检索结果-内蒙古大学图书馆

24th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2019

作者： Yan, Zhaofeng Lin, Yuzhe Peng, Lu Zhang, Weihua Software School Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Institute of Intelligent Electronics and Systems Shanghai China Division of Electrical and Computer Engineering Louisiana State University United States

ISBN: (纸本)9781450362252

B+tree is one of the most important data structures and has been widely used in different fields. With the increase of concurrent queries and data-scale in storage, designing an eficient B+tree structure has become critical. Due to abundant computation resources, GPUs provide potential opportunities to achieve high query throughput for B+tree. However, prior methods cannot achieve satisfactory performance results due to low resource utilization and poor memory performance. In this paper, we first identify the gaps between B+tree and GPUs. Concurrent B+tree queries involve many global memory accesses and different divergences, which mismatch with GPU features. Based on this observation, we propose Harmonia, a novel B+tree structure to bridge the gap. In Harmonia, a B+tree structure is divided into a key region and a child region. The key region stores the nodes with its keys in a breadth-first order. The child region is organized as a prefix-sum array, which only stores each node's first child index in the key region. Since the prefix-sum child region is small and the children's index can be retrieved through index computations, most of it can be stored in on-chip caches, which can achieve good cache locality. To make it more eficient, Harmonia also includes two optimizations: partially-sorted aggregation and narrowed thread-group traversal, which can mitigate memory and warp divergence and improve resource utilization. Evaluations on a TITAN V GPU show that Harmonia can achieve up to 3.6 billion queries per second, which is about 3.4X faster than that of HB+Tree [39], a recent state-of-the-art GPU solution. © 2019 Association for Computing Machinery.

关键词： Trees (mathematics)

来源：评论

学校读者我要写书评

暂无评论

GWSkyNet-Multi II: an updated deep learning model for rapid classification of gravitational-wave events

arXiv

引用

arXiv 2025年

作者： Raza, Nayyer Chan, Man Leong Haggard, Daryl Mahabal, Ashish McIver, Jess Durand, Audrey Larouche, Alexandre Moazen, Hadi Department of Physics McGill University 3600 rue University MontréalQCH3A2T8 Canada Trottier Space Institute at McGill 3550 rue University MontréalQCH3A2A7 Canada Department of Physics and Astronomy University of British Columbia VancouverBCV6T1Z1 Canada Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Department of Computer Science and Software Engineering Université Laval QuébecQCG1V0A6 Canada Canada CIFAR AI Chair Mila MontréalQCH2S3H1 Canada

Multi-messenger observations of gravitational waves and electromagnetic emission from compact object mergers offer unique insights into the structure of neutron stars, the formation of heavy elements, and the expansion rate of the Universe. With the LIGO-Virgo-KAGRA (LVK) gravitational-wave detectors currently in their fourth observing run (O4), it is an exciting time for detecting these mergers. However, assessing whether to follow up a candidate gravitational-wave event given limited telescope time and resources is challenging;the candidate can be a false alert due to detector glitches, or may not have any detectable electromagnetic counterpart even if it is real. GWSkyNet-Multi is a deep learning model developed to facilitate follow-up decisions by providing real-time classification of candidate events, using localization information released in LVK rapid public alerts. Here we introduce GWSkyNet-Multi II, an updated model targeted towards providing more robust and informative predictions during O4 and beyond. Specifically, the model now provides normalized probability scores and associated uncertainties for each of the four corresponding source categories released by the LVK: glitch, binary black hole, neutron star-black hole, and binary neutron star. Informed by explainability studies of the original model, the updated model architecture is also significantly simplified, including replacing input images with intuitive summary values, making it more interpretable. For significant O4 event alerts issued between May 2023 and December 2024, GWSkyNet-Multi II produces a prediction that is consistent with the updated LVK classification for 93% of events. The updated model can be used by the community to help make time-critical follow-up decisions. © 2025, CC BY.

