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28th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)

作者： Dasari, Dakshina Becker, Matthias Casini, Daniel Blas, Tobias Robert Bosch GmbH Gerlingen Germany KTH Royal Inst Technol Stockholm Sweden TeCIP Inst Pisa Italy Scuola Super Sant Anna Dept Excellence Robot & AI Pisa Italy Saarland Univ Saarland Informat Campus SIC Saarbrucken Germany

ISBN: (纸本)9781665499989

Modern autonomous cars run classic AUTOSAR applications alongside advanced driving assistance systems on a single-vehicle computer. Ensuring safety and predictability in such a complex system is challenging and requires temporal isolation between the various components. A promising solution is the POSIX-compliant QNX operating system: it meets the automotive standards for functional safety at the highest level (ISO 26262 ASIL-D) and provides temporal isolation through the Adaptive partitioning Scheduler (APS), a resource reservation algorithm that guarantees processor bandwidth to groups of threads. These guarantees make it an ideal platform for composing diverse and complex applications on centralized vehicle computers. However, so far, there is no precise description or analysis of the APS reservation mechanism in real-time literature. In this paper, we provide the first description of the behavior of the APS from a real-time point of view and validate the results by running experiments on a real QNX platform. Based on the derived scheduler rules, we develop a response-time analysis to bound the end-to-end latency of event chains under APS. Finally, we evaluate different design strategies on a case study based on a real autonomous construction vehicle.

关键词： Adaptation models Analytical models Operating systems Real-time systems Safety Behavioral sciences partitioning algorithms

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Machine Learning for User partitioning and Phase Shifters Design in RIS-Aided NOMA Networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第11期69卷 7414-7428页

作者： Yang, Zhong Liu, Yuanwei Chen, Yue Al-Dhahir, Naofal Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England Univ Texas Dallas Dept Elect & Comp Engn Dallas TX 75080 USA

A novel reconfigurable intelligent surface (RIS) aided non-orthogonal multiple access (NOMA) downlink transmission framework is proposed. We formulate a long-term stochastic optimization problem that involves a joint optimization of NOMA user partitioning and RIS phase shifting, aiming at maximizing the sum data rate of the mobile users (MUs) in NOMA downlink networks. To solve the challenging joint optimization problem, we invoke a modified object migration automation (MOMA) algorithm to partition the users into equal-size clusters. To optimize the RIS phase shifting matrix, we propose a deep deterministic policy gradient (DDPG) algorithm to collaboratively control multiple reflecting elements (REs) of the RIS. Different from conventional training-then-testing processing, we consider a long-term self-adjusting learning model where the intelligent agent is capable of learning the optimal action for every given state through exploration and exploitation. Extensive numerical results demonstrate that: 1) The proposed RIS-aided NOMA downlink framework achieves enhanced sum data rate compared with the conventional orthogonal multiple access (OMA) framework. 2) The proposed DDPG algorithm is capable of learning a dynamic resource allocation policy in a long-term manner. 3) The performance of the proposed RIS-aided NOMA framework can be improved by increasing the granularity of the RIS phase shifts. The numerical results also show that increasing the number of reflecting elements (REs) is an efficient method to improve the sum data rate of the MUs.

关键词： NOMA Downlink Array signal processing Wireless communication Wireless networks Optimization Heuristic algorithms Machine learning partitioning algorithms wireless communication

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Distributed-Memory Parallel algorithms for Sparse Matrix and Sparse Tall-and-Skinny Matrix Multiplication 24

Distributed-Memory Parallel Algorithms for Sparse Matrix and...

