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high-performance computing at a crossroads

science (New York, N.Y.) 2025年第6736期387卷 829-831页

作者： Deelman, Ewa Dongarra, Jack Hendrickson, Bruce Randles, Amanda Reed, Daniel Seidel, Edward Yelick, Katherine Information Sciences Institute University of Southern California Marina del Rey CA United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN United States Oak Ridge National Laboratory Oak Ridge TN United States Department of Mathematics University of Manchester Manchester United Kingdom Computing Directorate Lawrence Livermore National Laboratory Livermore CA United States Department of Biomedical Engineering Duke University Durham NC United States Scientific Computing and Imaging Institute University of Utah Salt Lake City UT United States Office of the President University of Wyoming Laramie WY United States Department of Electical Engineering and Computer Sciences University of California Berkeley Berkeley CA United States

Long-term plans and comprehensive vision are needed.

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Online Demand Peak Shaving with Machine-Learned Advice in Cyber-Physical Energy Systems

Online Demand Peak Shaving with Machine-Learned Advice in Cy...

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IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Minxi Fengl Wei Li Boyu Qin Albert Y. Zomaya Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Electrical Engineering State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University China

With the deep integration of cyber tools, control algorithms are increasingly employed in cyber-physical energy systems to enhance management, cost efficiency, and robustness. Effective demand load management is crucial in cyber-physical energy systems as it directly impacts operational costs. Failure to adequately manage spiky or seasonal demand loads can lead to significant expenses on monthly utility bills. In this study, we propose AMPAMOD, a randomized online algorithm with machine-learned advice, to optimize battery operations and mitigate highly dynamic peak loads. AMPAMOD utilizes limited advice from machine learning algorithms to guide our online algorithm and ensure cost-effective peak load management. The theoretical analysis shows that our solution has minimal advice complexity, a linear computational cost, and an improved competitive ratio. Finally, we conduct extensive trace-driven experiments on real-world datasets. AMPAMOD achieves a peak shaving success rate of over 90%, outperforming baselines by at least 50%. These experimental results confirm theoretical findings and demonstrate the potential of AMPAMOD.

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CoChain: high Concurrency Blockchain Sharding via Consensus on Consensus

CoChain: High Concurrency Blockchain Sharding via Consensus ...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Mingzhe Li You Lin Jin Zhang Wei Wang Computer Science and Engineering Department Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Computer Science and Engineering Department Hong Kong University of Science and Technology Technology and Research Institute of High Performance Computing and Agency for Science Singapore

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes (<2/3) while securing the system, thus reducing the shard size safely. We implement CoChain based on Harmony and conduct extensive experiments. Compared with Harmony, CoChain achieves 35x throughput gain with 6,000+ nodes.

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Data Logging and Non-invasive IoMT Approach for Rats Monitoring in laboratory Experiments 37th

Data Logging and Non-invasive IoMT Approach for Rats Monit...

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37th International Conference on Advanced information Networking and Applications, AINA 2023

作者： Barbosa, Bryan C. M. Garan, Steven A. Quintela, Barbara Melo Dantas, Mario R. Universidade Federal de Juiz de Fora Juiz de Fora Brazil Center for Research and Education in Aging University of California Berkeley and Lawrence Berkeley National Laboratory BerkeleyCA United States Computational Physiology and High Performance Computing Laboratory and Computer Science Department at Universidade Federal de Juiz de Fora Juiz de Fora Brazil Computer Science Graduate Program at Universidade Federal de Juiz de Fora Juiz de Fora Brazil

ISBN: (纸本)9783031290558

It is being observed that the use of Internet of Medical Things (IoMT) in health sciences research grows as the technology and miniaturization of devices occur. Those devices often times suffer from several issues such as: being invasive, lacking the ability to collect many signals concurrently and communication. The present work proposes an approach to the use of an ultra-small IoMT device to fulfill the functions of a bio-signals data collector, a mathematical models processor, a data transmitter-receptor through the network, and a mechanical actuators controller. Our test environment was conceived to be used in animal models. This is an ongoing project from which partial results indicate that, when functioning, the device will use real-time data for monitoring bio-signals and helping with decision-making as it interacts with the organism. The data collection is going to be made through low-cost non-invasive sensors. Therefore, it can favor installation, maintenance, access, and promote a better quality of life for the user. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Universal conservation laws of the wave-particleentanglement triad:theory and experiment

