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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

共 34440 条记录，以下是4591-4600 订阅

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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection

Graph Structure Learning via Transfer Entropy for Multivaria...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mingyu Liu Yijie Wang Xiaohui Zhou Yongjun Wang National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors.

关键词： Learning systems Time-frequency analysis Fast Fourier transforms Time series analysis Signal processing Feature extraction Entropy Iterative methods Speech processing Anomaly detection

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international Program Committee

International Program Committee

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed computing (SNPD)

Provides a listing of current committee members and society officers.

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Efficient Context-Aware Edge computing Algorithm in Mobile Cloud

Efficient Context-Aware Edge Computing Algorithm in Mobile C...

引用

Computer Science and Management Technology (ICCSMT), international conference on

作者： Jian Chang Muchuan Yang Yan Gong Jiayu Liu Yuan Tian Qi Li State Grid Sichuan Information & Communicatioon Company Chengdu China

Edge computing provides a feasible technical solution to solve the problem of resource and energy supply constraints in terminal device deep learning applications. By migrating part of the deep learning application computing from the terminal to the cloud edge server, the computing pressure of the terminal device can be greatly relieved, and the energy consumption of the terminal can also be reduced to a certain extent. However, in the process of computing migration, the context switching loss of deep learning applications results in a long delay. This paper studies the problem of minimizing the completion time of deep learning applications under edge computing. We model the deep learning application as a task graph, and consider the constraints such as task switching delay, energy consumption and task dependency. Without changing the structure of the task graph, we transform it into a task scheduling-based application completion time minimization problem. In this paper, we first design a parallel scheduling algorithm based on greedy search. Considering the loss of context switching and the dependence of task data, we can allocate tasks to multiple processors for parallel processing by calculating the expected completion time of tasks, thus reducing the completion time of applications. Based on the experimental results, the greedy scheduling algorithm and comparison algorithm are analyzed and verified to optimize the completion time of different depth learning applications.

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Deep Time Series Anomaly Detection with Local Temporal Pattern Learning

Deep Time Series Anomaly Detection with Local Temporal Patte...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yizhou Li Yijie Wang Hongzuo Xu Xiaohui Zhou National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab (IGDL) Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Self-supervised time series anomaly detection (TSAD) demonstrates remarkable performance improvement by extracting high-level data semantics through proxy tasks. Nonetheless, most existing self-supervised TSAD techniques rely on manual- or neural-based transformations when designing proxy tasks, overlooking the intrinsic temporal patterns of time series. This paper proposes a local temporal pattern learning-based time series anomaly detection (LTPAD). LTPAD first generates sub-sequences. Pairwise sub-sequences naturally manifest proximity relationships along the time axis, and such correlations can be used to construct supervision and train neural networks to facilitate the learning of temporal patterns. Time intervals between two sub-sequences serve as labels for sub-sequence pairs. By classifying these labeled data pairs, our model captures the local temporal patterns of time series, thereby modeling the temporal pattern-aware "normality". Abnormal scores of testing data are acquired by evaluating their conformity to these learned patterns shared in training data. Extensive experiments show that LTPAD significantly outperforms state-of-the-art competitors.

关键词： Time series analysis Semantics Neural networks Training data Manuals Signal processing Data models Speech processing Anomaly detection Testing

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Massively parallel Causal Inference of Whole Brain Dynamics at Single Neuron Resolution 26

Massively Parallel Causal Inference of Whole Brain Dynamics ...

