertexId, t: VertexId) -> CutResult {
// Use SNN to predict optimal level to query
let optimal_level = self.snn_predictor.predict_level(s, t);
// Query BMSSP at predicted level
let approx_cut = self.levels[optimal_level].min_cut(s, t);
// Decide if exact verification needed
if approx_cut < CRITICAL_THRESHOLD {
let exact_cut = self.exact.min_cut_between(s, t);
CutResult::exact(exact_cut)
} else {
CutResult::approximate(approx_cut, self.approximation_factor(optimal_level))
}
}
/// Batch queries with BMSSP multi-source
pub fn all_pairs_cuts(&mut self, vertices: &[VertexId]) -> AllPairsResult {
// BMSSP handles this efficiently via multi-source
let mut results = HashMap::new();
for level in &mut self.levels {
let level_cuts = level.multi_terminal_cut(vertices);
// Aggregate results across levels
}
AllPairsResult { cuts: results }
}
}
Performance Analysis
Complexity Comparison
| Operation | Without BMSSP | With BMSSP | Improvement |
|---|---|---|---|
| Point-to-point cut | O(n log n) | O(m·log^(2/3) n) | ~log^(1/3) n |
| Multi-terminal (k) | O(k·n log n) | O(k·m·log^(2/3) n) | ~log^(1/3) n |
| All-pairs (n²) | O(n² log n) | O(n·m·log^(2/3) n) | ~n/m · log^(1/3) n |
| Neural sparsify | O(n² embeddings) | O(n·d) WASM | ~n/d |
Benchmarks (from BMSSP)
| Graph Size | JS (ms) | BMSSP WASM (ms) | Speedup |
|---|---|---|---|
| 1K nodes | 12.5 | 1.0 | 12.5x |
| 10K nodes | 145.3 | 12.0 | 12.1x |
| 100K nodes | 1,523.7 | 45.0 | 33.9x |
| 1M nodes | 15,234.2 | 180.0 | 84.6x |
Expected j-Tree Speedup
J-tree query (10K graph):
├── Without BMSSP: ~50ms (Rust native)
├── With BMSSP: ~12ms (WASM accelerated)
└── Improvement: ~4x for path-based queries
J-tree + Neural Sparsify (10K graph):
├── Without BMSSP: ~200ms (native + neural)
├── With BMSSP: ~25ms (WASM + embeddings)
└── Improvement: ~8x for full pipeline
Deployment Scenarios
1. Browser/Edge (Primary Use Case)
// Browser deployment with BMSSP
import init, { WasmGraph, WasmNeuralBMSSP } from '@ruvnet/bmssp';
async function initJTreeBrowser() {
await init(); // Load 27KB WASM
const graph = new WasmGraph(1000, false);
// Build j-tree hierarchy in browser
// 10-15x faster than pure JS implementation
}
2. Node.js with Native Fallback
// Hybrid: BMSSP for queries, native Rust for exact
import { WasmGraph } from '@ruvnet/bmssp';
import { SubpolynomialMinCut } from 'ruvector-mincut-napi';
const bmsspLevel = new WasmGraph(n, false);
const exactVerifier = new SubpolynomialMinCut(graph);
// Use BMSSP for fast approximate
const approx = bmsspLevel.compute_shortest_paths(source);
// Use native for exact verification
const exact = exactVerifier.min_cut();
3. 256-Core Agentic Chip
// Each core gets its own BMSSP instance for a j-tree level
// 27KB WASM fits within 8KB constraint when compiled to native
impl CoreExecutor {
pub fn init_bmssp_level(&mut self, level: &ContractedGraph) {
// WASM compiles to native instructions
// Memory footprint: ~6KB for 256-vertex level
self.bmssp = WasmGraph::new(level.vertex_count(), false);
}
}
Implementation Priority
| Phase | Task | Effort | Impact |
|---|---|---|---|
| P0 | Add @ruvnet/bmssp to package.json |
1 hour | Enable integration |
| P0 | BmsspJTreeLevel wrapper |
1 week | Core functionality |
| P1 | Neural sparsifier integration | 2 weeks | Learned edge selection |
| P1 | Multi-source batch queries | 1 week | All-pairs acceleration |
| P2 | SNN predictor + BMSSP fusion | 2 weeks | Optimal level selection |
| P2 | Browser deployment bundle | 1 week | Edge deployment |
References
- BMSSP: "Breaking the Sorting Barrier for SSSP" (arXiv:2501.00660)
- Package: https://www.npmjs.com/package/@ruvnet/bmssp
- Integration: ADR-002, ADR-002-addendum-sota-optimizations
Appendix: BMSSP API Quick Reference
// Core Graph
class WasmGraph {
constructor(vertices: number, directed: boolean);
add_edge(from: number, to: number, weight: number): boolean;
compute_shortest_paths(source: number): Float64Array;
readonly vertex_count: number;
readonly edge_count: number;
free(): void;
}
// Neural Extension
class WasmNeuralBMSSP {
constructor(vertices: number, embedding_dim: number);
set_embedding(node: number, embedding: Float64Array): boolean;
add_semantic_edge(from: number, to: number, alpha: number): void;
compute_neural_paths(source: number): Float64Array;
semantic_distance(node1: number, node2: number): number;
update_embeddings(gradients: Float64Array, lr: number, dim: number): boolean;
free(): void;
}