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2024 International Conference for High Performance Computing, Networking, Storage and Analysis

作者： Ranawaka, Isuru Hussain, Md Taufique Block, Charles Gerogiannis, Gerasimos Torrellas, Josep Azad, Ariful Indiana Univ Bloomington IN 47405 USA Univ Illinois Champaign IL USA

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

We consider a sparse matrix-matrix multiplication (SpGEMM) setting where one matrix is square and the other is tall and skinny. This special variant, TS-SpGEMM, has important applications in multi-source breadth-first search, influence maximization, sparse graph embedding, and algebraic multi-grid solvers. Unfortunately, popular distributed algorithms like sparse SUMMA deliver suboptimal performance for TS-SpGEMM. To address this limitation, we develop a novel distributed-memory algorithm tailored for TS-SpGEMM. Our approach employs customized 1D partitioning for all matrices involved and leverages sparsity-aware tiling for efficient data transfers. In addition, it minimizes communication overhead by incorporating both local and remote computations. On average, our TS-SpGEMM algorithm attains 5x performance gains over 2D and 3D SUMMA. Furthermore, we use our algorithm to implement multi-source breadth-first search and sparse graph embedding algorithms and demonstrate their scalability up to 512 Nodes (or 65,536 cores) on NERSC Perlmutter.

关键词： Three-dimensional displays Shape Scalability Performance gain Data transfer partitioning algorithms Sparse matrices Distributed algorithms Parallel algorithms Optimization

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Service-Aware Two-Level partitioning for Machine Learning-Based Network Intrusion Detection With High Performance and High Scalability

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IEEE ACCESS 2021年 9卷 6608-6622页

作者： Uhm, Yeongje Pak, Wooguil DS Mentoring Corp Seoul 03909 South Korea Yeungnam Univ Dept Informat & Commun Engn Gyongsan 38541 South Korea

A network intrusion detection system (NIDS) is an important technology for cyber security. Recently, machine learning based NIDSs are being actively researched as various machine learning techniques are proposed. However, existing NIDSs have limitation in terms of generality because they have been designed based on specific characteristics obtained from analyzing some partial datasets. Moreover, in reality, the NIDS datasets have a significantly imbalanced ratio between normal and abnormal data. It causes the minority class problem, which needs to be addressed for developing robust and reliable NIDSs working in various environments. This paper proposes a novel technique using service-aware dataset partitioning, which provides high scalability to handle huge and rapidly growing network data flexibly, and helps the classifier to improve the classification performance in terms of accuracy and speed. We evaluated our approach with the Kyoto2016 dataset, which is a well-known dataset for highly imbalanced data, using various classification algorithms and parameters for achieving the best performance and compared it with existing state-of-the-art approaches. Our experimental results indicated that our approach can classify network traffics rapidly and accurately with huge imbalanced datasets. We conclude that it can relieve serious existing issues of imbalanced datasets for modern machine learning based NIDS solutions.

关键词： Classification algorithms Machine learning algorithms Machine learning partitioning algorithms Support vector machines Heuristic algorithms Scalability Network intrusion detection service based partitioning imbalanced data machine learning minority class problem

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Efficient Hierarchical State Vector Simulation of Quantum Circuits via Acyclic Graph partitioning

Efficient Hierarchical State Vector Simulation of Quantum Ci...

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IEEE International Conference on Cluster Computing (CLUSTER)

作者： Fang, Bo Ozkaya, M. Yusuf Li, Ang Catalyurek, Umit V. Krishnamoorthy, Sriram Pacific Northwest Natl Lab Richland WA 99352 USA Amazon Web Serv Seattle WA USA Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA

ISBN: (数字)9781665498562

ISBN: (纸本)9781665498562

Early but promising results in quantum computing have been enabled by the concurrent development of quantum algorithms, devices, and materials. Classical simulation of quantum programs has enabled the design and analysis of algorithms and implementation strategies targeting current and anticipated quantum device architectures. In this paper, we present a graph-based approach to achieving efficient quantum circuit simulation. Our approach involves partitioning the graph representation of a given quantum circuit into acyclic subgraphs/circuits that exhibit better data locality. Simulation of each sub-circuit is organized hierarchically, with the iterative construction and simulation of smaller state vectors, improving overall performance. Also, this partitioning reduces the number of passes through data, improving the total computation time. We present three partitioning strategies and observe that acyclic graph partitioning typically results in the best time-to-solution. In contrast, other strategies reduce the partitioning time at the expense of potentially increased simulation times. Experimental evaluation demonstrates the effectiveness of our approach.

