Approximate Nearest Neighbor Search (ANNS) is widely used in various fields, including database systems, recommendation engines, and large language models. As data dimension and size continue to expand, many studies explore GPU acceleration for graph-based ANNS. While previous methods use large batch to maximize throughput, they often lead to increased latency. In contrast, small batch is more effective for online lowlatency applications, as it minimizes batch accumulation time. However, employing small batch on GPU presents challenges. First, the query bubble issue in batch processing negatively impacts both latency and GPU utilization. Second, current GPU search methods incur excessive sorting overhead, and introduce additional TopK-merging overhead on GPU. To address these challenges, we propose ALGAS, a low-latency GPU search system designed for small batch. ALGAS employs dynamic batching based on persistent GPU kernel function to optimize query bubble. Additionally, it employs beam extend to reduce sorting overhead, especially effective at high recall rate. It also eliminates TopK-merging overhead via GPU-CPU cooperation. Furthermore, it employs an adaptive GPU tuning scheme to optimize resource utilization. We compare ALGAS with the state-of-the-art graph-based works. ALGAS reduces latency by up to 21.9%-35.4% and increases throughput by up to 27.8%-55.2% under various real-world datasets.

LSM-tree-based Key-value systems are widely used in many internet applications, known for their superior write performance. Compaction operations, responsible for maintaining the pyramidal storage structure of the LSM-tree to ensure acceptable read performance, pose significant performance bottlenecks. The application of high-performance SSDs and lightweight user-space file systems in LSM storage alleviates IO bandwidth bottlenecks, but it amplifies the computational resource consumption of compaction when KV is small and medium, shifting the bottleneck from IO to computation. To mitigate the computational bottleneck of compaction, we propose GPComp, a GPU-accelerated compaction strategy for high-performance SSDs with lightweight user-space file systems. GPComp features efficient GPU compaction units and a CPU-GPU cooperative compaction acceleration strategy. We introduce a user-space file system specifically designed for LSM storage, TopFS-GPU. It implements an SPDK-based SSD-GPU P2P IO stack to enhance data transfer throughput in GPU-accelerated Compaction. It features an asynchronous write-back cache strategy, facilitating mixed read-write workloads in LSM-tree-based key-value systems. Additionally, our pipeline mechanism overlaps GPU computations with SSD-GPU IO, increasing system throughput. Implemented based on LevelDB, GPComp shows up to a 2.65x increase in average write throughput and a 2.32x improvement in mixed read-write throughput, with a P99 tail latency reduction of up to 169.65% compared to state-of-the-art methods.