关键词： Neutrons

来源：评论

学校读者我要写书评

暂无评论

A comparison of deep learning-based techniques for solving partial differential equations

引用

AIP Conference Proceedings 2024年第1期2905卷

作者： Rabiu Bashir Yunus Nooraini Zainuddin Afza Shafie Muhammad Izzatullah Samsul Ariffin Abdul Karim Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS Bandar Seri Iskandar 32610 Seri Iskandar Perak Darul Ridzuan Malaysia King Abdullah University of Science and Technology (KAUST) Division of Physical Sciences and Engineering Bldg. 1 Desk #0202-WS023 Thuwal 23955-6900 Kingdom of Saudi Arabia Software Engineering Programme Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia Data Technologies and Applications (DaTA) Research Group Universiti Malaysia Sabah Jalan UMS 88400 Kota Kinabalu Sabah Malaysia

Obtaining the solutions of high-dimensional partial differential equations (PDEs) seems to be difficult by utilizing the classical numerical methods. Recently, deep neural networks (DNNs) techniques have received special attentions in solving high–dimensional problems in PDEs. In this study, our quest is to investigate some newly introduced data-driven deep learning-based approaches and compare their performance in terms of their efficiency and faster training towards high-dimensional PDEs. However, the comparison is carried out based on different activation functions, number of layers and gradient based optimizers. We consider some benchmark problems in our numerical experiments which includes Burgers equation, Diffusion-reaction equation and Allen-Cahn Equations.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Simulation-Based Approach to Multi-Item Multi-Echelon Spare Parts Inventory System optimization with Variable Warehouse Roles

A Simulation-Based Approach to Multi-Item Multi-Echelon Spar...

引用

Congress on Evolutionary Computation

作者： Daniel B. P. Coelho Wlamir O. L. Vianna Leonardo R. Rodrigues Performance Software Department Embraer S.A. Belo Horizonte Brazil Data Science Department Embraer S.A. São José dos Campos Brazil Electronics Engineering Division Aeronautics Institute of Technology São José dos Campos Brazil

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169309

An optimized supply chain is essential for the success of large-scale industries. In order to meet the high availability level requirements, an efficient inventory system is crucial to reduce downtime when a failure event occurs. In a multi-echelon spare parts inventory system, each warehouse within the system may operate as a hub or a spoke. A hub is a warehouse that fulfills the demands of other warehouses, while a spoke is a warehouse that fulfills the demands of final customers. When the inventory system deals with multiple items, a fixed role (hub or spoke) is commonly assigned to each warehouse. However, this limitation may lead to sub-optimal solutions. If the optimization model allows each warehouse to have a different role for different items, a new degree of freedom is included and more efficient solutions can be found. In this paper, we propose a simulation-based optimization model to define the configuration of a multiechelon spare parts inventory system of multiple items. The goal is to minimize total inventory costs, subject to a fill rate constraint. We relax the assumption that warehouses have a fixed role for all the items. Two algorithms are used to evaluate the model: the Teaching-Learning Based optimization (TLBO) and the Simulated Annealing (SA) algorithms. A case study based on a spare parts inventory system of an aircraft manufacturer is used to compare the performance of the proposed model with the performance obtained considered fixed warehouse roles. The results showed that the proposed model provided a reduction of 6.8% in total cost, without violating the fill rate constraint.

关键词： Simulated annealing Finance Supply chains Linear programming Cooling Atmospheric modeling

