检索结果-内蒙古大学图书馆

2017 2nd International Conference on Computer engineering,Information Science and Internet Technology(CII 2017)

作者： YUCHEN LIU SHENGLI LIU YANG WANG State Key Laboratory of Mathematical Engineering and Advanced Computing

With the rapid development and extensive use of the Internet,network security is the key point of the current research of computer *** router is the key interconnection equipment in the Internet system,and it is necessary to improve the security of the router *** routing protocol is used in the packet switching *** communication is implemented by each switching node in the network to forward data *** present,most of the router intrusion detection systems are based on the machine learning *** paper proposes a Long Short Term Memory(LSTM) architecture applied to the Recurrent Neural Network(RNN),and uses the KDD Cup 1999 data set to train the IDS *** performance testing,the ability to efficiently and accurately classify network traffic into attacks and normal capabilities.

关键词： Router Intrusion Detection Long Short Term Memory KDD99 dataset Recurrent Neural Network

来源：评论

学校读者我要写书评

暂无评论

SDNGuardian: Secure your REST NBIs with API-grained permission checking system 12th

SDNGuardian: Secure your REST NBIs with API-grained permissi...

引用

12th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2016

作者： Ren, Kailei Wei, Qiang Wu, Zehui Wang, Qingxian State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China China National Digital Switching System Engineering and Technological Research Center Zhengzhou450001 China

ISBN: (纸本)9783319596075

REST NBI (REST Northbound API) is the mainstream NBI implementation method of current SDN controllers, but none of them has achieved authorization management, which leads to serious privilege abuse problems. In this paper, we propose SDNGuardian, an API-grained permission checking method based SDN REST NBI security defense system that achieves API level REST NBI authorization and effectively solves the problem of privilege abuse. Comparing with current authorization enhanced SDN controllers, our system is able to defend against attacks via REST NBIs within the same permission group. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2017.

关键词： Computers

来源：评论

学校读者我要写书评

暂无评论

Boosting Electrocatalytic Activity of 3d-Block Metal (Hydro)oxides by Ligand-Induced Conversion

引用

Angewandte Chemie 2021年第19期133卷

作者： Dr. Wenxian Liu Dong Zheng Dr. Tianqi Deng Dr. Qiaoli Chen Chongzhi Zhu Chengjie Pei Dr. Hai Li Dr. Fangfang Wu Dr. Wenhui Shi Dr. Shuo-Wang Yang Prof. Yihan Zhu Prof. Xiehong Cao College of Materials Science and Engineering State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology Center for Electron Microscopy Center for Membrane Separation and Water Science & Technology College of Chemical Engineering Zhejiang University of Technology 18 Chaowang Road Hangzhou Zhejiang 310014 P. R. China Institute of High Performance Computing Agency for Science Technology and Research 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Singapore Key Laboratory of Flexible Electronics and Institute of Advanced Materials Jiangsu National Synergetic Innovation Center for Advanced Materials Nanjing Tech University 30 South Puzhu Road Nanjing Jiangsu 211816 P. R. China

The 3d-transition-metal (hydro)oxides belong to a group of highly efficient, scalable and inexpensive electrocatalysts for widespread energy-related applications that feature easily tailorable crystal and electronic structures. We propose a general strategy to further boost their electrocatalytic activities by introducing organic ligands into the framework, considering that most 3d-metal (hydro)oxides usually exhibit quite strong binding with reaction intermediates and thus compromised activity due to the scaling relations. Involving weakly bonded ligands downshifts the d-band center, which narrows the band gap, and optimizes the adsorption of these intermediates. For example, the activity of the oxygen evolution reaction (OER) can be greatly promoted by ≈5.7 times over a NiCo layered double hydroxide (LDH) after a terephthalic acid (TPA)-induced conversion process, arising from the reduced energy barrier of the deprotonation of OH* to O*. Impressively, the proposed ligand-induced conversion strategy is applicable to a series of 3d-block metal (hydro)oxides, including NiFe 2 O 4 , NiCo 2 O 4 , and NiZn LDH, providing a general structural upgrading scheme for existing high-performance electrocatalytic systems.