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Light(science & Applications) 2025年第3期14卷 861-868页

作者： Ziheng Ding Yaohao Deng Shao-Ming Fei Si-Qi Zhou Xiaojiong Chen Ziwen Rui Zhihao Ma Yunlong Xiao Jianwei Wang School of Mathematical Sciences MOE-LSCShanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory for Mesoscopic Physics School of PhysicsPeking UniversityBejing 100871China Bejing Academy of Quantum Information Sciences Beijing 100871China School of Mathematical Sciences Capital Normal UniversityBeijing 100048China Max Planck Institute for Mathematics in the Sciences Leipzig 04103Germany Department of Physics and Astronomy Stony Brook UniversityNew YorkNY11794-3800USA Shanghai Seres Information Technology Co. LtdShanghai 200040China shenzhen Institute for Quantum Science and Engineering Southern University of Science and TechnologyShenzhen 518055China A*STAR Quantum Innovation Centre(Q.InC) Agency for ScienceTechnology and Research(A*STAR)2Fusionopolis WayInnovis\#08-03Singapore138634Republic of Singapore institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis WayConnexis\#16-16Singapore 138632Republic of Singapore

When observed,a quantum system exhibits either wave-like or particle-like properties,depending on how it is ***,this duality is affected by the entanglement of the system with its quantum memory,raising a fundamental question:how are wave-particle duality and entanglement related?Here,we broaden the scope of wave-particle duality to include entanglement,introduce universal conservation laws for the wave-particle-entanglement triad,and perform demonstrations on silicon-integrated nanophotonic quantum *** experiments not only mark the first confirmation of universal conservation laws but also highlight the potential of integrated photonics for exploring complex quantum phenomena in high-dimensional systems.

关键词： high dimensional systems universal conservation laws entanglement nanophotonic quantum chips quantum systems wave particle duality quantum system

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A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning

arXiv

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arXiv 2024年

作者： Xu, Xiaoyang Yang, Mengda Yi, Wenzhe Li, Ziang Wang, Juan Hu, Hongxin Zhuang, Yong Liu, Yaxin Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Department of Computer Science and Engineering University at Buffalo SUNY United States

Split Learning (SL) is a distributed learning framework renowned for its privacy-preserving features and minimal computational requirements. Previous research consistently highlights the potential privacy breaches in SL systems by server adversaries reconstructing training data. However, these studies often rely on strong assumptions or compromise system utility to enhance attack performance. This paper introduces a new semi-honest Data Reconstruction Attack on SL, named Feature-Oriented Reconstruction Attack (FORA). In contrast to prior works, FORA relies on limited prior knowledge, specifically that the server utilizes auxiliary samples from the public without knowing any client’s private information. This allows FORA to conduct the attack stealthily and achieve robust performance. The key vulnerability exploited by FORA is the revelation of the model representation preference in the smashed data output by victim client. FORA constructs a substitute client through feature-level transfer learning, aiming to closely mimic the victim client’s representation preference. Leveraging this substitute client, the server trains the attack model to effectively reconstruct private data. Extensive experiments showcase FORA’s superior performance compared to state-of-the-art methods. Furthermore, the paper systematically evaluates the proposed method’s applicability across diverse settings and advanced defense strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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An optimal container update method for edge-cloud collaboration

An optimal container update method for edge-cloud collaborat...

