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Performance Improvement of Hadoop ext4-based Disk I/O

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Performance Improvement of Hadoop ext4-based Disk I/O

International Symposium on Computing and Networking (CANDAR)

作者： Makoto Nakagami Jose A. B. Fortes Saneyasu Yamaguchi Electrical Engineering and Electronics Kogakuin University Graduate School Tokyo Japan Advanced Computer and Information Systems (ACIS) Lab University of Florida Gainesville USA Kogakuin University Tokyo Japan

ISBN: (数字)9781728182216

ISBN: (纸本)9781728182223

Hadoop is one of the most popular big-data analytics platforms, often relying on hard disk drives for storage of big-data amounts that exceed the capacity of solid-state drives. Unlike other data-intensive applications, such as database management systems, big-data processing jobs frequently require extensive sequential I/O requests. Previously proposed methods for improving sequential I/O performance modified the block usage bitmap of the Ext2/3 filesystem in order to actively use the faster disk zones, which are the outer zones in each hard disk drive. However, these methods do not support Ext4, which is the current version of Ext filesystems. In this paper, we discuss a method for improving the sequential I/O performance of the Ext4 filesystem. First, we evaluate the sequential file access throughputs on Ext3, Ext4, and XFS filesystems. We point out that Ext4 does not actively utilize the area freed by deleting existing files, causing declines in file access performance. Second, we propose a method for improving the Ext4 sequential file access performance. The improved Ext4 actively utilizes the faster zones of storage devices by controlling file placement location. Third, we evaluate the proposed filesystem and show that it outperforms existing filesystems. In the case of TeraSort, Hadoop with the proposed Ext4 filesystem performs better than when using the original Ext4 filesystem by as much as 30.1%.

关键词： Performance evaluation Process control Drives Aerospace electronics Hard disks Throughput Database systems

Advances in nuclear detection and readout techniques

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Nuclear Science and Techniques 2023年第12期34卷 281-358页

作者： Rui He Xiao‑Yang Niu Yi Wang Hong‑Wei Liang Hong‑Bang Liu Ye Tian Hong‑Lin Zhang Chao‑Jie Zou Zhi‑Yi Liu Yun‑Long Zhang Hai‑Bo Yang Ju Huang Hong‑Kai Wang Wei‑Jia Han Bei Cao Gang Chen Cong Dai Li‑Min Duan Rui‑Rui Fan Fang‑Fa Fu Jian‑Hua Guo Dong Han Wei Jiang Xian‑Qin Li Xin Li Zhuo‑Dai Li Yu‑Tie Liang Shun Liao De‑Xu Lin Cheng‑Ming Liu Guo‑Rui Liu Jun‑Tao Liu Ze Long Meng‑Chen Niu Hao Qiu Hu Ran Xiang‑Ming Sun Bo‑Tan Wang Jia Wang Jin‑Xiang Wang Qi‑Lin Wang Yong‑Sheng Wang Xiao‑Chuan Xia Hao‑Qing Xie He‑Run Yang Hong Yin Hong Yuan Chun‑Hui Zhang Rui‑Guang Zhao Ran Zheng Cheng‑Xin Zhao Institute of Modern Physics Chinese Academy of SciencesLanzhou 730000China School of Nuclear Science and Technology University of Chinese Academy of SciencesBeijing 100049China Advanced Energy Science and Technology Guangdong Laboratory Huizhou 516000China Key Laboratory of Particle and Radiation Imaging Department of Engineering PhysicsTsinghua UniversityBeijing 100084China School of Integrated Circuits Dalian University of TechnologyDalian 116020China Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and TechnologyGuangxi UniversityNanning 530004China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China State Key Laboratory of Particle Detection and Electronics University of Science and Technology of ChinaHefei 230026China Department of Radiation Oncology National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100021China Electronic Engineering School Heilongjiang UniversityHarbin 150080China Spallation Neutron Source Science Center Dongguan 523803China Micro‑electronic Department Harbin Institute of TechnologyHarbin 150001China Purple Mountain Observatory Chinese Academy of SciencesNanjing 210034China Liaoning Academy of Materials Shenyang 110167China Istituto Nazionale di Fisica Nucleare Sezione di PerugiaPerugiaItaly School of Mathematics and Physics Chengdu University of TechnologyChengdu 620035China PLAC Key Laboratory of Quark and Lepton Physics(MOE)Central China Normal UniversityWuhan 430079China School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710129China State Key Lab of Superhard Materials College of PhysicsJilin UniversityChangchun 130012China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China