关键词： electronic-structure engineering flexible Zn–air batteries layered double hydroxides metal–organic frameworks oxygen evolution reaction

来源：评论

学校读者我要写书评

暂无评论

Study of Ultra-High-Energy Gamma-Ray Source 1LHAASO J0056+6346u and Its Possible Origins

arXiv

引用

arXiv 2025年

作者： Cao, Zhen Aharonian, F. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Bian, W. Bukevich, A.V. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, H.X. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Chu, M.C. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, J.H. Fang, K. Feng, C.F. Feng, H. Feng, L. Feng, S.H. Feng, X.T. Feng, Y. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Ge, T.T. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. Hannuksela, O.A. Hasan, M. He, H.H. He, H.N. He, J.Y. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Y.Y. Ji, X.L. Jia, H.Y. Jia, K. Jiang, H.B. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Karpikov, I. Khangulyan, D. Kuleshov, D. Kurinov, K. Li, B.B. Li, C.M. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, Jian Li, Jie Li, K. Li, S.D. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, D.B. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Luo, Q. Luo, Y. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ng, K.C.Y. Ou, L.J. Pattarakijwanich, P. Pei, Z.Y. Qi, J.C. Qi, M.Y. Qiao, B.Q. Qin, J.J. Raza, A. Ruffolo, D. Sáiz, A. Saeed, M. Semikoz, D. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Institute for Nuclear Research Russian Academy of Sciences Moscow117312 Russia Hebei Normal University Hebei Shijiazhuang050024 China University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Particle Detection and Electronics China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Research Center for Astronomical Computing Zhejiang Laboratory Zhejiang Hangzhou311121 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Key Laboratory of Radio Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Zhuhai Guan

We report a dedicated study of the newly discovered extended UHE γ-ray source 1LHAASO J0056+6346u. Analyzing 979 days of LHAASO-WCDA data and 1389 days of LHAASO-KM2A data, we observed a significant excess of γ-ray events with both WCDA and KM2A. Assuming a point power-law source with a fixed spectral index, the significance maps reveal excesses of 12.65 σ, 22.18 σ, and 10.24 σ in the energy ranges of 1–25 TeV, 25–100 TeV, and > 100 TeV, respectively. We use a 3D likelihood algorithm to derive the morphological and spectral parameters, and the source is detected with significances of 13.72 σ by WCDA and 25.27 σ by KM2A. The best-fit positions derived from WCDA and KM2A data are (R.A. = 13.96◦ ± 0.09◦, Decl. = 63.92◦ ± 0.05◦) and (R.A. = 14.00◦ ± 0.05◦, Decl. = 63.79◦ ± 0.02◦), respectively. The angular size (r39) of 1LHAASO J0056+6346u is 0.34◦ ± 0.04◦ at 1-25 TeV and 0.24◦ ± 0.02◦ at > 25 TeV. The differential flux of this UHE γ-ray source can be described by an exponential cutoff power-law function: (2.67±0.25)×10−15 (E / 20 TeV)(−1.97±0.10) e−E / (55.1±7.2) TeV TeV−1cm−2s−1. To explore potential sources of γ-ray emission, we investigated the gas distribution around 1LHAASO J0056+6346u. 1LHAASO J0056+6346u is likely to be a TeV PWN powered by an unknown pulsar, which would naturally explain both its spatial and spectral properties. Another explanation is that this UHE γ-ray source might be associated with gas content illuminated by a nearby CR accelerator, possibly the SNR candidate G124.0+1.4. Copyright © 2025, The Authors. All rights reserved.

关键词： Cosmic rays

来源：评论

学校读者我要写书评

暂无评论

An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

引用

Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Identification of mobile device threats based on PCA-fused neural network

Boletin Tecnico/Technical Bulletin

引用

Boletin Tecnico/Technical Bulletin 2017年第3期55卷 278-285页

作者： Chu, Sijun Wu, Hao Lu, Yulin State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China

To monitor and identify whether a mobile device is under threat or at risk effectively, in this paper, we summarize feature data from threat information of common mobile devices and set up an identification model of mobile device threats based on PCA-fused BP neural network. When extracting features from raw sample data, we consider primary vector information of all training samples. While more than 95% of the information in raw data is reserved, interference information in the data is eliminated and the robustness of the BP network model built subsequently is optimized. Finally, the trained model is tested and compared with other model algorithms. Results show that the overall identification accuracy of the proposed model algorithm reaches 91%. The identification accuracy of test samples reaches 90.4%. Compared with previous identification models, it has higher identification accuracy.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Excited-state Half-Lives in Cd130 and the Isospin Dependence of Effective Charges

