GraphZero is a next-generation, high-performance graph processing engine built from the ground up in C++ with seamless Python bindings. It is designed to crush the performance bottlenecks of pure Python graph libraries (like NetworkX) by leveraging:
- Zero-Copy Memory Mapping (mmap): Instantly load massive graphs (millions of nodes/edges) without RAM parsing overhead.
- Massive Parallelism (OpenMP): Fully utilizes multi-core CPUs for graph traversals and random walks.
- Cache-Optimized Data Structures: Compressed Sparse Row (CSR) layouts for extreme locality and speed.
"Stop waiting for your graph algorithms to finish. Go Zero."
I faced GraphZero against the industry standards: NetworkX (pure Python) and PyTorch Geometric (GNN Standard). The results speak for themselves.
| Benchmark Scenario | Dataset | Graph Scale | GraphZero Time | NetworkX Time | Speedup |
|---|---|---|---|---|---|
| Viral Blast Radius | Facebook Combined | ~4k Nodes | 0.034s | 6.85s | ~201x 🚀 |
| Wiki Wormhole | Wiki-Vote | ~7k Nodes | 0.009s | 0.56s | ~61x 🏎️ |
| Google Crawler | Google Web Graph | ~875k Nodes | 0.11s | 12.15s | ~110x 🔥 |
| RecSys SLA | Google Web Graph | ~875k Nodes | 0.016s | 1.65s | ~85x ⚡ |
| GNN Dataloader | Google Web Graph | ~875k Nodes | 1.27s | 1.59s (PyG) | ~1.25x |
- Goal: Simulate the spread of a viral message from random seed users.
- Task: 50,000 Random Walks (Length 100).
- Result: GraphZero chews through 5 million traversal steps in 34 milliseconds.
- Goal: Find shortest paths between Wikipedia articles (BFS).
- Task: 10,000 BFS traversals.
- Result: 0.007s vs 2.1s (NetworkX). GraphZero makes real-time pathfinding on knowledge graphs instantaneous.
- Goal: Simulate 1,000,000 concurrent web crawlers traversing the Google Web Graph.
- Task: 20 Million Steps total on 5.1M edges.
- Result: Throughput of 141 Million steps/sec. GraphZero handles million-agent simulations in sub-second time.
- Goal: Serve "Who To Follow" recommendations for 100 concurrent users under strict latency requirements.
- Result: GraphZero serves requests in 16ms, unlocking real-time graph features for production APIs where python would timeout.
- Goal: Compare neighbor sampling throughput for GNN training against PyTorch Geometric.
- Result: GraphZero achieves 158 batches/s, slightly outperforming PyG's optimized
NeighborLoader(149 batches/s).
Built with ❤️ for speed.