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Write-Optimized B^(+) Tree Index Technology for Persistent Memory

Journal of Computer Science & Technology 2021年第5期36卷 1037-1050页

作者： Rui-Xiang Ma Fei Wu Bu-Rong Dong Meng Zhang Wei-Jun Li Chang-Sheng Xie Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430070China Key Laboratory of Information Storage System Ministry of Education of ChinaWuhan 430070China Engineering Research Center of Data Storage Systems and Technology Huazhong University of Science and Technology Wuhan 430070China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430070China Shenzhen DAPU Microelectronics Co. LtdShenzhen 518116China

Due to its low latency,byte-addressable,non-volatile,and high density,persistent memory(PM)is expected to be used to design a high-performance storage ***,PM also has disadvantages such as limited endurance,thereby proposing challenges to traditional index technologies such as B(+)tree.B(+)tree is originally designed for dynamic random access memory(DRAM)-based or disk-based systems and has a large write amplification *** high write amplification is detrimental to a PM-based *** paper proposes WO-tree,a write-optimized B(+)tree for ***-tree adopts an unordered write mechanism for the leaf nodes,and the unordered write mechanism can reduce a large number of write operations caused by maintaining the entry order in the leaf *** the leaf node is split,WO-tree performs the cache line flushing operation after all write operations are completed,which can reduce frequent data flushing ***-tree adopts a partial logging mechanism and it only writes the log for the leaf *** inner node recognizes the data inconsistency by the read operation and the data can be recovered using the leaf node information,thereby significantly reducing the logging ***,WO-tree adopts a lock-free search for inner nodes,which reduces the locking overhead for concurrency *** evaluate WO-tree using the Yahoo!Cloud Serving Benchmark(YCSB)*** with traditional B(+)tree,wB-tree,and Fast-Fair,the number of cache line flushes caused by WO-tree insertion operations is reduced by 84.7%,22.2%,and 30.8%,respectively,and the execution time is reduced by 84.3%,27.3%,and 44.7%,respectively.

关键词： persist memory B^(+)tree write amplication consistency YCSB(Yahoo!Cloud Serving Benchmark)

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BlzFS: Crash Consistent Log-structured File system Based on Byte-loggable Zone for ZNS SSD

BlzFS: Crash Consistent Log-structured File System Based on ...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Wenjie Qi Zhipeng Tan Ziyue Zhang Jing Zhang Chao Yu Ying Yuan Shikai Tan Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science & Technology Huazhong University of Science & Technology Guangdong Oppo Mobile Telecommunications Corp. Ltd

Emerging Zoned Namespace (ZNS) SSDs provide hosts with fine-grained, performance-predictable storage management. The ZNS SSD divides its address space into zones, and each zone must be written sequentially and cannot be overwritten. However, the write constraint results in frequent small modifications of file metadata to record the latest logical block addresses of the updated data blocks, increasing the overhead of fsync() system call. We comprehensively analyze the metadata overhead of fsync on a real ZNS SSD and find that (1) the update size of most metadata blocks (e.g., tens of bytes) is far less than the IO unit of the zone (e.g., 4 KiB), and frequent fsyncs cause severe metadata write amplification; (2) metadata block IOs significantly increase the fsync latency, decreasing the throughput of *** reduce the metadata overhead of fsync, we build the byte-loggable zone based on the persistent write cache of the opened zone, a novel extension of ZNS, which can be appended at byte granularity instead of block. Based on the byte-loggable zone, we present BlzFS, a high-performance and crash-consistent file system for ZNS SSD. Firstly, BlzFS maintains the update ranges of the metadata block by the update bitmap. Secondly, BlzFS ensures the persistence and consistency of metadata by logging the partial updates of metadata blocks to the device’s byte-loggable zone. Finally, BlzFS restores metadata consistency by replaying the metadata update log and scanning the list of recent allocated metadata blocks. In the evaluation, BlzFS shows a 33.5%∼96.1% reduction in metadata write traffic and a 1.37×∼1.91× throughput improvement compared with the original ZNS storage system.

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Hierarchical Recording Architecture for Three-Dimensional Magnetic Recording

arXiv

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arXiv 2025年

作者： Jian, Yugen Luo, Ke Chen, Jincai Fong, Xuanyao Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Computer of Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan430074 China

Three-dimensional magnetic recording (3DMR) is a highly promising approach to achieving ultra-large data storage capacity in hard disk drives. One of the greatest challenges for 3DMR lies in performing sequential and correct writing of bits into the multi-layer recording medium. In this work, we have proposed a hierarchical recording architecture based on layered heat-assisted writing with a multi-head array. The feasibility of the architecture is validated in a dual-layer 3DMR system with FePt-based thin films via micromagnetic simulation. Our results reveal the magnetization reversal mechanism of the grains, ultimately attaining appreciable switching probability and medium signal-to-noise ratio (SNR) for each layer. In particular, an optimal head-to-head distance is identified as the one that maximizes the medium SNR. Optimizing the system’s noise resistance will improve the overall SNR and allow for a smaller optimal head-to-head distance, which can pave the way for scaling 3DMR to more recording layers. Copyright © 2025, The Authors. All rights reserved.