引用

Physical Review Letters 2024年第22期132卷 222501-222501页

作者： A. Jungclaus M. Górska M. Mikołajczuk J. Acosta J. Taprogge S. Nishimura P. Doornenbal G. Lorusso G. S. Simpson P.-A. Söderström T. Sumikama Z. Xu P. Kumar G. Martínez-Pinedo F. Nowacki P. Van Isacker H. Baba F. Browne N. Fukuda R. Gernhäuser G. Gey N. Inabe T. Isobe H. S. Jung D. Kameda G. D. Kim Y.-K. Kim I. Kojouharov T. Kubo N. Kurz Y. K. Kwon Z. Li H. Sakurai H. Schaffner Y. Shimizu K. Steiger H. Suzuki H. Takeda Zs. Vajta H. Watanabe J. Wu A. Yagi K. Yoshinaga G. Benzoni S. Bönig K. Y. Chae J.-M. Daugas F. Drouet A. Gadea S. Ilieva F. G. Kondev T. Kröll G. J. Lane A. Montaner-Pizá K. Moschner F. Naqvi M. Niikura H. Nishibata A. Odahara R. Orlandi Z. Patel Zs. Podolyák A. Wendt Instituto de Estructura de la Materia CSIC E-28006 Madrid Spain GSI Helmholtzzentrum für Schwerionenforschung D-64291 Darmstadt Germany Faculty of Physics University of Warsaw Warsaw PL-02093 Poland Departamento de Física Teórica Universidad Autónoma de Madrid E-28049 Madrid Spain RIKEN Nishina Center RIKEN 2-1 Hirosawa Wako-shi Saitama 351-0198 Japan LPSC Université Joseph Fourier Grenoble 1 CNRS/IN2P3 Institut National Polytechnique de Grenoble F-38026 Grenoble Cedex France Department of Physics Tohoku University Aoba Sendai Miyagi 980-8578 Japan Department of Physics University of Tokyo Hongo 7-3-1 Bunkyo-ku 113-0033 Tokyo Japan Institut für Kernphysik (Theoriezentrum) Fachbereich Physik Technische Universität Darmstadt D-64289 Darmstadt Germany Université de Strasbourg CNRS IPHC UMR Strasbourg 7178 F-67000 France Grand Accélérateur National d’Ions Lourds CEA/DRF–CNRS/IN2P3 F-14076 Caen France School of Computing Engineering and Mathematics University of Brighton Brighton BN2 4GJ United Kingdom Physik Department E12 Technische Universität München D-85748 Garching Germany Institut Laue-Langevin B.P. 156 F-38042 Grenoble Cedex 9 France Department of Physics Chung-Ang University Seoul 156-756 Republic of Korea Rare Isotope Science Project Institute for Basic Science Daejeon 305-811 Republic of Korea Department of Nuclear Engineering Hanyang University Seoul 133-791 Republic of Korea School of Physics and State key Laboratory of Nuclear Physics and Technology Peking University Beijing 100871 China MTA Atomki P.O. Box 51 Debrecen H-4001 Hungary Department of Physics Osaka University Machikaneyama-machi 1-1 Osaka 560-0043 Toyonaka Japan Department of Physics Faculty of Science and Technology Tokyo University of Science 2641 Yamazaki Noda Chiba Japan INFN Sezione di Milano via Celoria 16 I-20133 Milano Italy Institut für Kernphysik Technische Universität Darmstadt D-64289 Darmstadt Germany Department of Physics Sungkyunkwan Univer

The known Iπ=81+, Ex=2129−keV isomer in the semimagic nucleus Cd13082 was populated in the projectile fission of a U238 beam at the Radioactive Isotope Beam Factory at RIKEN. The high counting statistics of the accumulated data allowed us to determine the excitation energy, Ex=2001.2(7) keV, and half-life, T1/2=57(3) ns, of the Iπ=61+ state based on γγ coincidence information. Furthermore, the half-life of the 81+ state, T1/2=224(4) ns, was remeasured with high precision. The new experimental information, combined with available data for Sn134 and large-scale shell model calculations, allowed us to extract proton and neutron effective charges for Sn132, a doubly magic nucleus far-off stability. A comparison to analogous information for Sn100 provides first reliable information regarding the isospin dependence of the isoscalar and isovector effective charges in heavy nuclei.

关键词： Electromagnetic transitions Nuclear structure & decays Shell model

来源：评论

学校读者我要写书评

暂无评论

Word2Cluster: A New Multi-Label Text Clustering Algorithm with an Adaptive Clusters Number

Word2Cluster: A New Multi-Label Text Clustering Algorithm wi...

