DMG：一种可扩展且高效的内存解耦图形处理系统
DMG: A Scalable and Efficient Memory-Disaggregated Graph Processing System
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中文总结 AI 辅助
研究针对传统图形处理系统资源利用不足及现有DM图形处理系统不实用的问题，提出DMG系统，通过优化图形存储检索、更新协调及负载均衡等方法，实现计算和内存资源弹性扩展，性能提升且降低缓存需求。
中文摘要 AI 辅助
传统图形处理系统基于单片服务器构建，导致数据中心资源利用不足。虽解耦内存（DM）架构出现，但现有DM上的图形处理系统不实用，依赖不可扩展架构且计算端缓存需求大。本文提出DMG，首个实用的DM图形处理系统，具有卓越扩展性和缓存效率。它提出DM友好的图形存储并优化检索，采用自适应更新协调器减少更新传播成本，用两阶段工作负载管理器实现快速有效负载均衡。实验结果表明，与基于DM的现有系统相比，DMG可弹性扩展计算和内存资源，性能提升4.9倍，有效降低计算端缓存需求18.9倍。
英文摘要
Traditional graph processing systems are built on monolithic servers, which couple a fixed ratio of compute and memory resources but often result in resource under-utilization in data centers. Although the disaggregated memory (DM) architecture has emerged to address this inefficiency, we identify that existing graph processing systems on DM remain highly impractical. They rely on unscalable architectures that fail to scale beyond a single memory node and a single compute node, and they require compute-side caches that are orders of magnitude larger than conventional practice in DM. To this end, this paper presents DMG, the first practical graph processing system on DM, which demonstrates superior system scalability and cache efficiency while delivering high performance. To improve efficiency of graph retrieval on DM, DMG proposes a DM-friendly graph store with retrieval optimizations. To mitigate costly update propagation, DMG presents an adaptive update coordinator that coordinates compute and memory nodes to perform update propagation with low overhead. To enable fast and effective load balancing, DMG employs a two-stage workload manager that includes a coarse-grained initial partitioning and a fine-grained runtime re-scheduling. Experimental results substantiate that compared with the state-of-the-art DM-based graph processing system, DMG can elastically scale up both compute and memory resources, delivering up to 4.9X better performance and accommodating graphs with ever-increasing sizes; meanwhile, it effectively tames the compute-side cache demands by up to 18.9X, positioning itself as a DM-ready solution in practice.
