Abstract: With the rapid growth of graph computing,modern graph platforms routinely execute a large number of concurrent graph analytics tasks to extract the latent value in massive datasets.Consequently,concurrent graph processing has been widely adopted in domains,including intelligent education,public administration,and news media.However,most existing graph proces-sing systems are originally designed for single-task execution and suffer from excessive redundant data accesses when handling concurrent workloads.Although prior studies have observed significant redundancy in in-memory graph data across concurrent tasks and have attempted to exploit temporal and spatial locality to share underlying graph data,they largely overlook the data locality in private state updates.This limitation leads to low cache utilization and,ultimately,degraded system throughput.To address this challenge,this paper proposes CCG,a locality-aware cache management strategy for concurrent graph analysis,which fully exploits both temporal and spatial locality across tasks to reduce redundant data accesses and synchronization overhead.Specifically,CCG efficiently buffers and incrementally merges redundant updates,leveraging data locality to perform high-throughput batch updates in memory.This design minimizes access costs,mitigates cache thrashing,and significantly improves concurrency performance.Moreover,CCG employs a multi-level cache hierarchy to enable layered buffering and merging,thereby eliminating synchronization and locking overhead during private state updates.Experimental results show that CCG improves system throughput by 2.3×～7.8× over GRASP.

Key words: Concurrent graph analysis, Cache architecture, Data locality, Cache thrashing, Throughput