引用

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Kaili Mao Jianwei Niu Xuefeng Liu Shui Yu Longbo Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University School of Computer Science and Cyber Engineering Guangzhou University Guangdong China China aerospace science and industry corporation China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Text clustering has been widely used in many Natural Language Processing (NLP) applications such as text summarization and news recommendation. However, most of the current algorithms need to predefine a clustering number, which is difficult to obtain. Moreover, the mutli-label clustering is useful in multiple clustering tasks in many applications, but related works are rarely available. Although several studies have attempted to solve above two problems, there is a need for methods that can solve the two issues simultaneously. Therefore, we propose a new text clustering algorithm called Word2Cluster. Word2Cluster can automatically generate an adaptive number of clusters and support multi-label clustering. To test the performance of Wrod2Cluster, we build a Chinese text dataset, Hotline, according to real world applications. To evaluate the clustering results better, we propose an improved evaluation method based on basic accuracy, precision and recall for multi-label text clustering. Experimental results on a Chinese text dataset (Hotline) and a public English text dataset (Reuters) demonstrate that our algorithm can achieve better F1-measure and runs faster than the state-of- the-art baselines.

关键词： Clustering algorithms Biological system modeling Merging Task analysis Computer science Technological innovation Natural language processing

来源：评论

学校读者我要写书评

暂无评论

A WeChat user geolocating algorithm based on the relation between reported and actual distance 1

引用

9th International Symposium on Cyberspace Safety and Security, CSS 2017

作者： Shi, Wenqi Luo, Xiangyang Zhao, Fan Peng, Ziru Gan, Yong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Science and Technology Institute Zhengzhou China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China

ISBN: (数字)9783319694719

ISBN: (纸本)9783319694702

The accuracy of existing geolocation methods for WeChat users depends on the stable correspondence between reported distance and actual distance. In view of the difficulty to pinpoint users’ location in real-world due to WeChat location protection strategy, a WeChat User geolocating Algorithm based on reported and actual distance relation analysis is proposed. Firstly, statistical characteristics of the relation between reported distance and actual distance are obtained based on collected data. Secondly, optimization parameters are selected based on these characteristics to determine the space where the target user is located. Finally, stepwise strategies are taken to improve the accuracy rate of space partition. Experimental results show that, on the premise that target users can be discovered, the proposed algorithm could achieve higher accuracy compared with the classical space partition based algorithm and the heuristic number theory based algorithm. The highest geolocating accuracy is within 10 m and 56% of geolocation results are within 60 m. © 2017, Springer International Publishing AG.

关键词： Number theory

来源：评论

学校读者我要写书评

暂无评论

ShenTu: Processing Multi-Trillion Edge Graphs on Millions of Cores in Seconds 18

ShenTu: Processing Multi-Trillion Edge Graphs on Millions of...

引用

ACM/IEEE International Conference for High Performance computing, Networking, Storage, and Analysis

作者： Heng Lin Xiaowei Zhu Bowen Yu Xiongchao Tang Wei Xue Wenguang Chen Lufei Zhang Torsten Hoefler Xiaosong Ma Xin Liu Weimin Zheng Jingfang Xu Tsinghua University and Fma Technology Tsinghua University and Qatar Computing Research Institute Tsinghua University State Key Laboratory of Mathematical Engineering and Advanced Computing ETH Zurich Qatar Computing Research Institute National Research Centre of Parallel Computer Engineering and Technology Beijing Sogou Technology Development Co. Ltd

ISBN: (纸本)9781538683859

Graphs are an important abstraction used in many scientific fields. With the magnitude of graph-structured data constantly increasing, effective data analytics requires efficient and scalable graph processing systems. Although HPC systems have long been used for scientific computing, people have only recently started to assess their potential for graph processing, a workload with inherent load imbalance, lack of locality, and access irregularity. We propose ShenTu ⁸ the first general-purpose graph processing framework that can efficiently utilize an entire Petascale system to process multi-trillion edge graphs in seconds. ShenTu embodies four key innovations: hardware specialization, supernode routing, on-chip sorting, and degree-aware messaging, which together enable its unprecedented performance and scalability. It can traverse a record-size 70-trillion-edge graph in seconds. Furthermore, ShenTu enables the processing of a spam detection problem on a 12-trillion edge Internet graph, making it possible to identify trustworthy and spam webpages directly at the fine-grained page level.

关键词： Random access memory Servers Data analysis Supercomputers Task analysis Computer architecture Optimization supercomputer task analysis random access machine Computer Architecture Servers Data analysis core Core samples Scalability Edge of a figure or solid Unsolicited electronic mail

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：