PartitionedVC: Optimizing External Memory Graph Analytics

Thus paper introduces PartitionedVC, an innovative external memory graph analytics framework designed to enhance the processing of large graphs that exceed main memory capacity, especially when utilizing SSDs. The core of PartitionedVC's improvement over existing systems like GraphChi lies in its use of a compressed sparse row (CSR) based graph storage for efficiently loading only active vertices, unlike shard-based frameworks that load entire graph segments regardless of activity. To overcome CSR's limitation with random updates, PartitionedVC employs a multi-log update mechanism, dedicating a separate log for each vertex interval to streamline update processing and eliminate costly external sorting. Furthermore, it incorporates an edge-log optimizer that proactively logs outgoing edges of likely active vertices, significantly reducing read amplification and overall performance bottlenecks associated with SSD page granular access.

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