关键词： Computational modeling Graphics processing units Computer architecture Cluster computing Supercomputers partitioning algorithms Computational efficiency

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3-D partitioning for Large-Scale Graph Processing

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IEEE TRANSACTIONS ON COMPUTERS 2021年第1期70卷 111-127页

作者： Li, Xue Zhang, Mingxing Chen, Kang Wu, Yongwei Qian, Xuehai Zheng, Weiming Tsinghua Univ Dept Comp Sci & Technol Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing 100084 Peoples R China Tsinghua Univ Grad Sch Shenzhen Beijing 100084 Peoples R China Sangfor Inc Shenzhen Peoples R China Univ Southern Calif Ming Hsieh Dept Elect Engn Dept Comp Sci Los Angeles CA 90007 USA

Disk I/O is the major performance bottleneck of existing out-of-core graph processing systems. We found that the total I/O amount can be reduced by loading more vertices into memory every time. Although task partitioning of a graph processing system is traditionally considered equivalent to the graph partition problem, this assumption is untrue for many Machine Learning and Data Mining (MLDM) problems: instead of a single value, a vector of data elements is defined as the property for each vertex/edge. By dividing each vertex into multiple sub-vertices, more vertices can be loaded into memory every time, leading to less amount of disk I/O. To explore this new opportunity, we propose a category of 3-D partitioning algorithm that considers the hidden dimension to partition the property vector. The 3-D partitioning algorithm provides a new tradeoff to reduce communication costs, which is adaptive to both distributed and out-of-core scenarios. Based on it, we build a distributed graph processing system CubeSingleCube. Since network traffic is significantly reduced, Cube outperforms state-of-the-art graph-parallel system PowerLyra by up to 4.7x. By largely reducing the disk I/O amount, the performance of SingleCube is significantly better than state-of-the-art out-of-core system GridGraph (up to 4.5x).

关键词： partitioning algorithms Task analysis Programming Loading Memory management Microsoft Windows Computational efficiency Graph processing task partitioning distributed systems disk I/O big data

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Simultaneous Optimization of Optical Communication Link Placement and partitioning of Wide-Area Protection System in Smart Grids

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IEEE TRANSACTIONS ON POWER DELIVERY 2021年第5期36卷 3140-3149页

作者： Jia, Huibin Liu, Chuan Gai, Yonghe Ma, Jing North China Elect Power Univ Dept Elect & Commun Engn Baoding 071000 Peoples R China Global Energy Interconnect Res Inst Co Ltd Beijing Peoples R China North China Elect Power Univ Dept Elect & Commun Engn Beijing Peoples R China State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China

Based on modern sensors, communication networks and computing technologies, wide-area protection system (WAPS) can acquire electrical quantities to precisely locate faults in smart grids. In WAPS, the communication network is responsible for rapidly and reliably transmitting data between a regional master station and its slave stations. The real-time data transmission is heavily dependent on the communication link (CL) placement. In addition to the rational partitioning of WAPS, the CL placement should be chosen so as to ensure the good performance of WAPS. The aim of this study is to extend the traditional partitioning of WAPS to the simultaneous optimization problem of partitioning and optical CL placement. The simultaneous optimization problem is formalized so as to minimize the construction cost of WAPS. Besides the optical CL placement and partitioning, to ensure the reliable and real-time transmission of data, the link bandwidth and cost of each optical CL is also captured in the optimization problem. Finally, numerical simulations are carried out on the IEEE 39-bus, 57-bus and 118-bus standard testing cases. The simulation results illustrate that the optical CL scheme can be non-negligible and that it will significantly influence the partitioning of WAPS.

