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International Conference on Parallel and Distributed systems (ICPADS)

作者： Qiang Hu Lixian Ma En Shao Guangming Tan State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China University of Chinese Academy of Science Beijing China

ISBN: (纸本)9781665408790

The advances of containers have significantly promoted the development of microservice architecture. This architecture splits a monolithic application into multiple independent components and the container orchestrator manages these components by the container in the cloud environment. The feasibility of deploying high performance computing(HPC) applications as microservices has been proven, but the existing container orchestrator incurs a large performance overhead as there is interference between different containers on the same physical host. In this paper, we design an agile workflow microservice system for HPC applications with fast-start OSv. We consider improving HPC workflow performance from two aspects: single OSv startup time optimization and workflow orchestration optimization. For single OSv startup time optimization, we design a fast-start OSv by analyzing the process of OSv startup and finding an optimization by modifying OSv source code. In this way, we get nearly 50% improvement of startup time. For workflow orchestration optimization, we propose four optimization techniques to speed up the execution of workflow by jointly considering OSv and workflow features, namely: node fusion, node merge, image preload, boot delay. Furthermore, we utilize our fast startup OSv to design an orchestration system for efficiently building an agile HPC workflow microservice by Kubevirt. Our experimental results optimization microservice system reduces the execution time by 30% compared with the original deployment with docker.

关键词： Cloud computing Conferences Buildings Microservice architectures computer architecture Interference Containers

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SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector

arXiv

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arXiv 2024年

作者： Mao, Qianchen Li, Qiang Wang, Bingshu Zhang, Yongjun Dai, Tao Chen, C.L. Philip The School of Software Northwestern Polytechnical University Taicang Campus Suzhou215400 China The School of Software Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Computer Science and Engineering South China University of Technology and Pazhou Lab Guangzhou510641 China

In recent years, the detection of infrared small targets using deep learning methods has garnered substantial attention due to notable advancements. To improve the detection capability of small targets, these methods commonly maintain a pathway that preserves high-resolution features of sparse and tiny targets. However, it can result in redundant and expensive computations. To tackle this challenge, we propose SpirDet, a novel approach for efficient detection of infrared small targets. Specifically, to cope with the computational redundancy issue, we employ a new dual-branch sparse decoder to restore the feature map. Firstly, the fast branch directly predicts a sparse map indicating potential small target locations (occupying only 0.5% area of the map). Secondly, the slow branch conducts fine-grained adjustments at the positions indicated by the sparse map. Additionally, we design an lightweight DO-RepEncoder based on reparameterization with the Downsampling Orthogonality, which can effectively reduce memory consumption and inference latency. Extensive experiments show that the proposed SpirDet significantly outperforms state-of-the-art models while achieving faster inference speed and fewer parameters. For example, on the IRSTD-1K dataset, SpirDet improves MIoU by 4.7 and has a 7× FPS acceleration compared to the previous state-of-the-art model. The code will be open to the public. © 2024, CC BY.

关键词： Deep learning

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Deep Reinforcement Agent for Failure-aware Job scheduling in High-Performance Computing

Deep Reinforcement Agent for Failure-aware Job scheduling in...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Kang Yang Rongyu Cao Yueyuan Zhou Jiawei Zhang En Shao Guangming Tan State Key Laboratory of Computer Architecture Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665408790

Job scheduling is crucial in high-performance computing (HPC), which is dedicated to deciding when and which jobs are allocated to the system and placing the jobs on which resources, by considering multiple scheduling goals. Along with the incremental of various resources and dazzling deep learning training (DLT) workloads, job failure becomes a quite common issue in HPC, which will affect user satisfaction and cluster utilization. To alleviate the influence of hardware and software errors as much as possible, in this paper, we aim to tackle the problem of failure-aware job scheduling in HPC clusters. Inspired by the success of previous studies of deep reinforcement learning-driven job scheduling, we propose a novel HPC scheduling agent named FARS (Failure-aware RL-based scheduler) by considering the effects of job failures. On the one hand, a neural network is applied to map the information of raw cluster and job states to job placement decisions. On the other hand, to consider the influence of job failure for user satisfaction and cluster utilization, FARS leverages make-span of the entire workload as the training objective. Additionally, effective exploration and experience replay techniques are applied to obtain effectively converged agent. To evaluate the capability of FARS, we design extensive trace-based simulation experiments with the popular DLT workloads. The experimental results show that, compared with the best baseline model, FARS obtains 5.69% improvement of average make-span under different device error rates. Together, our FARS is an ideal candidate for failure-aware job scheduler in HPC clusters.

