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.