A ReRAM-Based Processing-in-Memory Framework for LSM-Based Key-Value Store
Zehao Chen, Kai Zhang, Qian Wei, Nan Su, Yuhao Zhang, Zhaoyan Shen, Dongxiao Yu, Lei Ju · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2025
Log-structured merge (LSM) tree-based key-value (KV) stores organize writes into hierarchical batches to optimize write performance. However, the notorious compaction process and multi-level query mechanism of LSM-tree severely hurt system performance. Our preliminary experiments show that (1) When compaction occurs in the L0 and L1 of the LSM-tree, it may saturate system computation and memory resources, ultimately causing the entire system to stall, and (2) large number of iterative retrievals across multiple levels is usually required to locate the queried data, while redundant key range overlap in L0 further increases the overhead. Based on these observations, we introduce Re-LSM+, a ReRAM-based Processing-in-Memory framework for LSM-based Key-Value Stores. In Re-LSM+, we offload compaction tasks from the higher levels of the LSM-tree to the PIM processing part. A highly parallel ReRAM compaction accelerator is designed by breaking down the three-phase compaction process into basic logic operations. Additionally, we design an index table and a multi-layer Bloom filter for different levels to improve the query efficiency of the LSM-tree. Evaluation results from db_bench show that Re-LSM+ achieves a 2.37× improvement in random write throughput compared to RocksDB. Furthermore, the ReRAM-based compaction accelerator achieves a 68.16× speedup over the CPU-based implementation and reduces energy consumption to 25.5×.