关键词： Training Deep learning Processor scheduling Error analysis Computational modeling Conferences Neural networks

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Submodularity-based False Data Injection Attack Scheme in Multi-agent Dynamical systems

arXiv

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arXiv 2022年

作者： Luo, Xiaoyu Zhao, Chengcheng Fang, Chongrong He, Jianping The Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China The Department of Electrical and Computer Engineering University of Victoria BC Canada

Consensus in multi-agent dynamical systems is prone to be sabotaged by the adversary, which has attracted much attention due to its key role in broad applications. In this paper, we study a new false data injection (FDI) attack design problem, where the adversary with limited capability aims to select a subset of agents and manipulate their local multi-dimensional states to maximize the consensus convergence error. We first formulate the FDI attack design problem as a combinatorial optimization problem and prove it is NP-hard. Then, based on the submodularity optimization theory, we show the convergence error is a submodular function of the set of the compromised agents, which satisfies the property of diminishing marginal returns. In other words, the benefit of adding an extra agent to the compromised set decreases as that set becomes larger. With this property, we exploit the greedy scheme to find the optimal compromised agent set that can produce the maximum convergence error when adding one extra agent to that set each time. Thus, the FDI attack set selection algorithms are developed to obtain the near-optimal subset of the compromised agents. Furthermore, we derive the analytical suboptimality bounds and the worst-case running time under the proposed algorithms. Extensive simulation results are conducted to show the effectiveness of the proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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Microcomb-driven photonic convolutional engine

Microcomb-driven photonic convolutional engine

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Conference on Lasers and Electro-Optics (CLEO)

作者： Bowen Bai Qipeng Yang Haowen Shu Lin Chang Yichen Wu Fenghe Yang Bitao Shen Zihan Tao Weiqiang Xie Weiwei Hu John E. Bowers Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China Zhangjiang Laboratory Shanghai China Peking University Yangtze Delta Institute of Optoelectronics Nantong China

We demonstrate a microcomb-driven chip-based photonic convolutional engine utilizing time-wavelength plane stretching approach. A preeminent photonic-core compute density of 1.04 TOPS/mm 2 and a record high weight co... 详细信息

ISBN: (纸本)9781957171258

关键词： Calibration Distributed feedback lasers Edge detection Optical delay lines Parallel processing Silicon modulators

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High-Efficient Removal of Mercury in Water by the Flexible Nh2-Mil-53(Al): Exploiting The Gate Opening Effect

SSRN

引用

SSRN 2023年

作者： Jian, Meipeng Yang, Xiaoqin Huang, Congxin Tang, Chaochun Zhang, Xiwang Liu, Ruiping Li, Haiyan Beijing Energy Conservation & Sustainable Urban and Rural Development Provincial Ministry Co-construction Collaboration Innovation Center School of Environment and Energy Engineering Beijing University of Civil Engineering and Architecture Beijing100044 China Key Laboratory of Urban Stormwater System and Water Environment Ministry of Education Beijing University of Civil Engineering and Architecture Beijing100044 China School of Civil Engineering and Architecture East China JiaoTong University Jiangxi Nanchang330013 China Monash University Australia Center for Water and Ecology State Key Joint Laboratory of Environment Simulation and Pollution Control School of Environment Tsinghua University Beijing100084 China

Flexible metal-organic frameworks (MOFs) with the gate opening feature can optimize the removal efficiency on the adsorption behavior by accommodating to the specific target molecules. In this study, a flexible MOF denoted as NH2-MIL-53(Al) was investigated to remove Hg(II) from water, in which the adsorption capacities of large-pore NH2-MIL-53(Al) (NH2-MIL-53(Al)-LP) for Hg(II) reached 433.20 mg/g, which is about 4 times higher than narrow-pore NH2-MIL-53(Al) (112.75 mg/g). Further, the kinetic experiment also revealed that nearly 90% Hg(II) can be captured within 60 min for NH2-MIL-53(Al)-LP. Advanced characterizations and density functional theory calculations demonstrated that the strong coordination of the amino and oxygen centers on NH2-MIL-53(Al)-LP to Hg(II). Importantly, molecular dynamic simulation verified the gate opening effect of NH2-MIL-53(Al)-LP, as the faster diffusivity of Hg(II) ions in NH2-MIL-53(Al)-LP was observed. This work provides another opportunity in designing the pore geometry of porous materials for water remediation. © 2023, The Authors. All rights reserved.

关键词： Density functional theory

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Automatic Classification of Plasmodium for Malaria Diagnosis based on Ensemble Neural Network 2020

Automatic Classification of Plasmodium for Malaria Diagnosis...