关键词： Hard disk storage

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Decision-Feedback Single-Layer Read Reconstruction and Separation for Three-dimensional Magnetic Recording

Decision-Feedback Single-Layer Read Reconstruction and Separ...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Yanzhe Liao Kezheng Zhang Yugen Jian Shaobing Wang Jincai Chen Ping Lu Ke Luo School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Public Health Hubei University of Medicine Shiyan China

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

Three-dimensional magnetic recording (3DMR) based on energy-assisted recording techniques by either heat or microwave is a leading storage solution. Separation and detection are impeded by inter-symbol interference, inter-track interference, and inter-layer interference, especially when using traditional tunneling magnetoresistive (TMR) readers. To support a multi-layer magnetic recording system at an recording areal density of 4 Tbits/inch 2 on a dual-layer recording medium, we propose a neural network estimator to reconstruct the top-layer read response signal from the decision feedback of a Viterbi detector. Then we separate the bottom-layer signal by subtracting the reconstructed top-layer signal from the mixed readback signal. For the critical bound of channel bit error rate (BER), our method gains over 7 dB than a multiple-output detecting system.

关键词： Viterbi algorithm Magnetic separation Bit error rate Neural networks Interference Microwave theory and techniques Tunneling magnetoresistance

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FAGC: Free Space Fragmentation Aware GC Scheme based on Observations of Energy Consumption

FAGC: Free Space Fragmentation Aware GC Scheme based on Obse...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Lihua Yang Zhipeng Tan Fang Wang Yang Xiao Wei Zhang Biao He Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology College of Science and Technology National University of Defense Technology China Huawei Technology Co. Ltd China

Smartphones are everyday necessities with limited power supply. Charging a smartphone twice a day or more affects user experience. Flash friendly file system (F2FS) is a widely-used log-structured file system for smartphones. Free space fragmentation of F2FS consists of invalid blocks mainly causing performance degradation. F2FS reclaims invalid blocks by garbage collection (GC). We explore the energy consumption of GC and the effect of GC on reducing free space fragments. We observe the energy consumption of one background GC is large but its effect on reducing free space fragments is limited. These motivate us to improve the energy efficiency of GC. We reassess how much free space is a free space fragment based on data analysis, use the free space fragmentation factor to measure the degree of free space fragmentation quickly. We suggest the free space fragmentation aware GC scheme (FAGC) that optimizes the selection for victim segments and the migration for valid blocks. Experiments on real platform show that FAGC reduces GC count by 82.68% and 74.51% respectively than traditional F2FS and the latest GC optimization of F2FS, ATGC. FAGC reduces the energy consumption by 164.37 J and 100.64 J compared to traditional F2FS and ATGC respectively for a synthetic benchmark.

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Quaternary Neural Network Equalization for Three-Dimentional Magnetic Recording

Quaternary Neural Network Equalization for Three-Dimentional...

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Magnetic Conference - Short Papers (INTERMAG Short Papers), IEEE International

作者： Ke Luo Yufei Wu Yanzhe Liao Shaobing Wang Yugen Jian Jincai Chen Ping Lu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China School of Public Health Hubei University of Medicine Shiyan China

ISBN: (数字)9798350362213

ISBN: (纸本)9798350362220

One approach considered promising for advancing the development of magnetic data storage systems is three-dimensional magnetic recording. But when the recording density rises, media noise becomes a bigger problem for magnetic recording technology. Machine learning algorithms have been proven to outperform traditional numerical techniques in solving the magnetic recording signal quality issue. We compared the detection bit error rate (BER) performance of a per-layer shallow neural network equalizer and a quaternary neural network equalizer for two recording layers. For a two-layer recording density of 4 Tb/in 2 , the results show that the combined quaternary neural network equalization and detection scheme outperforms the per-layer shallow neural network equalization and one-dimensional Viterbi detection and outperforms the conventional two-dimensional generalized partial response equalizer combined with a quaternary Viterbi detector, especially for the bottom layer.

