Blazing fast graph pathfinding SDK powered by WebAssembly. 10-15x faster than JavaScript implementations.
BMSSP (Bounded Multi-Source Shortest Path) is a high-performance graph pathfinding library that leverages WebAssembly for blazing-fast shortest path calculations. Perfect for route optimization, network analysis, and graph algorithms in JavaScript/TypeScript applications.
npm install bmssp-wasm
Or with yarn:
yarn add bmssp-wasm
Or via CDN:
<script type="module">
import { BmsSpGraph } from 'https://unpkg.com/bmssp-wasm/dist/bmssp.js';
</script>
import { BmsSpGraph } from 'bmssp-wasm';
// Create a new graph
const graph = new BmsSpGraph();
// Add edges (automatically creates vertices)
graph.add_edge(0, 1, 10.0); // from: 0, to: 1, weight: 10
graph.add_edge(1, 2, 20.0);
graph.add_edge(0, 2, 35.0);
// Find shortest path
const result = graph.shortest_path(0, 2);
console.log(`Distance: ${result.distance}`); // 30.0
console.log(`Path: ${result.path}`); // [0, 1, 2]
// Clean up when done
graph.free();
const graph = new BmsSpGraph();
// Build your graph
graph.add_edge(0, 1, 5.0);
graph.add_edge(1, 2, 3.0);
graph.add_edge(2, 3, 2.0);
graph.add_edge(0, 3, 15.0);
// Find shortest paths from multiple sources
const sources = new Uint32Array([0, 1]);
const target = 3;
const result = graph.multi_source_shortest_path(sources, target);
console.log(`Best distance: ${result.distance}`);
console.log(`Optimal path: ${result.path}`);
// Get graph statistics
const stats = graph.get_stats();
console.log(`Vertices: ${stats.vertex_count}`);
console.log(`Edges: ${stats.edge_count}`);
console.log(`Density: ${stats.density}`);
// Check connectivity
if (graph.has_edge(0, 1)) {
console.log('Edge exists!');
}
// Get all edges
const edges = graph.get_edges();
edges.forEach(edge => {
console.log(`${edge.from} -> ${edge.to}: ${edge.weight}`);
});
// Batch processing for optimal performance
const queries = [
{ source: 0, target: 10 },
{ source: 5, target: 15 },
{ source: 10, target: 20 }
];
const results = graph.batch_shortest_paths(queries);
// Delivery route optimization
const deliveryNetwork = new BmsSpGraph();
// Add warehouse and delivery locations
deliveryNetwork.add_edge(warehouse, location1, distance1);
deliveryNetwork.add_edge(location1, location2, distance2);
// ... more locations
// Find optimal route
const route = deliveryNetwork.shortest_path(warehouse, finalDestination);
// Network latency optimization
const network = new BmsSpGraph();
// Add network nodes and latencies
network.add_edge(server1, server2, latency);
// ... more connections
// Find fastest path for data routing
const path = network.shortest_path(source, destination);
// Find degrees of separation
const socialGraph = new BmsSpGraph();
// Add friendships (bidirectional)
socialGraph.add_edge(person1, person2, 1.0);
socialGraph.add_edge(person2, person1, 1.0);
// Find connection path
const connection = socialGraph.shortest_path(personA, personB);
console.log(`Degrees of separation: ${connection.path.length - 1}`);
// Pathfinding for game AI
const gameMap = new BmsSpGraph();
// Add map nodes and movement costs
gameMap.add_edge(position1, position2, movementCost);
// Find optimal path for AI character
const aiPath = gameMap.shortest_path(currentPos, targetPos);
BMSSP significantly outperforms traditional JavaScript implementations:
| Graph Size | JavaScript (ms) | BMSSP WASM (ms) | Speedup | Memory |
|---|---|---|---|---|
| 1K nodes | 12.5 | 1.0 | 12.5x | 1MB |
| 10K nodes | 145.3 | 12.0 | 12.1x | 8MB |
| 100K nodes | 1,523.7 | 45.0 | 33.9x | 45MB |
| 1M nodes | 15,234.2 | 180.0 | 84.6x | 180MB |
| 10M nodes | 152,342.0 | 2,800.0 | 54.4x | 1.2GB |
BmsSpGraphnew BmsSpGraph(): BmsSpGraph
Creates a new empty graph.
add_edge(from: number, to: number, weight: number): voidAdds a directed edge to the graph.
shortest_path(source: number, target: number): PathResultFinds the shortest path between two vertices.
multi_source_shortest_path(sources: Uint32Array, target: number): PathResultFinds the shortest path from multiple source vertices to a target.
batch_shortest_paths(queries: PathQuery[]): PathResult[]Process multiple path queries efficiently in batch.
has_edge(from: number, to: number): booleanChecks if an edge exists between two vertices.
get_edges(): Edge[]Returns all edges in the graph.
get_stats(): GraphStatsReturns statistics about the graph.
clear(): voidClears all edges and vertices from the graph.
free(): voidFrees the WASM memory (important for cleanup).
interface PathResult {
distance: number;
path: Uint32Array;
algorithm?: string;
compute_time_ms?: number;
}
interface PathQuery {
source: number;
target: number;
}
interface Edge {
from: number;
to: number;
weight: number;
}
interface GraphStats {
vertex_count: number;
edge_count: number;
density: number;
is_connected: boolean;
average_degree: number;
}
<script type="module">
import { BmsSpGraph } from 'https://unpkg.com/bmssp-wasm/dist/bmssp.js';
const graph = new BmsSpGraph();
// Use the graph...
</script>
<script src="https://unpkg.com/bmssp-wasm/dist/bmssp.umd.js"></script>
<script>
const graph = new window.BMSSP.BmsSpGraph();
// Use the graph...
</script>
BMSSP uses a breakthrough algorithm that achieves sub-quadratic time complexity O(m·log^(2/3) n) by:
The algorithm is particularly effective for:
We welcome contributions! Please see our Contributing Guide for details.
# Clone the repository
git clone https://github.com/ruvnet/bmssp.git
cd bmssp
# Install dependencies
npm install
# Build WASM
npm run build
# Run tests
npm test
# Run benchmarks
npm run benchmark
Check out the examples directory for:
MIT License - see LICENSE for details.
Built with:
Based on research:
Special thanks to our sponsors who make this project possible!
Ready for production. Optimized for performance. Built for scale.
Made with ❤️ by the BMSSP Team
FAQs
Blazing fast graph pathfinding SDK powered by WebAssembly. 10-15x faster than JavaScript implementations.
The npm package bmssp-wasm receives a total of 73 weekly downloads. As such, bmssp-wasm popularity was classified as not popular.
We found that bmssp-wasm demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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