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作者： Zhang, Haotong Lin, Weiwei Xie, Rong Li, Shenghai Dai, Zhiyan Wang, James Z. School of Software Engineering South China University of Technology Guangzhou China School of Computer Science & Engineering South China University of Technology Guangzhou China Department of New Network Technologies Peng Cheng Laboratory Shenzhen China School of Information Guangdong University of Finance & Economics Guangzhou China School of Computing Clemson University ClemsonSC United States

Emerging computing paradigms provide field-level service responses for users, for example, edge computing, fog computing, and MEC. Edge virtualization technologies represented by Docker can provide a platform-independent, low-resource-consumption operating environment for edge service. The image-pulling time of Docker is a crucial factor affecting the start-up speed of edge services. The layer reuse mechanism of native Docker cannot fully utilize the duplicate data of node local images. In this paper, we propose a chunk reuse mechanism (CRM), which effectively targets node-local duplicate data during container updates and reduces the volume of data transmission required for image building. We orchestrate the CRM process for cloud and remote-cloud nodes to ensure that the resource overhead from container update data preparation and image reconstruction is within an acceptable range. The experimental results show that the CRM proposed in this paper can effectively utilize the node local duplicate data in the synchronous update of containers in multiple nodes, reduce the volume of data transmission, and significantly improve container update efficiency. © 2023 John Wiley & Sons, Ltd.

关键词： Containers

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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng Han Xing-Yun Li Xin-Yu Yi Yun-Shao Zheng Wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

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Deep Learning Techniques in Mobile Edge computing for Internet of Medical Things 3

Deep Learning Techniques in Mobile Edge Computing for Intern...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Alawadhi, Abdulwadood Ahmad, R. Badlishah Almogahed, Abdullah Abrar, Ahmad Department of Computers and Information Technology Yemen Universiti Malaysia Perlis Faculty of Electronic Engineering and Technology Perlis Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Parit Raja Malaysia School of Computing Universiti Utara Malaysia InterNetWorks Research Laboratory Sintok-Kedah Malaysia

ISBN: (纸本)9798350305333

The fast expansion of the Internet of Medical Things (IoMT) has resulted in a ubiquitous home health diagnostic network. high patient demand results in high costs, short latency, and communication overload. As a result, 6G is the next generation of IoT, IoMT, and cellular networks, intending to considerably improve the quality of smart healthcare services through high throughput and decreased latency. So far, adopting cloud computing for time-critical applications and decreasing access delays to resources is difficult. Deep learning has been extensively employed to extract characteristics from complicated networks as artificial intelligence technology has advanced. On the other hand, deep learning models are often run in cloud computing data centres with tremendous processing resources. Conventional cloud computing systems primarily rely on the network, which has significant latency and security and privacy issues. One of the successful solutions is the Mobile Edge computing (MEC) paradigm, which brings cloud computing services closer to the edge network and uses available resources. Mobile edge computing places computer and storage nodes near mobile devices at the Internet's edge, leading to considerable savings in system operating time, memory cost, and power usage. Deep learning is used in mobile edge computing to forecast changes in demand based on daily patient behaviours. It also prepares the network by scaling up network resources as required. This study aims to discuss IoMT and identfy the problems in deep learning for mobile edge computing technology and its applications. © 2023 IEEE.

关键词： Deep learning

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Pipette: Efficient Fine-Grained Reads for SSDs

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第12期42卷 4721-4734页

作者： Bai, Shuhan Wan, Hu Huang, Yun Sun, Xuan Wu, Fei Xie, Changsheng Hsieh, Hung-Chih Kuo, Tei-Wei Xue, Chun Jason Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan430074 China City University of Hong Kong Department of Computer Science Pokfulam Hong Kong Imperial College London Department of Computing LondonSW7 2BX United Kingdom Yeestor Microelectronics Company Ltd. Department of Research and Development Shenzhen518057 China National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

Big data applications, such as recommendation system and social network, often generate a huge number of fine-grained reads to the storage. Block-oriented storage devices upon the traditional storage system rely on the paging mechanism to migrate pages to the host DRAM, tending to suffer from these fine-grained read operations in terms of I/O traffic as well as performance. Motivated by this challenge, an efficient fine-grained read framework, Pipette, is proposed in this article as an extension to the traditional I/O framework. With adaptive design for caching, merging, and scheduling, Pipette explores locality and acceleration for fine-grained read requests to establish an efficient byte-granular read path upon the dedicated byte-addressable interface. When the Pipette prototype on an SSD runs popular workloads, we measured throughput gains by up to 50% and 54% with traffic reduction in the range of 41.3× and 56.5× . © 1982-2012 IEEE.

关键词： Merging

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