In real-world scenarios, the long-tailed distribution phenomenon of datasets leads to extremely poor performance of models in recognizing minority-class samples, which limits the practical applications of the models. To address this challenge, we propose a Multi-expert System based on Dual-Scale Contrastive Distillation (MSDSCD). Specifically, to address the issues of minority class signals being overshadowed and insufficient knowledge transfer in most distillation strategies, we design the Dual-Scale Contrastive Distillation (DSCD) module. This module achieves more sufficient knowledge transfer by constructing global-local alignment. In addition, we construct Mask-Based Local Distribution Learning (MBLDL) to focus on local key decision regions. Furthermore, we adopt a multi-threshold Top-K strategy to capture effective information in classes with different frequencies. Extensive experimental results on multiple datasets demonstrate the effectiveness of our method.

The Log-Structured Merge (LSM) tree is a widely used storage engine in key-value systems, but its compaction process is a major performance bottleneck due to high computational and I/O demands. While GPU-accelerated compaction (GPU Compaction) has emerged as an efficient solution, state-of-the-art approaches rely on CPU-centric GPU-SSD direct I/O stacks, suffering from synchronization overhead between IO and GPU Computation and limited GPU-SSD IO bandwidth. To address these limitations, we propose GCLComp, a GPUCentric LSM Compaction Strategy based on GPU-Initiated Direct Storage (GIDS). It is a computational storage architecture application based on GPU and GIDS. GCLComp introduces MiniFS, an efficient user-space Host-GPU shared-driver file storage system specifically designed for GPU-accelerated LSM compaction. It enables GPU-initiated I/O for compaction and higher GPU-SSD IO throughput. This allows the GPU to independently read the input files required for compaction from the SSD, perform compaction, and write the output to SSD, minimizing CPU involvement. GCLComp further employs a GPU IO-Computation Pipeline using CUDA streams to seamlessly orchestrate I/O and computation, eliminating synchronization overhead. The experimental results show that MiniFS improves read and write throughput by 52 % and 34 %, respectively, over the state-of-the-art. Based on that, GCLComp achieves up to 19.16 % higher compaction throughput, 21.98 % higher average Fillrandom throughput, 17.59 % lower average Fillrandom latency, 97.27 % lower Fillrandom P999 tail latency and 31.85% higher ReadWriteMix throughput under the KV store, compared to SOTA. We also find that compaction throughput scales nearly linearly with pipeline batch size by a sensitivity analysis.

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.

This paper proposes a new feature of fingerprint, called corner-cue. It is based on the curvature of fingerprint ridges. To extract the corner-cue, we first compute the curvature of fingerprint ridges and find the local maximum curvature points. Without regard to the high curvature points near minutiae, corner-cues are obtained. Corner-cues are further utilized in the matching stage to enhance the system's performance. Since high curvature points are important features of a fingerprint, the proposed method can obtain better results than conventional solely minutiae-based methods. Experimental results illustrate its effectiveness.