“A Craftsman Must Sharpen His Tools to Do His Job,”said *** detection and readout techniques are the foundation of particle physics,nuclear physics,and particle astrophysics to reveal the nature of the ***,they are being increasingly used in other disciplines like nuclear power generation,life sciences,environmental sciences,medical sciences,*** article reviews the short history,recent development,and trend of nuclear detection and readout techniques,covering Semiconductor Detector,Gaseous Detector,Scintillation Detector,Cherenkov Detector,Transition Radiation Detector,and Readout *** explaining the principle and using examples,we hope to help the interested reader underst and this research field and bring exciting information to the community.

关键词： Silicon detector Wide bandgap detector Time projection chamber(TPC) Multigap resistive plate chamber detector(MRPC) Scintillation detector Calorimeters Cherenkov detector Transition radiation detector Artificial intelligence Trigger-less readout

Diffraction-limited imaging with monolayer 2D material-based ultrathin flat lenses

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Light(Science & Applications) 2020年第1期9卷 696-706页

作者： Han Lin Zai-Quan Xu Guiyuan Cao Yupeng Zhang Jiadong Zhou Ziyu Wang Zhichen Wan Zheng Liu Kian Ping Loh Cheng-Wei Qiu Qiaoliang Bao Baohua Jia Centre for Translational Atomaterials Faculty of ScienceEngineering and TechnologySwinburne University of TechnologyP.O.Box 218HawthornVIC 3122Australia Department of Materials Science and Engineering ARC Centre of Excellence in Future Low-Energy Electronics Technologies(FLEET)Monash UniversityWellington RoadClaytonVIC 3800Australia School of Mathematical and Physical Sciences Faculty of ScienceUniversity of Technology Sydney15 BroadwayUltimoNSW 2007Australia Institute of Microscale Optoelectronics Lab of Artificial Microstructure for OptoelectronicsShenzhen University518000 ShenzhenChina School of Materials Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Chemistry National University of SingaporeSingapore 117543Singapore Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore The Australian Research Council(ARC)Industrial Transformation Training Centre in Surface Engineering for Advanced Materials(SEAM) Swinburne University of TechnologyP.O.Box 218HawthornVIC 3122Australia

Ultrathin flat optics allow control of light at the subwavelength scale that is unmatched by traditional refractive *** approach the atomically thin limit,the use of 2D materials is an attractive possibility due to their high refractive ***,achievement of diffraction-limited focusing and imaging is challenged by their thickness-limited spatial resolution and focusing *** we report a universal method to transform 2D monolayers into ultrathin flat *** laser direct writing was applied to generate local scattering media inside a monolayer,which overcomes the longstanding challenge of obtaining sufficient phase or amplitude modulation in atomically thin 2D *** achieved highly efficient 3D focusing with subwavelength resolution and diffractionlimited *** high focusing performance even allows diffraction-limited imaging at different focal positions with varying *** work paves the way for downscaling of optical devices using 2D materials and reports an unprecedented approach for fabricating ultrathin imaging devices.

