Work

GDH+: A GPGPU-Empowered Gradient Data Hierarchy and Key-Value Separation for Optimizing LSM-Tree-Based KV Stores

Year: 2026

Type: article

Abstract: The rapid growth of unstructured data has driven the widespread adoption of LSM-tree-based key-value stores (KV stores). The write amplification resulting from compaction in LSM-trees causes a performance bottleneck. Existing solutions attempt to address this issue through key-value separation strategies. However, these studies fail to optimize the memory components of LSM-trees or provide efficient garbage collection (GC) strategies that achieve high performance while minimizing CPU overhead. These limitations motivate us to propose a GPGPU-empowered gradient data hierarchy and key-value separation for optimizing KV stores, named GDH+ . We utilize GPGPU acceleration for sorting and flushing operations, optimizing the memory components of the LSM-tree. Additionally, we enhance read performance with an LRU-based memory component that distinguishes between hot and cold data, in combination with an adaptive migration strategy. Furthermore, we propose an in-place GC strategy to reduce CPU overhead while maintaining high performance. GDH+ achieves a 2×, 1.5×, and 40% improvement in write performance, read performance, and CPU utilization, respectively, compared to state-of-the-art KV stores.

Source: ACM Transactions on Architecture and Code Optimization

DOI: 10.1145/3799980

Authors: Hui Sun, Xiangxiang Jiang, Jinfeng Xu, Enhui Wang, Zheng Fang, Yi Zou, Yinliang Yue, Xiao Qin

Institutions: Anhui University, Columbus State University, Auburn University

Language: English

Topic: Advanced Data Storage Technologies

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Aeronautics and Space Administration, National Natural Science Foundation of China, National Highway Traffic Safety Administration

Awards: G2323910686, G27958311, 面向近数据计算的键值存储关键技术研究