引用

26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Watanakeesuntorn, Wassapon Takahashi, Keichi Ichikawa, Kohei Park, Joseph Sugihara, George Takano, Ryousei Haga, Jason Pao, Gerald M. Nara Inst Sci & Technol Nara Japan US Dept Interior Gainesville FL USA Univ Calif San Diego San Diego CA 92103 USA Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki Japan Salk Inst Biol Studies 10010 N Torrey Pines Rd La Jolla CA 92037 USA

ISBN: (纸本)9781728190747

Empirical Dynamic Modeling (EDM) is a nonlinear time series causal inference framework. The latest implementation of EDM, cppEDM, has only been used for small datasets due to computational cost. With the growth of data collection capabilities, there is a great need to identify causal relationships in large datasets. We present mpEDM, a parallel distributed implementation of EDM optimized for modern GPU-centric supercomputers. We improve the original algorithm to reduce redundant computation and optimize the implementation to fully utilize hardware resources such as GPUs and SIMD units. As a use case, we run mpEDM on AI Bridging Cloud Infrastructure (ABCI) using datasets of an entire animal brain sampled at single neuron resolution to identify dynamical causation patterns across the brain. mpEDM is 1,530x faster than cppEDM and a dataset containing 101,729 neuron was analyzed in 199 seconds on 512 nodes. This is the largest EDM causal inference achieved to date.

关键词： Empirical Dynamic Modeling Causal Inference parallel distributed computing GPU High-Performance computing Neuroscience

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RITA: Efficient Memory Allocation Scheme for Containerized parallel Systems to Improve Data Processing Latency 13

RITA: Efficient Memory Allocation Scheme for Containerized P...

引用

IEEE 13th international conference on Cloud computing (CLOUD)

作者： Jia, Danlin Bayati, Mahsa Lee, Ron Mi, Ningfang Northeastern Univ Boston MA 02115 USA Samsung Semicond San Jose CA USA

ISBN: (纸本)9781728187808

The emerging resource-sharing container-based virtualization is prevalent in IT, as it is a much lighter deployment in the cloud environment compared to VM-based virtualization. distributed data-processing workloads executing in parallel take advantages of resource sharing, fast delivery, and excellent portability of containerization, but also suffer from resource competition and performance interference. Especially for memory virtualization, data-processing frameworks allocate physical memory (i.e., RAM) and swap to applications specified by users, without considering cache-characteristics and parallelism of applications, which induces performance degradation and significantly protracted latency which is worse given over-provisioning. We design an efficient memory allocation scheme (RITA) for containerized parallel systems to improve data processing latency. RITA monitors memory usage and cache characteristics of applications, and dynamically re-allocates memory resources. We implement RITA in a real-world system, which can easily migrate to other container-based virtualization environments. Our experimental results show that RITA provides remarkable latency improvement for memory intensive distributed data-processing workloads.

关键词： Containerization distributed Data-processing Latency Memory Allocation

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CDM Controller Design of a grid Connected Photovoltaic System 14th

CDM Controller Design of a Grid Connected Photovoltaic Syste...

引用

14th APCA international conference on Automatic Control and Soft computing (CONTROLO)

作者： Coelho, Joao Paulo Giernacki, Wojciech Goncalves, Jose Boaventura-Cunha, Jose Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Poznan Univ Tech Inst Control & Informat Engn Ul Piotrowo 3A PL-60965 Poznan Poland Univ Tras Os Montes & Alto Douro Escola Ciencias & Tecnol Vila Real Portugal INESC TEC Technol & Sci Campus FEUP P-4200465 Porto Portugal

ISBN: (纸本)9783030586553;9783030586539;9783030586522

distributed power sources will become increasingly ubiquitous in the near future. In this power production paradigm, photovoltaic conversion systems will play a fundamental role due to the growing tendency of energy price, and an opposed trend for the photovoltaic panels. This will lead to increased pressure for the installation of this particular renewable energy source in home buildings. In particular, on-grid photovoltaic systems where the generated power can be injected directly to the main power grid. This strategy requires the use of DC-AC inverters whose output is synchronized, in phase, with the main grid voltage. In order to provide steady output in the presence of load disturbances, the inverter must work in closed-loop. This work presents a new way to design an inverter controller by resorting to the CDM design technique. The obtained results suggest that the controller achieved with this method, although simpler than other methods, leads to an acceptable and robust closed-loop response.

关键词： CDM Power converter Renewable energy

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PPSS: A Privacy-Preserving Supervision Scheme Based on Blockchain

PPSS: A Privacy-Preserving Supervision Scheme Based on Block...

