.png){kind=icon}
返回
CEGraph: Cache-Efficient Management for Streaming Graph Processing
.png){kind=icon}
OA
AI
DOI：10.1109/TCAD.2025.3575362.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Efficient processing of streaming graphs is crucial to improve system performance. Due to the highly irregular and frequent access to data in streaming graph processing, existing cache management methods are difficult to accurately predict cache behavior, resulting in serious cache misses. To address the issues, we propose CEGraph, an efficient cache management approach for streaming graph processing. Specifically, for graph data, we propose a cache replacement policy based on vertex importance. This policy accurately evaluates the importance of vertices in the incremental processing of streaming graphs from our proposed three factors: 1) the association degree of affected state of a vertex; 2) the path distance of a vertex; and 3) whether a vertex will be updated. Vertices with high importance are identified and kept in the cache to reduce cache thrashing. Experimental results reveal that compared with LRU, DRRIP and Grasp, CEGraph reduces the last-level cache (LLC) misses by an average of 22.93% (maximum 34.27%), 20.87% and 11.91%, respectively. Compared with the state-of-the-art cache management method P-OPT, CEGraph reduces the LLC misses by 6.46% on average, therefore demonstrating the effectiveness of CEGraph.
Keyword:
Cache management
last-level cache (LLC)
streaming graph processing
vertex importance
期刊
I
IF:
2.9
论文数:
680
被引数:
9.6K
机构
引用论文
DepGraph: A Dependency-Driven Accelerator for Efficient Iterative Graph ProcessingDepGraph：一种基于依赖的加速器，用于高效的迭代图处理
PREAI
分享收藏Local Higher-Order Graph Clustering.png)
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
OAAI
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningIF0
OAAI分享收藏SpotLightProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningOAAI
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data MiningIF0
OAAI分享收藏
查看更多内容