Locality-Aware Data Placement for NUMA Architectures: Data Decoupling and Asynchronous Replication
Shuhan Bai, Haowen Luo, Bu-Rong Dong, Jian Bo Zhou, Fei Wu · 2025
Non-Uniform Memory Access (NUMA) architectures bring new opportunities and challenges to bridge the gap between computing power and memory performance. Their complex memory hierarchies feature non-uniform access performance, known as NUMA locality, indicating data placement and access without NUMA-awareness significantly impact performance. Existing NUMA-aware solutions often prioritize fast local access but at the cost of heavy replication overhead, suffering a read-write performance tradeoff and limited scalability. To overcome these limitations, this paper presents Ladapa, a scalable and high-performance locality-aware data placement strategy. The key insight is decoupling data into metadata and data layers, allowing independent management with adaptive asynchronous replication for lower overhead. Additionally, Ladapa employs multi-level metadata management leveraging fast caches for efficient data location, further boosting performance. Experimental results show that Ladapa outperforms typical replication techniques by up to 27.37× in write performance and 1.63× in read performance.