引用

IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Ming Chen Peichang Shi Xiang Fu Feng Jiang Fei Gao Penghui Ma Jinzhu Kong National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Kylin Software Co.LTD Changsha China Zhejiang Lab Hangzhou China

In JointCloud computing, multi-party participation introduces complexity and uncertainty. For all participants in JointCloud computing, both continuous supervision and necessary privacy protection are required. Traditional supervision methods usually adopt the centralized information interaction mode, which has such defects as collusion of interests, single point of failure, privacy disclosure, etc. Building a decentralized supervision mechanism has become a new research direction. In this paper, we propose PPSS, a privacy-preserving supervision scheme based on blockchain, which decentralizes the supervision of the participants in JointCloud computing, and combines the “double encryptions” and “threshold encryption” technologies to provide privacy protection. While making full use of the decentralization of the blockchain, a committee is established to carry out the analysis and decision-making tasks in terms of supervision and privacy protection. Experimental results indicate that PPSS can balance performance and security by reasonably configuring the committee.

关键词： Privacy Uncertainty Decision making Buildings Reliability engineering Blockchains Complexity theory

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Balanced and Compressed Coordinate Layout for the Sparse Matrix-Vector Product on GPUs

Balanced and Compressed Coordinate Layout for the Sparse Mat...

引用

26th international conference on parallel and distributed computing (Euro-Par)

作者： Aliaga, Jose Ignacio Anzt, Hartwig Quintana-Orti, Enrique S. Tomas, Andres E. Tsai, Yuhsiang M. Univ Jaume 1 Dept Ingn & Ciencia Comp Castellon De La Plana Spain Karlsruhe Inst Technol Steinbuch Ctr Comp Karlsruhe Germany Univ Tennessee Innovat Comp Lab Knoxville TN USA Univ Politecn Valencia DISCA Valencia Spain Univ Valencia Dept Informat Valencia Spain

ISBN: (纸本)9783030715939;9783030715922

We contribute to the optimization of the sparse matrixvector product on graphics processing units by introducing a variant of the coordinate sparse matrix layout that compresses the integer representation of the matrix indices. In addition, we employ a look-ahead table to avoid the storage of repeated numerical values in the sparse matrix, yielding a more compact data representation that is easier to maintain in the cache. Our evaluation on the two most recent generations of NVIDIA GPUs, the V100 and the A100 architectures, shows considerable performance improvements over the kernels for the sparse matrix-vector product in cuSPARSE (CUDA 11.0.167).

关键词： Sparse matrix-vector product Sparse matrix data layouts Sparse linear algebra High performance computing GPUs

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Hybrid job scheduling in distributed systems based on clone detection 6

Hybrid job scheduling in distributed systems based on clone ...

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6th international conference on parallel, distributed and grid computing, PDGC 2020

作者： Sen, Uddalok Sarkar, Madhulina Mukherjee, Nandini Asansol Engineering College Dept. of Computer Science and Engineering West Bengal Asansol India Government College of Engineering and Textile Technology Dept. of Computer Science and Engineering West Bengal Berhampore India Jadavpur University Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9781728171326

In order to propose an efficient scheduling policy in a large distributed heterogeneous environment, resource requirements of newly submitted jobs should be predicted prior to the execution of jobs. An execution history can be maintained to store the execution profile of all jobs executed earlier on a given set of resources. The execution history stores the actual CPU cycle consumed by the job as well as the resource details where it is executed. A feedback-guided job-modeling scheme can be used to detect similarity between the newly submitted jobs and previously executed jobs. It can also be used to predict resource requirements based on this similarity. However, efficient resource scheduling based on this knowledge has not been dealt with. In this paper, we propose a hybrid, scheduling policy of new jobs, which are independent of each other, based on their similarity with history jobs. Here we focus on exact clone jobs only i.e. its identical job is found in execution history and predicted resource consumption is same as exact resource consumption. We also endeavor to deal with two conflicting parameters i.e., execution cost and make span of jobs. A comparison with other existing algorithms is also presented in this paper. © 2020 IEEE.

关键词： Cloning

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