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Time-Triggered Scheduling for Nonpreemptive Real-Time DAG Tasks Using 1-Opt Local Search

IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2024年第11期43卷 3650-3661页

作者： Wang, Sen Li, Dong Huang, Shao-Yu Deng, Xuanliang Sifat, Ashrarul H. Huang, Jia-Bin Jung, Changhee Williams, Ryan Zeng, Haibo Virginia Tech Dept Elect & Comp Engn Blacksburg VA 24061 USA Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Univ Maryland Coll Pk Dept Comp Sci College Pk MD 20742 USA

Modern real-time systems often involve numerous computational tasks characterized by intricate dependency relationships. Within these systems, data propagate through cause-effect chains from one task to another, making it imperative to minimize end-to-end latency to ensure system safety and reliability. In this article, we introduce innovative nonpreemptive scheduling techniques designed to reduce the worst-case end-to-end latency and/or time disparity for task sets modeled with directed acyclic graphs (DAGs). This is challenging because of the noncontinuous and nonconvex characteristics of the objective functions, hindering the direct application of standard optimization frameworks. Customized optimization frameworks aiming at achieving optimal solutions may suffer from scalability issues, while general heuristic algorithms often lack theoretical performance guarantees. To address this challenge, we incorporate the "1-opt" concept from the optimization literature (Essentially, 1-opt means that the quality of a solution cannot be improved if only one single variable can be changed) into the design of our algorithm. We propose a novel optimization algorithm that effectively balances the tradeoff between theoretical guarantees and algorithm scalability. By demonstrating its theoretical performance guarantees, we establish that the algorithm produces 1-opt solutions while maintaining polynomial run-time complexity. Through extensive large-scale experiments, we demonstrate that our algorithm can effectively reduce the latency metrics by 20% to 40%, compared to state-of-the-art methods.

关键词： Measurement Processor scheduling Scalability Heuristic algorithms Real-time systems Polynomials partitioning algorithms Complexity theory Optimization Standards End-to-end latency optimization real-time system scheduling time-triggered scheduling (TTS)

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Degree-M Bethe and Sinkhorn Permanent Based Bounds on the Permanent of a Non-Negative Matrix

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第7期70卷 5289-5308页

作者： Huang, Yuwen Kashyap, Navin Vontobel, Pascal O. Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Indian Inst Sci Dept Elect Commun Engn Bengaluru 560012 India Chinese Univ Hong Kong Inst Theoret Comp Sci & Commun Hong Kong Peoples R China

The permanent of a non-negative square matrix can be well approximated by finding the minimum of the Bethe free energy function associated with some suitably defined factor graph;the resulting approximation to the permanent is called the Bethe permanent. Vontobel gave a combinatorial characterization of the Bethe permanent via degree-M Bethe permanents, which are based on degree-M covers of the underlying factor graph. In this paper, we prove a degree-M-Bethe-permanent-based lower bound on the permanent of a non-negative matrix, which solves a conjecture proposed by Vontobel in [IEEE Trans. Inf. Theory, Mar. 2013]. We also prove a degree-M-Bethe-permanent-based upper bound on the permanent of a non-negative matrix. In the limit M -> infinity , these lower and upper bounds yield known Bethe-permanent-based lower and upper bounds on the permanent of a non-negative matrix. Moreover, we prove similar results for an approximation to the permanent known as the (scaled) Sinkhorn permanent.

关键词： Linear matrix inequalities Upper bound Stochastic processes partitioning algorithms Minimization Information theory Indexes Permanent non-negative matrix graphical model Bethe approximation Sinkhorn approximation

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Mapping-Aware Kernel partitioning Method for CGRAs Assisted by Deep Learning

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第5期33卷 1213-1230页

作者： Kojima, Takuya Ohwada, Ayaka Amano, Hideharu Keio Univ Kohoku Ku Yokohama Kanagawa 2238522 Japan RIKEN Ctr Computat Sci Chuo Ku Kobe Hyogo 6500047 Japan

