parallel graph algorithm
**Parallel Graph Algorithms** — processing large-scale graphs (social networks, web graphs, road networks) using parallel hardware, challenging because graphs have irregular access patterns and poor data locality.
**Why Graphs Are Hard to Parallelize**
- Irregular structure: Unlike arrays, no predictable access pattern
- Poor locality: Following edges jumps randomly through memory
- Load imbalance: Some vertices have millions of edges, others have few
- Data dependencies: BFS levels, shortest paths have sequential dependencies
**Key Parallel Graph Algorithms**
**Parallel BFS**
- Level-synchronous: Process all vertices at current level in parallel
- Each level: For all frontier vertices, explore neighbors → build next frontier
- GPU-accelerated BFS can process billion-edge graphs in seconds
**Parallel PageRank**
- Iterative: Each vertex updates its rank based on neighbors' ranks
- All vertex updates within an iteration are independent → parallel
- GPU implementation: 10–100x faster than single-thread CPU
**Graph Frameworks**
- **Gunrock**: GPU graph analytics library. Push/pull traversal strategies
- **GraphBLAS**: Express graph algorithms as sparse matrix operations
- **Pregel/Giraph**: Vertex-centric distributed graph processing
- **DGL / PyG**: Graph neural network frameworks (GPU-accelerated)
**Representation Matters**
- CSR (Compressed Sparse Row): Good for GPU — coalesced row access
- Edge list: Good for streaming/sorting-based algorithms
- Adjacency matrix: Only for dense graphs (sparse wastes memory)
**Graph parallelism** is an active research area — no single approach dominates due to the diversity of graph structures and algorithms.