来源：评论

学校读者我要写书评

暂无评论

A call to action to address escalating global threats to academic research

引用

The Innovation 2025年第4期6卷 17-19页

作者： Gaelle Piret Fun Man Fung Josie Fullerton Giuseppe Fico Dmitriy Ponkratov Wenxin Chen Daniela Latorre Kirsty Y.Wan Nima Aghaeepour Jules Welgryn Adeel Razi Patricia Silveyra Ahmet Altun Renata Z.Jurkowska Alice C.Hughes Joy Wolfram INSERM U1205 UGA38400 Saint Martin d’HèresFrance School of Chemistry University College DublinD04 C1P1 BelfieldIreland UCD Geary Institute for Public Policy University College DublinD04 N9Y1 DublinIreland School of Cardiovascular&Metabolic Health University of GlasgowG128TA GlasgowUK European Alliance for Medical and Biological Engineering and Science 3001 LeuvenBelgium Life Supporting Technologies Universidad Politécnica de Madrid28040 MadridSpain Siemens Digital Industries Software B908BG SolihullUK Department of Philosophy of Science and Technology University of Science and Technology of ChinaHefei 230026China Institute of Microbiology ETH Zurich8093 ZurichSwitzerland Living Systems Institute&Department of Mathematics and Statistics University of ExeterEX44QD ExeterUK Department of Anesthesiology Painand Perioperative MedicineDepartment of PediatricsDepartment of Biomedical Data ScienceStanford University School of MedicineStanfordCA 94305USA UniversitéParis Nanterre 92000 NanterreFrance Turner Institute for Brain and Mental Health School of Psychological Sciencesand Monash Biomedical ImagingMonash UniversityClaytonVIC 3800Australia Wellcome Centre for Human Neuroimaging University College LondonWC1N 3AR LondonUK CIFAR Azrieli Global Scholars Program TorontoON M5G 1M1Canada Department of Environmental and Occupational Health Indiana University School of Public Health BloomingtonBloomingtonIN 47408USA Department of Pharmacology Sivas Cumhuriyet UniversitySivas 58140Turkey Division of Biomedicine School of BiosciencesCardiff UniversityCF103AX CardiffUK School of Biological Sciences University of Hong KongHong Kong SAR 999077China Australian Institute for Bioengineering and Nanotechnology The University of QueenslandBrisbaneQLD 4072Australia School of Chemical Engineering The University of QueenslandBrisbaneQLD 4072Australia

This article is a call to action to address escalating threats to scientific progress that affect academic researchers across the *** threats include public mistrust of science,challenges in translating academic research to end-user applications,a disconnect between academics and policymakers,emerging barriers to international collaboration,and a reliance on conventional metrics to evaluate academic *** article presents various calls to action informed by exemplary approaches across the globe that serve as frameworks to drive beneficial transformation for researchers,academic institutions,and society.

关键词： academic research calls action global threats public mistrust sciencechallenges translating academic research disconnect academics policymakers call action translation research

来源：评论

学校读者我要写书评

暂无评论

Adaptively Lossy Image Compression for Onboard Processing

Adaptively Lossy Image Compression for Onboard Processing

引用

IEEE Aerospace Conference

作者： Justin Goodwill David Wilson Sebastian Sabogal Alan D. George Christopher Wilson NSF Center for Space High-Performance and Resilient Computing (SHREC) University of Pittsburgh 4420 Bayard St Suite 560 Pittsburgh PA Software Engineering Division NASA Goddard Space Flight Center Science Data Processing Branch Code 587 8800 Greenbelt Rd Greenbelt MD

ISBN: (数字)9781728127347

ISBN: (纸本)9781728127354

More efficient image-compression codecs are an emerging requirement for spacecraft because increasingly complex, onboard image sensors can rapidly saturate downlink bandwidth of communication transceivers. While these codecs reduce transmitted data volume, many are compute-intensive and require rapid processing to sustain sensor data rates. Emerging next-generation small satellite (SmallSat) computers provide compelling computational capability to enable more onboard processing and compression than previously considered. For this research, we apply two compression algorithms for deployment on modern flight hardware: (1) end-to-end, neural-network-based, image compression (CNN-JPEG);and (2) adaptive image compression through feature-point detection (FPD-JPEG). These algorithms rely on intelligent data-processing pipelines that adapt to sensor data to compress it more effectively, ensuring efficient use of limited downlink bandwidths. The first algorithm, CNN-JPEG, employs a hybrid approach adapted from literature combining convolutional neural networks (CNNs) and JPEG;however, we modify and tune the training scheme for satellite imagery to account for observed training instabilities. This hybrid CNN-JPEG approach shows 23.5% better average peak signal-to-noise ratio (PSNR) and 33.5% better average structural similarity index (SSIM) versus standard JPEG on a dataset collected on the Space Test Program - Houston 5 (STP-H5-CSP) mission onboard the International Space Station (ISS). For our second algorithm, we developed a novel adaptive image-compression pipeline based upon JPEG that leverages the Oriented FAST and Rotated BRIEF (ORB) feature-point detection algorithm to adaptively tune the compression ratio to allow for a tradeoff between PSNR/SSIM and combined file size over a batch of STP-H5-CSP images. We achieve a less than 1% drop in average PSNR and SSIM while reducing the combined file size by 29.6% compared to JPEG using a static quality factor (QF) of 90.