Coarse-grained reconfigurable architectures (CGRAs) provide high energy efficiency with word-level programmability rather than bit-level ones such as FPGAs. The coarser reconfigurability brings about higher energy efficiency and reduces the complexity of compiler tasks compared to the FPGAs. However, application mapping process for CGRAs is still time-consuming. When the compiler tries to map a large and complicated application data-flow-graph(DFG) onto the reconfigurable fabric, it tends to result in inefficient resource use or to fail in mapping. In case of failure, the compiler must divide it into several sub-DFGs and goes back to the same flow. In this work, we propose a novel partitioning method based on a genetic algorithm to eliminate the unmappable DFGs and improve the mapping quality. In order not to generate unmappable sub-DFGs, we also propose an estimation model which predicts the mappability and resource requirements using a DGCNN (Deep Graph Convolutional Neural Network). The genetic algorithm with this model can seek the most resource-efficient mapping without the back-end mapping process. Our model can predict the mappability with more than 98% accuracy and resource usage with a negligible error for two studied CGRAs. Besides, the proposed partitioning method demonstrates 53-75% of memory saving, 1.28-1.39x higher throughput, and better mapping quality over three comparative approaches.

关键词： Registers partitioning algorithms Arrays Routing Optimization Estimation Pipelines Coarse-grained reconfigurable architecture CGRA deep learning genetic algorithm graph partitioning

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Fast Domain Decomposition Algorithm Using Barycentric Interpolation and Overlapping Subdomains for 3D Multiscale Problems

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IEEE JOURNAL ON MULTISCALE AND MULTIPHYSICS COMPUTATIONAL TECHNIQUES 2025年 10卷 198-208页

作者： Kielian, Nils Stiemer, Marcus Univ Fed Armed Forces Hamburg Helmut Schmidt Univ Dept Theory Elect Engn & Numer Field Computat D-22043 Hamburg Germany

A robust data-transfer process for balanced domain decomposition is presented. This algorithm reduces the number of mesh elements required to solve certain 3D multiscale problems with the finite element method. In some examples, a reduction factor of up to 5 has been observed. The reduction is achieved by introducing an overlapping auxiliary domain to an originally non-overlapping domain decomposition scheme, allowing for individual meshing of the subdomains. The data transfer to couple the individually meshed subdomains is performed with the help of barycentric interpolation. Hence, the advantages of parallel solution of subdomain problems is combined with a stable inter-subdomain data transfer. The developed algorithm can be applied on problems with a scalar valued second order spatial elliptic differential operator in various fields of engineering, such as semiconductors, huge and complex biological cell clusters, heat conducting and pressure problems on multiple scales.

关键词： Data transfer Couplings Finite element analysis Interpolation Mortar Convergence Differential operators Boundary conditions partitioning algorithms Iterative methods Domain decomposition finite element method interface problems electro-quasistatic problems

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Task Allocation in Spatial Crowdsourcing: An Efficient Geographic Partition Framework

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第9期36卷 4943-4955页

作者： Zhao, Yan Chen, Xuanlei Ye, Guanyu Guo, Fangda Zheng, Kai Zhou, Xiaofang Aalborg Univ Dept Comp Sci DK-9220 Aalborg Denmark Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Chinese Acad Sci Inst Comp Technol CAS Key Lab AI Safety & Secur Beijing 100190 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Yangtze Delta Reg Inst Quzhou Chengdu 610054 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Shenzhen 518110 Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

Recent years have witnessed a revolution in Spatial Crowdsourcing (SC), in which people with mobile connectivity can perform spatio-temporal tasks that involve traveling to specified locations. In this paper, we identify and study in depth a new multi-center-based task allocation problem in the context of SC, where multiple allocation centers exist. In particular, we aim to maximize the total number of the allocated tasks while minimizing the allocated task number difference. To solve the problem, we propose a two-phase framework, called Task Allocation with Geographic Partition, consisting of a geographic partition and a task allocation phase. The first phase divides the whole study area based on the allocation centers by using both a basic Voronoi diagram-based algorithm and an adaptive weighted Voronoi diagram-based algorithm. In the allocation phase, we utilize a Reinforcement Learning method to achieve the task allocation, where a graph neural network with the attention mechanism is used to learn the embeddings of allocation centers, delivery points, and workers. To further improve the efficiency, we propose an early stopping optimization strategy for the adaptive weighted Voronoi diagram-based algorithm in the geographic partition phase and give a distance-constrained graph pruning strategy for the Reinforcement Learning method in the task allocation phase. Extensive experiments give insight into the effectiveness and efficiency of the proposed solutions.