关键词： ultrathin scattering optics

A perspective on meta-boundaries

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arXiv 2022年

作者： Zhang, Xinyan Chen, Jialin Chen, Ruoxi Wang, Chan Cai, Tong Chen, Hongsheng Lin, Xiao Interdisciplinary Center for Quantum Information State Key Laboratory of Modern Optical Instrumentation ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou310027 China International Joint Innovation Center Key Lab. of Advanced Micro/Nano Electronic Devices & Smart Systems of Zhejiang The Electromagnetics Academy Zhejiang University Zhejiang University Haining314400 China Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa32000 Israel Air and Missile Defense College Air Force Engineering University Xi’an710051 China

The judicious design of electromagnetic boundary provides a crucial route to control light-matter interactions, and it is thus fundamental to basic science and practical applications. General design approaches rely on the manipulation of bulk properties of superstrate or substrate and on the modification of boundary geometries. Due to the recent advent of metasurfaces and low-dimensional materials, the boundary can be flexibly featured with a surface conductivity, which can be rather complex but provide an extra degree of freedom to regulate the propagation of light. In this perspective, we denote the boundary with a non-zero surface conductivity as the meta-boundary. The meta-boundaries are categorized into four types, namely isotropic, anisotropic, biisotropic and bianisotropic meta-boundaries, according to the electromagnetic boundary conditions. Accordingly, the latest development for these four kinds of meta-boundaries are reviewed. Finally, an outlook on the research tendency of meta-boundaries is provided, particularly on the manipulation of light-matter interactions by simultaneously exploiting meta-boundaries and metamaterials. © 2022, CC BY.

关键词： Degrees of freedom (mechanics)

Adversarial Learning with Cost-Sensitive Classes

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arXiv 2021年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This paper proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples. We find in this framework an interesting phenomenon during the training of deep neural networks, called Min-Max property, that is, the absolute values of most parameters in the convolutional layer approach zero while the absolute values of a few parameters are significantly larger becoming bigger. Based on this Min-Max property which is formulated and analyzed in a view of random distribution, we further build a new defense model against adversarial examples for adversarial robustness improvement. An advantage of the built model is that it performs better than the standard one and can combine with adversarial training to achieve an improved performance. It is experimentally confirmed that, regarding the average accuracy of all classes, our model is almost as same as the existing models when an attack does not occur and is better than the existing models when an attack occurs. Specifically, regarding the accuracy of protected classes, the proposed model is much better than the existing models when an attack occurs. © 2021, CC BY.

关键词： Deep neural networks

Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior

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arXiv 2025年

作者： Dong, Hanyang Sheng, Shurong Wang, Xiongfei Gao, Jia-Hong Sun, Yi Yang, Wanli Xiao, Kuntao Teng, Pengfei Luan, Guoming Lv, Zhao School of Artificial Intelligence Anhui University Hefei230601 China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Department of Neurosurgery Beijing Key Laboratory of Epilepsy Sanbo Brain Hospital Capital Medical University Beijing China McGovern Institute for Brain Research Peking University Beijing China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Peking University Beijing China Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

A Magnetoencephalography (MEG) time-series recording consists of multi-channel signals collected by superconducting sensors, with each signal’s intensity reflecting magnetic field changes over time at the sensor location. Automating epileptic MEG spike detection significantly reduces manual assessment time and effort, yielding substantial clinical benefits. Existing research addresses MEG spike detection by encoding neural network inputs with signals from all channel within a time segment, followed by classification. However, these methods overlook simultaneous spiking occurred from nearby sensors. We introduce a simple yet effective paradigm that first clusters MEG channels based on their sensor’s spatial position. Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals. This module is fed into a custom MEEG-ResNet3D developed by the authors, which learns to extract relevant features and classify the input as a spike clip or not. Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%. Moreover, it demonstrates efficacy and stability in the Electroencephalographic (EEG) seizure detection task, yielding an improved weighted F1 score of 1.4% compared to current state-of-the-art techniques evaluated on TUSZ, whch is the largest EEG seizure dataset. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetoencephalography

NTIRE 2021 challenge on perceptual image quality assessment

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arXiv 2021年

作者： Gu, Jinjin Cai, Haoming Dong, Chao Ren, Jimmy S. Qiao, Yu Gu, Shuhang Timofte, Radu Cheon, Manri Yoon, Sungjun Kang, Byungyeon Lee, Junwoo Zhang, Qing Guo, Haiyang Bin, Yi Hou, Yuqing Luo, Hengliang Guo, Jingyu Wang, Zirui Wang, Hai Yang, Wenming Bai, Qingyan Shi, Shuwei Xia, Weihao Cao, Mingdeng Wang, Jiahao Chen, Yifan Yang, Yujiu Li, Yang Zhang, Tao Feng, Longtao Liao, Yiting Li, Junlin School of Electrical and Information Engineering The University of Sydney Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SenseTime Research Computer Vision Lab Eth Zurich Switzerland Lg Electronics Meituan Group Department of Electronic Engineering Tsinghua University Shenzhen International Graduate School Department of Electronic Engineering Tsinghua University Tsinghua Shenzhen International Graduate School Tsinghua University Department of Automation Tsinghua University Peking University Bytedance Inc.

This paper reports on the NTIRE 2021 challenge on perceptual image quality assessment (IQA), held in conjunction with the New Trends in Image Restoration and Enhancement workshop (NTIRE) workshop at CVPR 2021. As a new type of image processing technology, perceptual image processing algorithms based on Generative Adversarial Networks (GAN) have produced images with more realistic textures. These output images have completely different characteristics from traditional distortions, thus pose a new challenge for IQA methods to evaluate their visual quality. In comparison with previous IQA challenges, the training and testing datasets in this challenge include the outputs of perceptual image processing algorithms and the corresponding subjective scores. Thus they can be used to develop and evaluate IQA methods on GAN-based distortions. The challenge has 270 registered participants in total. In the final testing stage, 13 participating teams submitted their models and fact sheets. Almost all of them have achieved much better results than existing IQA methods, while the winning method can demonstrate state-of-the-art performance. © 2021, CC BY.

关键词： Generative adversarial networks

Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

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arXiv 2023年

作者： Teng, Siyu Hu, Xuemin Deng, Peng Li, Bai Li, Yuchen Yang, Dongsheng Ai, Yunfeng Li, Lingxi Xuanyuan, Zhe Zhu, Fenghua Chen, Long BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon 999077 Hong Kong The School of Computer Science and Information Engineering Hubei University Wuhan430062 China The State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body China The College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China University of Chinese Academy of Sciences Beijing100049 China The School of Public Management/Emergency Management Jinan University Guangzhou510632 China Indianapolis United States Guangdong provincial key lab of IRADS BNU-HKBU United International College Zhuhai519087 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd. China

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This paper reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results 17th

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MIPI 2022 Challenge on Under-Display Camera Image Restorati...

17th European Conference on computer Vision, ECCV 2022

作者： Feng, Ruicheng Li, Chongyi Zhou, Shangchen Sun, Wenxiu Zhu, Qingpeng Jiang, Jun Yang, Qingyu Loy, Chen Change Gu, Jinwei Zhu, Yurui Wang, Xi Fu, Xueyang Hu, Xiaowei Hu, Jinfan Liu, Xina Chen, Xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, Xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, Xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie Xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Jiang, Biao Ding, Yuhan Li, WanZhang Feng, Xiaoyue Chen, Sijing Zhong, Tianheng Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, Xiang Nanyang Technological University Singapore Singapore SenseBrain San Jose United States Shanghai AI Laboratory Shanghai China SenseTime Research and Tetras.AI Beijing China University of Science and Technology of China Hefei China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology Hefei China School of Artificial Intelligence Xidian University Xi’an China School of Remote Sensing and Information Engineering Wuhan University Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Harbin Institute of Technology Harbin China University of Macau China Changsha Research Institute of Mining and Metallurgy Changsha China Tianjin University Tianjin China Computer Vision Lab University of Wurzburg Würzburg Germany Couger Inc. Tokyo Japan School of Statistics and Data Science Nankai University Tianjin China Founding Minds Software Thiruvananthapuram India Department of Electronics and Communication SRM University AP Amaravati India Fudan University Shanghai China Shenyang Aerospace University Shenyang China Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9783031250712

Developing and integrating advanced image sensors with novel algorithms in camera systems is prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image reconstruction