关键词： Optimization Substations Bandwidth Optical fiber communication Communication networks Smart grids partitioning algorithms Communication link partitioning placement smart grid wide-area protection

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Optimizing Vertex Pressure Dynamic Graph partitioning in Many-Core Systems

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IEEE TRANSACTIONS ON COMPUTERS 2021年第6期70卷 936-949页

作者： McCrabb, Andrew Bertacco, Valeria Univ Michigan Coll Engn Elect Engn & Comp Sci Ann Arbor MI 48109 USA

With the rise of graph-based algorithms in many applications, dynamic graphs have become critical for applications that work with real-time or time-series relationship data. Due to their large storage footprint, these applications require high parallelism and locality. Many-core architectures offer high parallelism for both static and dynamic graphs. However, systems operating on dynamic graphs must continuously repartition graph data across storage units to achieve good locality, load balancing, and performance. In this work, we examine the effectiveness and efficiency of different vertex-pressure repartitioning schemes, which move vertices so to co-locate them near their most relevant neighbors. We describe key repartitioning design choices and provide a thorough evaluation of the impact of a range of design features with different datasets. Our evaluation indicates that optimized dynamic repartitioning techniques can often provide over 2x performance speedup over state-of-the-art static solutions.

关键词： Heuristic algorithms Bandwidth Social networking (online) partitioning algorithms Market research Arrays Transportation Graphs and networks graph algorithms hardware

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Advances in Wearable Brain-Computer Interfaces From an Algorithm-Hardware Co-Design Perspective

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第7期69卷 3071-3077页

作者： Byun, Wooseok Je, Minkyu Kim, Ji-Hoon SAPEON Korea Architecture Team Seongnam 13486 South Korea Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea Ewha Womans Univ Dept Elect & Elect Engn Smart Factory Multidisciplinary Program Seoul 03760 South Korea

Brain-computer interface (BCI), a communication technology between brain and computer developed for a long time since the 1970s, can be incorporated into wearable devices by developing powerful signal processing algorithms and semiconductor technologies. For a satisfactory user experience based on BCI, high information transfer rate and low power consumption should be considered together without losing accuracy. Although many existing BCI algorithms have been mainly focused solely on the accuracy, their deployment on wearable devices is not straightforward due to the limited hardware resources and computational capabilities. This tutorial summarizes recent advances in wearable BCI algorithms and hardware implementations from an algorithm-hardware co-design perspective and discusses future directions.

关键词： Signal processing algorithms partitioning algorithms Hardware Wearable computers Recording Visualization Signal processing Brain-computer interface (BCI) algorithm-hardware co-design domain-specific architecture deep learning accelerator linear algebra accelerator

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Privacy-Preserving Scheme With Account-Mapping and Noise-Adding for Energy Trading Based on Consortium Blockchain

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2022年第1期19卷 569-581页

作者： Zhang, Xiaoyan Jiang, Shunrong Liu, Yiliang Jiang, Tao Zhou, Yong China Univ Min & Technol Dept Informat Sci Xuzhou 221116 Jiangsu Peoples R China Xi An Jiao Tong Univ Sch Cyber Sci & Engn Xian 710049 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China

The maturity in information technology and new energy technologies enables participants to generate, buy, and sell energy in energy trading systems. Although applying blockchain technology to energy trading has solved some drawbacks in traditional centralized energy systems, the openness and transparency characteristics make the trading records stored on the blockchain vulnerable to data-mining attacks that may cause indispensable privacy leakage. Due to high efficiency and low overhead, noise-addition is an appropriate solution for privacy preservation. Nonetheless, recent research on noise-addition needs to generate massive accounts, which brings a certain amount of waste and inconvenience for later regulation and management. To avoid the aforementioned issues, this paper proposes a consortium blockchain-enabled scheme to ensure the privacy of data stored on the blockchain and resist linking attacks initiated by data mining algorithms. Our scheme utilizes a dynamic partition algorithm to leverage an account mapping algorithm and a virtual token algorithm. Specifically, the account mapping algorithm utilizes a dynamic account allocation method to hide the trading distribution of active users. Furthermore, the virtual token algorithm applies Laplace noise to hide the actual energy consumption of inactive users and curb excessive accounts generation. Finally, we formally demonstrate the privacy and effectiveness of our proposed scheme in security analysis and experiment evaluations.

关键词： Blockchains Privacy Heuristic algorithms Batteries partitioning algorithms Encryption Differential privacy Consortium blockchain neighboring energy trading privacy-preserving account mapping Laplace noise

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