关键词： Task analysis Resource management partitioning algorithms Crowdsourcing Training Reinforcement learning Optimization Geographic partition task allocation spatial crowdsourcing reinforcement learning

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Soft Multiprototype Clustering Algorithm via Two-Layer Semi-NMF

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第4期32卷 1615-1629页

作者： Zeng, Shan Duan, Xiangjun Bai, Jun Tao, Wei Hu, Kun Tang, Yuanyan Wuhan Polytech Univ Sch Math & Comp Sci Wuhan 430048 Peoples R China Deakin Univ Sch Informat Technol Melbourne Vic 3125 Australia Univ Sydney Sch Comp Sci Camperdown NSW 2050 Australia Univ Macau Fac Sci & Technol Macau 999078 Peoples R China

This article proposes a novel soft multiprototype clustering algorithm (SMP) for high-dimensional data clustering with noisy and complex structural patterns. SMP integrates dimensionality reduction, multiprototype clustering, and multiprototype merge clustering under a two-layer seminonnegative matrix factorization (semi-NMF) architecture. Specifically, the first semi-NMF layer performs multiprototype clustering, which solves the problem that a single prototype cannot represent complex data structures. Meanwhile, the multiprototype fuzzy clustering constraints ensure that the multiprototypes better characterize the original data structure. The second semi-NMF layer performs multiprototype merge clustering to mitigate the issues of heavy computation burden and poor antinoise performance of the spectral clustering algorithm. The introduction of the Laplace graph matrix regularization constraint in this layer assists SMP in completing the merging of multiprototypes with complex data structures. Comprehensive experiments demonstrate that the proposed method outperforms the state-of-the-art algorithms.

关键词： Clustering algorithms Dimensionality reduction Kernel Prototypes Switched mode power supplies partitioning algorithms Merging Fuzzy clustering Laplace graph regularization seminonnegative matrix factorization (semi-NMF) soft multiprototype clustering

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MoEI: Mobility-Aware Edge Inference Based on Model Partition and Service Migration

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第10期23卷 9437-9450页

作者： Liu, Zhicheng Tian, Meng Dong, Mianxiong Wang, Xiaofei Qiu, Chao Zhang, Cheng Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Adv Networking Tianjin 300350 Peoples R China Muroran Inst Technol Dept Sci & Informat Muroran Hokkaido 0508585 Japan Tianjin Univ Finance & Econ Inst Technol Tianjin 300222 Peoples R China

Deep neural networks are the cornerstone of many mobile intelligent systems, and their inference processes bring about computation-intensive tasks. Device-edge cooperative inference in mobile edge computing provides a fine-grained processing method to migrate the burden of inference computation. However, the geographical dispersion of resources and the mobility pattern of devices pose scheduling issues to be considered. In this paper, we propose a task scheduling framework for such device-edge systems to improve the pipeline time of model inference. First, we consider the resource provisioning strategy with a pre-fetching service migration setting in the environment of multiple mobile devices and edge nodes. Then, we leverage game theory to analyze the property of the decision-making process and propose an offline algorithm under complete information. Next, we propose an algorithm based on proximal policy optimization to enable mobile devices to make decisions in a distributed online manner. Further, we adopt a memory mechanism into the online algorithm to improve the decision-makers' understanding of the system environment. Experiments demonstrate the effectiveness of the two algorithms. The average pipeline time of the proposed online algorithm is only 61.44% of that of local processing, which is 1.196 times that of the proposed offline algorithm.

关键词： Mobile handsets Task analysis Computational modeling Inference algorithms Pipelines partitioning algorithms Multi-access edge computing Collaborative inference deep reinforcement learning mobile edge computing model partition service migration

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One-Step Joint Learning of Self-Supervised Spectral Clustering With Anchor Graph and Fuzzy Clustering for Land Cover Classification

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 11178-11193页

作者： Wu, Chengmao Zhang, Jiale Xian Univ Posts & Telecommun Xian 710121 Peoples R China

Spectral clustering, as an algorithm based on graph theory and spectral theory, has shown excellent performance in classification tasks of hyperspectral images in recent years. Although better results have been achieved, some challenges still exist. The inclusion of a priori information can increase the performance of spectral clustering algorithms;however, in practice, it is often unable to meet the demand for a priori information;at the same time, the two-stage structure of spectral clustering leads to bias and information loss during its clustering process. To address the above problems, we propose a one-step joint learning of self-supervised spectral clustering with anchor graph and fuzzy clustering. In this algorithm, we first use the BKHK method to generate anchor points that vary with the size of the dataset to enhance the quality of the similarity graph. After that, we use the idea of fuzzy clustering to optimize the membership and simultaneously learn the local and global structures of the data. Moreover, clustering information is integrated with pairwise constraint information in a unified update framework, which achieves mutual learning of clustering results and constraints under soft partitioning. After comparing with the existing algorithms, the proposed algorithm has better performance in classifying numerical datasets, remote sensing images, and hyperspectral datasets.

