Write amplification (WA) from migrating valid pages during garbage collection (GC) degrades SSD performance and lifespan. Although stream management based on high-level software semantics reduces WA, existing solutions require host modifications, hindering their adoption. We introduce StreamCSD, an SSD-autonomous stream management approach using in-storage content learning, eliminating host-side changes. Leveraging compression ratios from embedded compressors in computational storage drives (CSDs), StreamCSD employs a streaming Kmeans algorithm to cost-efficiently cluster data into streams. Evaluations show that StreamCSD reduces WA from 1.7 to 1.06 under multimodal generative AI workloads, matching state-of-the-art methods with minimal impact on bandwidth. StreamCSD operates without host modifications, promoting broader adoption of multi-stream SSDs.

Integrating data compression capability into SSDs has demonstrated great potential to improve the utilization and lifetime of the storage device and also the performance of the entire system. It is advocated to add a hardware engine into the SSD for low-latency compression and decompression. However, this requires a new and long hardware product development cycle, which would prevent current storage systems from reaping the benefits of in-SSD compression. In this paper, we explore a software-based in-SSD compression solution, which can be delivered to users quickly through a simple SSD firmware update. The most critical challenge is the severe performance bottleneck caused by compression and decompression, as the in-SSD embedded CPU has quite limited computing power. To tackle this challenge, we propose a host-assisted computational storage device, called HA-CSD. It employs an offline, data hotness- and compressibility-aware compression strategy to remove compression from the critical write I/O path. A novel decompression architecture is devised to utilize the powerful host CPU for fast decompression. We implement HA-CSD in a commercial enterprise SSD with a code change of more than 25K lines in the host NVMe driver and SSD firmware. Experimental results show that HA-CSD achieves 2.1GB/s and 5.2GB/s read and write bandwidth. Compared with RocksDB built-in compression, HA-CSD can increase the YCSB benchmark throughput by up to 5.7×, and improve the host CPU efficiency significantly.

SZ is a lossy floating-point data compressor that excels in compression ratio and throughput for high-performance computing (HPC), time series databases, and deep learning applications. However, SZ performs poorly for small chunks and has slow decompression. We pinpoint the Huffman tree in the quantization factor encoder as the bottleneck of SZ. In this paper, we propose ADT-FSE, a new quantization factor encoder for SZ. Based on the Gaussian distribution of quantization factors, we design an adaptive data transcoding (ADT) scheme to map quantization factors to codes for better compressibility, and then use finite state entropy (FSE) to compress the codes. Experiments show that ADT-FSE improves the quantization factor compression ratio, compression and decompression throughput by up to 5×, 2× and 8×, respectively, over the original SZ Huffman encoder. On average, SZ_ADT is over 2× faster than ZFP in decompression. Case studies of the TDengine time series database and HDF5 file store confirm that SZ_ADT significantly boosts user-perceived application performance. In addition, ADT-FSE makes the compression ratio prediction of SZ_ADT easy and accurate, and has the potential to dramatically reduce the area size of SZ hardware implementation.

Abstract Within Open Relation Extraction (ORE) tasks, the Zero-shot ORE method is to generalize undefined relations from predefined relations, while the Unsupervised ORE method is to extract undefined relations without the need for annotations. However, despite the possibility of overlap between predefined and undefined relations in the training data, a unified framework for both Zero-shot and Unsupervised ORE has yet to be established. To address this gap, we propose U-CORE: A Unified Deep Cluster-wise Contrastive Framework for both Zero-shot and Unsupervised ORE, by leveraging techniques from Contrastive Learning (CL) and Clustering.1 U-CORE overcomes the limitations of CL-based Zero-shot ORE methods by employing Cluster-wise CL that preserves both local smoothness as well as global semantics. Additionally, we employ a deep-cluster-based updater that optimizes the cluster center, thus enhancing the accuracy and efficiency of the model. To increase the stability of the model, we adopt Adaptive Self-paced Learning that effectively addresses the data-shifting problems. Experimental results on three well-known datasets demonstrate that U-CORE significantly improves upon existing methods by showing an average improvement of 7.35% ARI on Zero-shot ORE tasks and 15.24% ARI on Unsupervised ORE tasks.

We propose a new design of a hardware accelerator for processing regular expression to speedup text search inside SSD storage (Processing in Storage: PIS). The unique features include parallel processing of 32 streams to quickly identify the first matched character under scan mode and match four characters concurrently under matching mode. In addition, we present a new approach of combining forward and backward scan to accomplish the first character search efficiently. Our experimental results show that the new parallel algorithm reduces the depth of logic circuit and the hybrid architecture performs as well as the Linux Grep algorithm does.