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Network pruning is an effective technique that reduces the computational costs of networks while maintaining accuracy. However, pruning requires expert knowledge and hyperparameter tuning, such as determining the pruning rate for each layer. Automatic pruning methods address this challenge by proposing an effective training-free metric to quickly evaluate the pruned network without fine-tuning. However, most existing automatic pruning methods only investigate a certain pruning granularity, and it remains unclear whether metrics benefit automatic pruning at different granularities. Neural architecture search also studies training-free metrics to accelerate network generation. Nevertheless, whether they apply to pruning needs further investigation. In this study, we first systematically analyze various advanced training-free metrics for various granularities in pruning, and then we investigate the correlation between the training-free metric score and the after-fine-tuned model accuracy. Based on the analysis, we proposed FreePrune score, a more general metric compatible with all pruning granularities. Aiming at generating high-quality pruned networks and unleashing the power of FreePrune score, we further propose FreePrune, an automatic framework that can rapidly generate and evaluate the candidate networks, leading to a final pruned network with both high accuracy and pruning rate. Experiments show that our method achieves high correlation on various pruning granularities and comprehensively improves the accuracy.

Computational storage drive (CSD) improves the data processing efficiency by processing the data within the storage. However, existing CSDs rely on the host-centric file systems to manage the data, where the layouts of files are retrieved by the host and sent to the CSD, resulting in additional I/O overhead and reduced processing efficiency, especially in heavy small file accesses. Moreover, the lack of consistency mechanisms poses potential consistency issues. To address these challenges, we propose LightFS, a lightweight host-CSD coordinated file system for the CSD file management. To reduce task offloading overhead, LightFS builds an index file $.ndpmeta$ which summarizes the files’ metadata and shares between the host and CSD to enable CSD to retrieve the file layout in storage directly. To ensure consistency, LightFS employs a metadata locker and an update synchronizer. The metadata locker leverages the out-of-place update feature of the flash to capture a snapshot of the file to be written without any data copy, while the update synchronizer triggers metadata updates by monitoring the addresses of written blocks to ensure that the modified file is successfully written to the CSD. We implement and evaluate LightFS on a real testbed, and the results demonstrate that LightFS achieves $3.66\times $ performance improvement on the average in real-world operations.

Block-level data deduplication is a widely used technology for saving storage space by filtering the data blocks with the same hash value. However, existing block-level data deduplication approaches either ignore the data consistency of deduplication or suffer severe performance degradation for providing consistency guarantees. In this article, we propose Consistency-Aware Deduplication (CADedup+) to achieve high-performance block-level data deduplication with data consistency. The main idea of CADedup+ is to achieve an efficient journaling mechanism for deduplication by taking advantage of persistent memory (PM), such as byte-addressability and near-DRAM access latency. To balance the tradeoffs between performance and consistency requirements in data deduplication, we carefully design three modes of journaling mechanism, i.e., writeback mode, ordered mode, and journal mode, for CADedup+. We properly place the deduplication metadata of CADedup+ onto the DRAM- PM hybrid memory architecture to minimize PM costs according to the features of metadata updates. The deduplication metadata on PM is managed by a set of metadata transactions and updated with the help of the efficient hardware atomic operations provided by CPU. We implement CADedup+ in the generic block layer in Linux kernel 4.9.0. We conduct extensive experiments on Intel Optane PMEM to evaluate CADedup+ with typical benchmarks. Experimental results show that CADedup+ can reduce 63%–70% write volume and 50%–60% I/O latency over Dmdedup, a widely used open-source block-level data deduplication system, while ensuring deduplication consistency.