aprender-graph 0.29.0

Table of Contents

• Installation
• Usage
• Architecture
• API Reference
• Examples
• Testing
• Contributing
• License

GPU-First Embedded Graph Database for Code Analysis

Call graphs, dependencies, AST traversals with 10-250x GPU acceleration.

Features

• CSR Storage: Compressed Sparse Row for O(1) neighbor queries
• GPU Acceleration: BFS (250x), PageRank (100x) via WGSL shaders
• Parquet Persistence: DuckDB-inspired columnar storage
• Louvain Clustering: Community detection for code modules
• Anti-Pattern Detection: God Class, Circular Dependencies, Dead Code
• VRAM Paging: Morsel-based tiling for large graphs

Installation

[dependencies]
trueno-graph = "0.1"

# Optional: GPU acceleration
trueno-graph = { version = "0.1", features = ["gpu"] }

Quick Start

use trueno_graph::{CsrGraph, NodeId, pagerank, bfs};

let mut graph = CsrGraph::new();
graph.add_edge(NodeId(0), NodeId(1), 1.0)?;
graph.add_edge(NodeId(0), NodeId(2), 1.0)?;

// Graph algorithms
let reachable = bfs(&graph, NodeId(0))?;
let scores = pagerank(&graph, 20, 1e-6)?;

// Persistence
graph.write_parquet("graph").await?;

GPU Usage

use trueno_graph::gpu::{GpuDevice, GpuCsrBuffers, gpu_bfs};

let device = GpuDevice::new().await?;
let buffers = GpuCsrBuffers::from_csr_graph(&device, &graph)?;
let result = gpu_bfs(&device, &buffers, NodeId(0)).await?;

Performance

Architecture

┌─────────────────────────────────────────────┐
│           Graph Algorithms                   │
│   (BFS, PageRank, Louvain, Anti-Patterns)    │
├──────────┬──────────────────────────────────┤
│  GPU     │  CPU                              │
│  (WGSL)  │  (CSR iterators)                  │
├──────────┴──────────────────────────────────┤
│         CSR Graph Storage                    │
│   (Compressed Sparse Row, O(1) neighbors)    │
├─────────────────────────────────────────────┤
│       Parquet Persistence Layer              │
│   (columnar I/O, DuckDB-compatible)          │
└─────────────────────────────────────────────┘

• CSR Storage: Compressed Sparse Row format for cache-friendly traversals and O(1) neighbor access
• GPU Backend: WGSL compute shaders for BFS and PageRank with automatic VRAM paging
• Algorithms: BFS, PageRank (power iteration), Louvain community detection, anti-pattern analysis
• Persistence: Parquet-based columnar storage for graph serialization

API Reference

CsrGraph

Core graph data structure:

let mut graph = CsrGraph::new();
graph.add_edge(NodeId(0), NodeId(1), 1.0)?;
let neighbors = graph.neighbors(NodeId(0));

Graph Algorithms

let reachable = bfs(&graph, NodeId(0))?;          // Breadth-first search
let scores = pagerank(&graph, 20, 1e-6)?;          // PageRank scores
let communities = louvain(&graph)?;                 // Community detection

GPU Acceleration

let device = GpuDevice::new().await?;
let buffers = GpuCsrBuffers::from_csr_graph(&device, &graph)?;
let result = gpu_bfs(&device, &buffers, NodeId(0)).await?;

Examples

cargo run --example basic_graph --release
cargo run --example pagerank_demo --release
cargo run --example gpu_bfs --features gpu --release

Testing

cargo test --lib          # Unit tests
cargo test                # All tests including integration
make coverage             # Coverage report (target: >=95%)
make bench                # Performance benchmarks

Property-based tests verify graph invariants (edge counts, BFS reachability, PageRank convergence).

Development

make test      # Run tests
make coverage  # >=95% coverage
make bench     # Benchmarks

Contributing

Contributions are welcome! Please see the CONTRIBUTING.md guide for details.

MSRV

Minimum Supported Rust Version: 1.75

License

MIT

Part of the Aprender monorepo — 70 workspace crates.