关键词： Clustering algorithms Hyperspectral imaging partitioning algorithms Classification algorithms Unsupervised learning Training Optimization Anchor graph fuzzy clustering hyperspectral image self-supervised spectral clustering

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Regularized Simple Multiple Kernel k-Means With Kernel Average Alignment

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第11期35卷 15910-15919页

作者： Li, Miaomiao Zhang, Yi Ma, Chuan Liu, Suyuan Liu, Zhe Yin, Jianping Liu, Xinwang Liao, Qing Changsha Univ Coll Elect Informat & Elect Engn Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Zhejiang Lab Hangzhou 311121 Peoples R China Southeast Univ Key Lab Comp Network & Informat Integrat Minist Educ Nanjing 211189 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China Dongguan Univ Technol Dongguan 523820 Guangdong Peoples R China Harbin Inst Technol Dept Comp Sci & Technol Shenzhen 518055 Peoples R China

Multiple kernel clustering (MKC) aims to learn an optimal kernel to better serve for clustering from several precomputed basic kernels. Most MKC algorithms adhere to a common assumption that an optimal kernel is linearly combined by basic kernels. Based on a min-max framework, a newly proposed MKC method termed simple multiple kernel k-means (SimpleMKKM) can acquire a high-quality unified kernel. Although SimpleMKKM has achieved promising clustering performance, we observe that it cannot benefit from any prior knowledge. This would cause the learned partition matrix may seriously deviate from the expected one, especially in clustering tasks where the ground truth is absent during the learning course. To tackle this issue, we propose a novel algorithm termed regularized simple multiple kernel k-means with kernel average alignment (R-SMKKM-KAA). According to the experimental results of existing MKC algorithms, the average partition is a strong baseline to reflect true clustering. To gain knowledge from the average partition, we add the average alignment as a regularization term to prevent the learned unified partition from being far from the average partition. After that, we have designed an efficient solving algorithm to optimize the new resulting problem. In this way, both the incorporated prior knowledge and the combination of basic kernels are helpful to learn better unified partition. Consequently, the clustering performance can be significantly improved. Extensive experiments on nine common datasets have sufficiently demonstrated the effectiveness of incorporation of prior knowledge into SimpleMKKM.

关键词： Kernel Optimization partitioning algorithms Clustering algorithms Tensors Minimization Learning systems Multiple kernel clustering (MKC) multiple kernel learning

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Efficient Skyline Frequent-Utility Itemset Mining Algorithm on Massive Data

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第7期36卷 3009-3023页

作者： He, Jingxuan Han, Xixian Wan, Xiaolong Wang, Jinbao Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

Frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are two important branches of itemset mining which is a key technology of knowledge discovery in many applications. Nowadays, there have been extensive algorithms on FIM and HUIM, but few studies consider frequency and utility together, so skyline frequent-utility itemset mining (SFUIM) is proposed to find useful itemsets with both frequency and utility measurements. Nevertheless, SFUIM is more challenging than FIM and HUIM since the search space is large and the calculation cost is expensive without any threshold, especially on large-scale databases. To address it, this paper proposes a novel prefix-based algorithm PSI* to mine skyline frequent-utility itemsets on massive data. PSI* divides the huge database by prefix-based partitioning, so that the calculation of itemsets with a specific prefix-item only involves a partition instead of the database. A multilevel-index based list is presented to compactly maintain the maximal utility under the frequency constraint, and a novel grid-based structure is devised to organize partitions or items by a designed order. Moreover, four efficient pruning strategies are proposed to prune itemsets as early as possible. Substantial experiments show that the PSI* algorithm has better performance than the state-of-the-art algorithms, obviously on large-scale databases.

关键词： Itemsets Databases partitioning algorithms Data mining Dairy products Costs Frequency measurement Skyline frequent-utility itemset mining massive data prefix-based partitioning grid storage multilevel-index based list

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