关键词： Machine learning algorithms Equalizers Viterbi algorithm Buffer storage Neural networks Bit error rate Noise

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ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering The School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

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On the Impact of Interlayer Misalignment for Dual-Layer data Detection in Three Dimensional Magnetic Recording

SSRN

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SSRN 2024年

作者： Luo, Ke Liao, Yanzhe Zhang, Kezheng Jian, Yugen Wang, Shaobing Chen, Jincai Lu, Ping Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China School of Public Health Hubei University of Medicine Shiyan442000 China

Three-dimensional magnetic recording (3DMR) is a crucial technology for significantly increasing the storage capacity of hard disk drives (HDDs). However, the presence of inter-symbol interference (ISI), inter-track interference (ITI), and inter-layer interference (ILI) poses significant challenges to the accurate detection of data stored on multiple layers. This study addresses the impact of interlayer misalignment on the bit error rate (BER) performance in 3DMR systems. We evaluate the performance based on a neural network estimator for reconstructing the top-layer read response signal using feedback from a Viterbi detector, enabling the separation of bottom-layer signals by subtraction from the mixed readback signal. We introduce a dual-layer partial response maximum likelihood (PRML) detector for simultaneous bit retrieval from both layers. We investigate methods of a per-layer binary classifier and a dual-layer four-class classifier based on neural networks. Our study demonstrates the BER performance of these detection schemes influenced by the interlayer misalignment, especially when the offset is 0, 10%, 50%, and 90% of the bit dimensions between the two layers. The results show that the neural network-based reconstruction and separation method achieves better bottom-layer BER performance under a slight interlayer misalignment. The BER performance benefits more from a mild downtrack offset compared with the crosstrack offset. Both the separation detection based on a neural network and the dual-layer PRML achieve the lowest top-layer BER and the worst bottom-layer BER when the interlayer misalignment is 50% of a bit size. © 2024, The Authors. All rights reserved.

关键词： Bit error rate

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All-electric mimicking synaptic plasticity based on the noncollinear antiferromagnetic device

arXiv

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arXiv 2024年

作者： Cao, Cuimei Duan, Wei Feng, Xiaoyu Xu, Yan Wang, Yihan Yang, Zhenzhong Zhan, Qingfeng You, Long School of Integrated Circuits Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China School of Physics and Electronic Science East China Normal University Shanghai200241 China Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518000 China

Neuromorphic computing, which seeks to replicate the brain’s ability to process information, has garnered significant attention due to its potential to achieve brain-like computing efficiency and human cognitive intelligence. Spin-orbit torque (SOT) devices can be used to simulate artificial synapses with non-volatile, high-speed processing and endurance characteristics. Nevertheless, achieving energy-efficient all-electric synaptic plasticity emulation using SOT devices remains a challenge. We chose the noncollinear antiferromagnetic Mn3Pt as spin source to fabricate the Mn3Pt-based SOT device, leveraging its unconventional spin current resulting from magnetic space breaking. By adjusting the amplitude, duration, and number of pulsed currents, the Mn3Pt-based SOT device achieves nonvolatile multi-state modulated by all-electric SOT switching, enabling emulate synaptic behaviors like excitatory postsynaptic potential (EPSP), inhibitory postsynaptic potential (IPSP), long-term depression (LTD) and the long-term potentiation (LTP) process. In addition, we show the successful training of an artificial neural network based on such SOT device in recognizing handwritten digits with a high recognition accuracy of 94.95 %, which is only slightly lower than that from simulations (98.04 %). These findings suggest that the Mn3Pt-based SOT device is a promising candidate for the implementation of memristor-based brain-inspired computing systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Multicarrier modulation

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TSU: A Two-stage update approach for persistent skiplist 13th

TSU: A Two-stage update approach for persistent skiplist

引用

13th Conference on Advanced Computer Architecture, ACA 2020

作者： Wang, Shucheng Cao, Qiang Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China Wuhan China

ISBN: (纸本)9789811581342

Skiplist, a widely used in-memory index structure, could incur crash inconsistency when running on emerging NVRAM (Non-Volatile Random Access Memory). Logging or strict serialization can ensure crash consistency at the cost of severe performance degradation. In this paper, we propose TSU, a Two-stage update approach to improve the performance of persistent skiplist while preserve crash consistency. TSU exploits space locality of skiplist and atomic write of NVRAM, thus effectively reducing expensive cache line flush (clflush) operations. To this end, we category all four crash inconsistent states into two types: recoverable and unrecoverable. TSU could guarantee the crash state is recoverable by constraining the memory access order for insertion and deletion. We further design a persistency algorithm to reduce clflush by preserving the memory persistent order of skiplist update. In addition, we develop a concurrent search for TSU. The evaluation result shows that TSU can reduce cache line flush with up to 47.6%, and decrease the average request latency by up to 36% for insertions compared to the strict serialization. © Springer Nature Singapore Pte Ltd 2020.

关键词： Cache memory

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