关键词：

来源：评论

学校读者我要写书评

暂无评论

On the Conformation of Dimeric Acceptors and Their Polymer Solar Cells with Efficiency over 18 %

引用

Angewandte Chemie 2023年第45期135卷

作者： Jingnan Wu Zhaoheng Ling Leandro R. Franco Sang Young Jeong Zewdneh Genene Josué Mena Si Chen Cailing Chen Prof. C. Moyses Araujo Prof. Cleber F. N. Marchiori Joost Kimpel Xiaoming Chang Furkan H. Isikgor Qiaonan Chen Hendrik Faber Prof. Yu Han Frédéric Laquai Prof. Maojie Zhang Prof. Han Young Woo Prof. Donghong Yu Prof. Thomas D. Anthopoulos Prof. Ergang Wang Department of Chemistry and Chemical Engineering Chalmers University of Technology 41296 Göteborg Sweden Department of Chemistry and Bioscience Aalborg University 9220 Aalborg Denmark Contribution: Conceptualization (lead) Data curation (lead) Formal analysis (lead) Writing - original draft (lead) Writing - review & editing (lead) King Abdullah University of Science and Technology (KAUST) KAUST Solar Center Thuwal 23955 Saudi Arabia Contribution: Data curation (lead) Formal analysis (lead) Writing - original draft (equal) Writing - review & editing (equal) Department of Engineering and Physics Karlstad University 65188 Karlstad Sweden Contribution: Data curation (equal) Writing - review & editing (equal) Department of Chemistry Korea University Seoul 02841 (Republic of Korea Contribution: Data curation (equal) Formal analysis (equal) Advanced Membranes and Porous Materials Center Physical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Contribution: Data curation (equal) Methodology (equal) Materials Theory Division Department of Physics and Astronomy Uppsala University 75120 Uppsala Sweden Contribution: Data curation (equal) Funding acquisition (equal) Software (equal) Writing - review & editing (equal) Contribution: Data curation (equal) Methodology (equal) Writing - review & editing (equal) Contribution: Data curation (equal) Writing - review & editing (supporting) Contribution: Data curation (equal) Methodology (supporting) Contribution: Data curation (equal) Contribution: Data curation (supporting) Funding acquisition (supporting) National Engineering Research Center for Colloidal Materials School of Chemistry & Chemical Engineering Shandong University Jinan Shandong 250100 China Contribution: Data curation (equal) Funding acquisition (supporting) Contribution: Data curation (equal) Funding acquisition (supporting) Writing - review & editing (supporting) Contribution: Data curation (equal) Fu

The determination of molecular conformations of oligomeric acceptors (OAs) and their impact on molecular packing are crucial for understanding the photovoltaic performance of their resulting polymer solar cells (PSCs) but have not been well studied yet. Herein, we synthesized two dimeric acceptor materials, DIBP3F-Se and DIBP3F-S, which bridged two segments of Y6-derivatives by selenophene and thiophene, respectively. Theoretical simulation and experimental 1D and 2D NMR spectroscopic studies prove that both dimers exhibit O -shaped conformations other than S - or U -shaped counter-ones. Notably, this O -shaped conformation is likely governed by a distinctive “conformational lock” mechanism, arising from the intensified intramolecular π–π interactions among their two terminal groups within the dimers. PSCs based on DIBP3F-Se deliver a maximum efficiency of 18.09 %, outperforming DIBP3F-S-based cells (16.11 %) and ranking among the highest efficiencies for OA-based PSCs. This work demonstrates a facile method to obtain OA conformations and highlights the potential of dimeric acceptors for high-performance PSCs.

关键词： Acceptor Molecular Conformation Oligomer Selenophene Solar Cells

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence for modelling infectious disease epidemics

引用

Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Electrocardiograms Insights: A Comparative Study of Network Architectures Predicting Sex and Left Ventricular Dysfunction

