graphzero 0.1.2

High-performance Zero-Copy Graph Engine

GraphZero

High-Performance, Zero-Copy Graph Engine for Massive Datasets on Consumer Hardware.

GraphZero is a C++ graph processing engine with lightweight Python bindings designed to solve the "Memory Wall" in Graph Neural Networks (GNNs). It allows you to load and sample 100 Million+ node graphs (like ogbn-papers100M) on a standard 16GB RAM laptop—something standard libraries like PyTorch Geometric (PyG) or DGL cannot do.

⚡ The Problem

GNN datasets can be massive. ogbn-papers100M contains 111 Million nodes and 1.6 Billion edges.

• Standard approach (PyG/NetworkX): Tries to load the entire graph structure into RAM.
• The Result: MemoryError (OOM) on consumer hardware. You need 64GB+ RAM servers just to load the data.

🛠️ The Solution:

GraphZero abandons the "Load-to-RAM" model. Instead, it uses a custom Zero-Copy Architecture:

• Memory Mapping (mmap): The graph stays on disk. The OS only loads the specific "hot" pages needed for computation into RAM.
• Compressed CSR: A custom binary format (.gl) that compresses raw edges by ~60% (30GB CSV 13GB Binary).
• Parallel Sampling: OpenMP-accelerated random walks that saturate NVMe SSD throughput.

🏆 Benchmarks: GraphZero vs. PyTorch Geometric

Task: Load ogbn-papers100M (56GB Raw) and perform random walks. Hardware: Windows Laptop (16GB RAM, NVMe SSD).

Metric	GraphZero (v0.1)	PyTorch Geometric
Load Time	0.000000 s ⚡	FAILED (Crash) ❌
Peak RAM Usage	~5.1 GB (OS Cache)	>24.1 GB (Required)
Throughput	1,264,000 steps/s	N/A
Status	✅ Success	❌ OOM Error

Proof of Performance

Left: GraphZero loading instantly and utilizing OS Page Cache. Right: PyG crashing with Unable to allocate 24.1 GiB.

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📦 Installation

GraphZero is available on PyPI (Pre-Alpha):

pip install graphzero

Requirements: Python 3.8+, C++17 Compiler (MSVC/GCC), OpenMP.

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🚀 Quick Start

1. Convert Your Data

GraphZero uses a high-efficiency binary format (.gl). Convert your generic CSV edges list once.

import graphzero as gz

# Converts raw CSV (src, dst) to memory-mapped binary
# Handles 100M+ edges easily on minimal RAM
gz.convert_csv_to_gl(
    input_csv="dataset/edges.csv", 
    output_bin="graph.gl", 
    directed=True
)

2. High-Speed Sampling

Once converted, the graph is instantly accessible.

import graphzero as gz
import numpy as np

# 1. Zero-Copy Load (Instant)
g = gz.Graph("graph.gl")

# 2. Define Start Nodes (e.g., 1000 random nodes)
start_nodes = np.random.randint(0, g.num_nodes, 1000).astype(np.uint64)

# 3. Parallel Random Walk (node2vec / DeepWalk style)
# Returns: List of walks (flat or list-of-lists)
walks = g.batch_random_walk_uniform(
    start_nodes=start_nodes, 
    walk_length=10
)

print(f"Generated {len(walks)} steps instantly.")

⚙️ Under the Hood

GraphZero is built for Systems & GNN enthusiasts.

• Core: C++20 with nanobind for Python bindings.
• Parallelism: Uses #pragma omp with thread-local RNGs to prevent false sharing and lock contention.
• IO: Direct CreateFileMapping (Windows) and mmap (Linux) calls with alignment optimization (4KB/2MB pages).

🗺️ Roadmap

• v0.1 (Current): Topology-only support. Uniform Random Walks.
• v0.2: Columnar Feature Store (mmap support for Node Features ).
• v0.3: Weighted Edges & SIMD (AVX2) Neighbor Intersection.
• v0.4: Dynamic Updates (LSM-Tree based mutable graphs).
• v0.5: Pinned Memory Allocator for faster CPU GPU transfer.

📄 License

MIT License. Created by Krish Singaria (IIT Mandi).