Keyphrase extraction aims to automatically identify concise phrases that effectively represent the content of a document. While recent methods leveraging pre-trained language models (PLMs) have significantly improved the extraction of keyphrases with strong global semantic relevance, they often fall short in capturing the local contextual importance of keyphrases tied to specific subtopics dispersed in a document. In this paper, we propose a novel context-centric framework, MUDY, that effectively captures multi-granular contextual salience of candidate keyphrases. MUDY employs two complementary components: (1) a prompt-based scoring that estimates the generation likelihood of each candidate keyphrase, augmented with candidate-aware weighting to better reflect its local contextual importance, and (2) a self-attention-based scoring that utilizes multi-granular attention patterns from PLMs to assess candidate significance at both the document-wide and segment-specific levels. Evaluations on four real-world datasets demonstrate that MUDY outperforms state-of-the-art baselines in top-k accuracy at various cutoff thresholds. In-depth quantitative and qualitative analyses further highlight the efficacy of context-centric keyphrase extraction with multi-granular saliency. For reproducibility, the source code of MUDY is available at https://github.com/HgKang1/MUDY.

Several key-value stores such as RocksDB and MongoDB are implemented on the file system using the Log-Structured Merge-Tree (LSM-tree). The LSM-tree involves high compaction overhead. To minimize this overhead, WiscKey, the state-of-the-art LSM-tree, separates key and value, appends the value to the Value Log file, and LSM-tree manages only the key and Value Log offset. This minimizes the compaction overhead by reducing the number of SSTables managed by the LSM-tree. However, WiscKey still has a high I/O stack overhead that must go through the OS file system and block-layer. Therefore, this paper proposes iLSM-SSD that implements WiscKey in SSD and supports near-data processing. iLSM-SSD has the following features: (i) iLSM-SSD implements a key-value separation based LSM-tree in a limited memory space inside the SSD. (ii) The Value Log offset update management overhead incurred during the Value Log cleaning has a significant performance impact on CPU and memory-constrained SSD environments. To minimize this overhead, iLSM-SSD implements Scattered Logging, which reuses invalidated Value Log pages on the Value Log. (iii) iLSM-SSD manages the data layout internally. This enables iLSM-SSD to eliminate the need for file system interactions to obtain the data layout for in-storage processing on traditional block-interface-based SSDs. We prototyped the iLSM-SSD on the Cosmos+ OpenSSD platform in a Linux environment. Extensive evaluations with synthetic benchmarks have shown that the PUT performance of iLSM-SSD is 1.6-4 times higher than that of WiscKey implemented in RocksDB.

In Manycore server environment, we observe the performance degradation in parallel writes and identify the causes as follows - (i) When multiple threads write to a single file simultaneously, the current POSIX-based F2FS file system does not allow this parallel write even though ranges are distinct where threads are writing. (ii) The high processing time of Fsync at file system layer degrades the I/O throughput as multiple threads call Fsync simultaneously. (iii) The file system periodically checkpoints to recover from system crashes. All incoming I/O requests are blocked while the checkpoint is running, which significantly degrades overall file system performance. To solve these problems, first, we propose file systems to employ a fine-grained file-level Range Lock that allows multiple threads to write on mutually exclusive ranges of files rather than the course-grained inode mutex lock. Second, we propose NVM Node Logging that uses NVM as an extended storage space to store file metadata and file system metadata at high speed during Fsync and checkpoint operations. In particular, the NVM Node Logging consists of (i) a fine-grained inode structure to solve the write amplification problem caused by flushing the file metadata in block units and (ii) a Pin Point NAT (Node Address Table) Update, which can allow flushing only modified NAT entries. We implemented Range Lock and NVM Node Logging for F2FS in Linux kernel 4.14.11. Our extensive evaluation at two different types of servers (single socket 10 cores CPU server, multi-socket 120 cores NUMA CPU server) shows significant write throughput improvements in both real and synthetic workloads.