引用

Heliyon 2025年

作者： Michal Cohen-Shelly David Hochstein Noam Barda Amit Bleiweiss Estelle Aflalo Nitzan Bar Eyal Zimlichman Eyal Klang Nisim Rahman Talia Sela Robert Klempfner Elad Maor Roy Beinart Amit Segev Ehud Raanani Avi Sabbag The Olga and Lev Leviev Heart Center Sheba Medical Center Sheba ARC Sagol Big data & AI Hub Sheba Medical Center Tel Hashomer Israel Sheba ARC and Hospital Management Sheba Medical Center Tel Hashomer Israel Tel Aviv University Tel Aviv Israel Software and Information Systems Engineering Ben-Gurion University of the Negev Be'er Sheva Israel Epidemiology Biostatistics and Community Health Sciences Ben-Gurion University of the Negev Be’er Sheva Israel Datacenter & AI group Intel Corporation The Division of Data Driven and Digital Medicine (D3M) Icahn School of Medicine at Mount Sinai New York NY USA

Objective To compare the effectiveness of Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) in developing AI-driven electrocardiogram (ECG) algorithms for predicting sex and left ventricular dysfunction (LVD). Patients and Methods In this retrospective study, we utilized 12-lead ECG data collected from January 1, 2005, through December 31, 2022, from a tertiary healthcare center. The data comprised 150,691 patients for the sex model and 29,422 patients for the LVD model (defined as Left Ventricular Ejection Fraction [LVEF] below 35%). We developed binary classification models using complete ECGs, single leads, and truncated recordings of 1 to 10 seconds, employing CNNs (PyTorch) and ViTs (HuggingFace) for comparison. Results For the sex prediction model, AUROCs were 0.915 (95% CI: 0.912-0.918) for CNNs and 0.898 (95% CI: 0.894-0.901) for ViTs, with the difference statistically significant (P-Value < 0.001). LVD prediction yielded comparable AUROCs of 0.895 (95% CI: 0.884-0.906) for CNNs and 0.866 (95% CI: 0.853-0.879) for ViTs (P = 0.023). Furthermore, we found that 99% for the maximal predictive power of the models was achieved within a 2 second time frame of a 12 lead ECG. Similarly, a single lead based model achieved a relatively high AUC for the prediction of LVD. Conclusion AI-ECG models developed with ViT architecture did not demonstrate superiority over CNN-based models in predicting sex and LVD. The study underscores the potential of lean models allowing for rapid prediction within 2-second tracings or using a single lead, demonstrates the potential for streamlined AI-ECG applications in clinical practice.

关键词： Deep Learning Cardiology ECG

来源：评论

学校读者我要写书评

暂无评论

Endurable SSD-Based Read Cache for Improving the Performance of Selective Restore from Deduplication Systems

引用

Journal of Computer Science & Technology 2018年第1期33卷 58-78页

作者： Jian Liu Yun-Peng Chai Xiao Qin Yao-Hong Liu Division of Computer Science and Engineering Louisiana State University Baton Rouge LA 70803 U.S.A. Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education of China Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Shelby Center for Engineering Technology Department of Computer Science and Software Engineering Samuel Ginn College of Engineering Auburn University Auburn AL 368~9-53~7 U.S.A.

Deduplication has been commonly used in both enterprise storage systems and cloud storage. To overcome the performance challenge for the selective restore operations of deduplication systems, solid-state-drive-based （i.e., SSD-based） re^d cache cm, be deployed for speeding up by caching popular restore contents dynamically. Unfortunately, frequent data updates induced by classical cache schemes （e.g., LRU and LFU） significantly shorten SSDs＇ lifetime while slowing down I/O processes in SSDs. To address this problem, we propose a new solution -- LOP-Cache to greatly improve tile write durability of SSDs as well as I/O performance by enlarging the proportion of long-term popular （LOP） data among data written into SSD-based cache. LOP-Cache keeps LOP data in the SSD cache for a long time period to decrease the number of cache replacements. Furthermore, it prevents unpopular or unnecessary data in deduplication containers from being written into the SSD cache. We implemented LOP-Cache in a prototype deduplication system to evaluate its pertbrmance. Our experimental results indicate that LOP-Cache shortens the latency of selective restore by an average of 37.3% at the cost of a small SSD-based cache with only 5.56% capacity of the deduplicated data. Importantly, LOP-Cache improves SSDs＇ lifetime by a factor of 9.77. The evidence shows that LOP-Cache offers a cost-efficient SSD-based read cache solution to boost performance of selective restore for deduplication systems.

关键词： data deduplication solid state drive （SSD） flash cache endurance

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：