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.

LSM-tree-based key-value stores or KV stores are widely deployed in modern cloud storage systems thanks to high data storage efficiency and retrieval capabilities. The compaction process in the LSM-tree, however, results in severe performance bottlenecks, especially in scenarios involving large volumes of data. While key-value separation methods mitigate the performance bottlenecks caused by compaction, the existing methods do not fully address merge-sorting during compaction and expensive garbage collection (GC). We propose gParaKV, a GPGPU-empowered KV store with a KV separation mechanism, leveraging the GPGPU parallel technology to accelerate merge-sorting in compaction and GC. gParaKV embraces unique features like a GPGPU bitmap structure, parallel data marking, and a parallel GC mechanism. These critical components effectively curtail the overhead of merge-sorting and GC operations by virtue of parallel computing. We compare it with state-of-the-art KV stores (e.g., RocksDB, BlobDB, Wisckey, DiffKV, UniKV, and HPDK) under various workloads. The experimental results show that gParaKV can improve the write performance and GC efficiency compared to the existing key-value separation-based KV stores.

Open Relation Extraction (OpenRE) aims at clustering relation instances to extract relation types. By learning relation patterns between named entities, it clusters semantically equivalent patterns into a unified relation cluster. Existing clustering-based OpenRE methods only consider the information of the instance itself, ignoring knowledge of any relations between instances. Therefore, a Cross-Attention Fusion based Graph Convolution Auto-Encoder (CAGCE) method for Open Relation Extraction is proposed. The Auto-Encoder learns the semantic information of the sentence instance itself, and the Graph Convolution Network learns the relational similarity information between sentences. Then, the two heterogeneous representations are crossed and fused layer-by-layer through a cross-attention fusion mechanism. Finally, the fused features are used for clustering to form the relation types. A comparison with baseline models using the FewRel and NYT-FB datasets shows the effectiveness and superiority of the proposed method.