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2nd International Conference on Intelligent Medicine and Image Processing, IMIP 2020

作者： Shi, Lulin Guan, Zhen Liang, Chunzi You, Haihang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Central South Hospital of Wuhan University Wuhan China

ISBN: (纸本)9781450377799

Malaria is one of the important public health issues of global concern. It is a kind of infectious disease caused by Plasmodium which can endanger human life and health. The examination of Plasmodium blood smear is the main method to diagnose malaria. Applying machine learning method to automatically analyze malaria smear images is very important for rapid diagnosis and surveillance of malaria. However, the existing machine learning methods need further improvement in feature extraction and generalization ability. For this reason, this paper introduces an ensemble neural network to automatically learn more accurate image features and achieve automatic classification of malaria images. In addition, we propose an adaptive threshold control method for cell segmentation, and then put the cell as center to extract images to obtain the training dataset, which solves the noise problem caused by sliding window cutting. And the idea of transfer learning is applied to solve the problem of shortage of training data. We additionally apply the proposed method to malaria image recognition and achieve 94.58% accuracy. The experimental results show that the model has good robustness and generalization ability and provides a valid data processing method for clinical malaria rapid diagnosis application. © 2020 ACM.

关键词： Diseases

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Empirical investigation of stochastic local search for maximum satisfiability

引用

Frontiers of computer Science 2019年第1期13卷 86-98页

作者： Yi CHU Chuan LUO Shaowei CAI Haihang YOU State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

The maximum satisfiability (MAX-SAT)problem is an important NP-hard problem in theory,and has a broad range of applications in *** local search (SLS)is becoming an increasingly popular method for solving ***,a powerful SLS algorithm called CCLS shows efficiency on solving random and crafted MAX-SAT ***,the performance of CCLS on solving industrial MAX-SAT instances lags far *** this paper,we focus on experimentally analyzing the performance of SLS algorithms for solving industrial MAXSAT ***,we conduct experiments to analyze why CCLS performs poor on industrial *** we propose a new strategy called additive BMS (Best from Multiple Selections)to ease the serious *** integrating CCLS and additive BMS,we develop a new SLS algorithm for MAXSAT called CCABMS,and related experiments indicate the efficiency of ***,we experimentally analyze the effectiveness of initialization methods on SLS algorithms for MAX-SAT,and combine an effective initialization method with CCABMS,resulting in an enhanced *** results show that our enhanced algorithm performs better than its state-of-the-art SLS competitors on a large number of industrial MAX-SAT instances.

关键词： empirical investigation stochastic local search maximum satisfiability industrial instances additive BMS

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A mopso based design exploration algorithm for micro-processor with elitist learning strategy 6th

A mopso based design exploration algorithm for micro-process...

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Liu, Niangao Zhi, Tian School of Information Science and Technology University of Science and Technology of China Hefei230026 China State Key Laboratory of Computer Architecture ICT CAS Beijing China

ISBN: (纸本)9789811581007

As the continuous development of processor technology, the design space exploration (DSE) has a critical impact on overall performance. However, due to the lack of systematic way, DSE is usually time-consuming and unaccomplished, leaving much of the opportunity unexploited. In this paper, on the basis of particle swarm optimization (PSO), a multi-object optimization algorithm with elitist learning strategy (ELS) is proposed, with which we construct a prediction model for microprocessor design in the purpose of optimizing performance and efficiency. The proposed algorithm is compared with two other multi-object optimization algorithms by three metrics. The experiment conducted shows the proposed algorithm is more efficient and more effective. © Springer Nature Singapore Pte Ltd 2020.

关键词： Particle swarm optimization (PSO)

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Distributed Nash Equilibrium Seeking for Aggregative Games with Mismatched Disturbances

Distributed Nash Equilibrium Seeking for Aggregative Games w...

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Chinese Control and Decision Conference, CCDC

作者： Qi Wang Feng Xiao Bo Wei State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources North China Electric Power University Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781665478977

In this paper, we focus on the distributed Nash equilibrium seeking problem for aggregative games for players described by double-integrator dynamics. Each player in the game is subject to unknown mismatched time-varying disturbances. To solve the Nash equilibrium seeking problem with mismatched disturbances, a distributed Nash equilibrium seeking algorithm is proposed. In aggregative games, the proposed algorithm guarantees the convergence of strategies of players to the Nash equilibrium of aggregative games and suppresses the influence of mismatched disturbances.

关键词： Heuristic algorithms Games Nash equilibrium Convergence Lyapunov methods

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