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

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parallel Training of a Set of Online Sequential Extreme Learning Machines

Parallel Training of a Set of Online Sequential Extreme Lear...

引用

Computer Science Society (SCCC) international conference Chilean

作者： Elkin Gelvez-Almeida Ricardo J. Barrientos Karina Vilches-Ponce Marco Mora Universidad Católica del Maule Talca Chile Facultad de Ciencias Básicas Universidad Simón Bolívar San José de Cúcuta Colombia Laboratorio LITRP Facultad de Ciencias de la Ingeniería Universidad Católica del Maule Talca Chile Facultad de Ciencias de la Ingeniería Universidad Católica del Maule Talca Chile

ISBN: (纸本)9781665456753

Size databases have constantly increased from advances in technology and the Internet, so processing this vast amount of information has been a great challenge. The neural network Extreme Learning Machine (ELM) have been widely accepted in the scientific community due to their simplicity and good generalization capacity. This model consists of randomly assigning the weights of the hidden layer, and analytically calculating the weights of the output layer through the Moore- Penrose generalized inverse. High-Performance computing has emerged as an excellent alternative for tackling problems involving large-scale databases and reducing processing times. The use of parallel computing tools in Extreme Learning Machines and their variants, especially the Online Sequential Extreme Learning Machine (OS-ELM), has proven to be a good alternative to tackle regression and classification problems with largescale databases. In this paper, we present a parallel training methodology consisting of several Online Sequential Extreme Learning Machines running on different cores of the Central Processing Unit, with a balanced fingerprint database having 2,000,000 samples distributed in five classes. The results show that training and validation times decrease as the number of processes increases since the number of samples to train in each process decreases. In addition, by having several Online Sequential Extreme Learning Machines trained, new samples can beclassified on any of them.

关键词： Extreme learning machines Databases Training Fingerprint recognition Computer science Computational modeling Reactive power

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Fog computing Based Traffic and Car Parking Intelligent System 19th

Fog Computing Based Traffic and Car Parking Intelligent Syst...

引用

19th international conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Alajali, Walaa Gao, Shang Alhusaynat, Abdulrahman D. Deakin Univ Melbourne Vic 3217 Australia Thi Qar Univ Nasiriyah 64001 Iraq

ISBN: (纸本)9783030389611;9783030389604

Internet of Things (IoT) has attracted the attention of researchers from both industry and academia. Smart city, as one of the IoT applications, includes several sub-applications, such as intelligent transportation system (ITS), smart car parking and smart grid. Focusing on traffic flow management and car parking systems because of their correlation, this paper aims to provide a framework solution to both systems using online detection and prediction based on fog computing. Online event detection plays a vital role in traffic flow management, as circumstances, such as social events and congestion resulting from accidents and roadworks, affect traffic flow and parking availability. We developed an online prediction model using an incremental decision tree and distributed the prediction process on fog nodes at each intersection traffic light responsible for a connecting road. It effectively reduces the load on the communication network, as the data is processed, and the decision is made locally, with low storage requirements. The spatially correlated fog nodes can communicate if necessary to take action for an emergency. The experiments were conducted using the Melbourne city open data.

关键词： Traffic flow Prediction ITS Smart car parking

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distributed Graph Analytics 16th

Distributed Graph Analytics

引用

16th international conference on distributed computing and Internet Technology (ICDCIT)

作者： Srikant, Y. N. Indian Inst Sci Bengaluru 560012 India

ISBN: (纸本)9783030369873;9783030369866

Graph Analytics is important in different domains: social networks, computer networks, and computational biology to name a few. This paper describes the challenges involved in programming the underlying graph algorithms for graph analytics for distributed systems with CPU, GPU, and multi-GPU machines and how to deal with them. It emphasizes how language abstractions and good compilation can ease programming graph analytics on such platforms without sacrificing implementation efficiency.

关键词： Graph analytics Social networks parallel algorithms Multi-core processors GPU computation Graph frameworks Graph processing languages

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Data-driven Energy Theft Detection in Modern Power grids 21

Data-driven Energy Theft Detection in Modern Power Grids

引用

12th ACM international conference on Future Energy Systems, e-Energy 2021

作者： Althobaiti, Ahlam Jindal, Anish Marnerides, Angelos K. School of Computing and Communications Lancaster University United Kingdom School of Computer Science and Electronic Engineering University of Essex United Kingdom School of Computing Science University of Glasgow United Kingdom

ISBN: (纸本)9781450383332

Energy theft is an old and multifaceted phenomenon affecting our society on a global scale from both an operational as well as from a monetary perspective. The relatively recent decentralisation of the grid infrastructure with the integration of distributed Renewable Energy Resources (DRES) in synergy with the widely adopted demand-response business model, has undoubtedly broadened the spectrum of attack surface enabling energy theft. Conventional data-driven energy theft detection schemes have a strong dependency on assessing the spatio-temporal patterns of SCADA measurements aggregated at the Distribution System Operator (DSO) or Transmission System Operator (TSO) with minimal consideration of the intrinsic weather patterns related to individual DRES deployments. Hence, theft scenarios instrumented by DRES owners consuming the energy they produce (i.e., prosumers) can effectively be stealthy and hard to spot. Therefore, in this work we introduce a data-driven, SCADA-agnostic energy theft detection framework explicit to DRES-based scenarios. We provide a comprehensive formalisation of a DRES-based theft attack model and further assess the performance of our framework by utilising and relating freely available third-party weather measurements with real solar and wind turbine deployments in Australia and France. Evidently, our proposed framework yields an energy theft detection accuracy rate of over 98% with optimal computational costs. Thus, reasonably addressing the highly demanding requirements of low-cost and accurate real-time energy theft detection in modern power grids. © 2021 ACM.

关键词： Smart power grids

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Communication Avoiding 2D Stencil Implementations over PaRSEC Task-Based Runtime 34

Communication Avoiding 2D Stencil Implementations over PaRSE...

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34th IEEE international parallel and distributed Processing Symposium (IPDPS)

作者： Pei, Yu Cao, Qinglei Bosilca, George Luszczek, Piotr Eijkhout, Victor Dongarra, Jack Univ Tennessee Innovat Comp Lab Knoxville TN 37996 USA Univ Texas Austin Texas Adv Comp Ctr Austin TX USA

ISBN: (纸本)9781728174457

Stencil computation or general sparse matrix-vector product (SpMV) are key components in many algorithms like geometric multigrid or Krylov solvers. But their low arithmetic intensity means that memory bandwidth and network latency will be the performance limiting factors. The current architectural trend favors computations over bandwidth, worsening the already unfavorable imbalance. Previous work approached stencil kernel optimization either by improving memory bandwidth usage or by providing a Communication Avoiding (CA) scheme to minimize network latency in repeated sparse vector multiplication by replicating remote work in order to delay communications on the critical path. Focusing on minimizing communication bottleneck in distributed stencil computation, in this study we combine a CA scheme with the computation and communication overlapping that is inherent in a dataflow task-based runtime system such as PaRSEC to demonstrate their combined benefits. We implemented the 2D five point stencil (Jacobi iteration) in PETSc, and over PaRSEC in two flavors, full communications (base-PaRSEC) and CA-PaRSEC which operate directly on a 2D compute grid. Our results running on two clusters, NaCL and Stampede2 indicate that we can achieve 2X speedup over the standard SpMV solution implemented in PETSc, and in certain cases when kernel execution is not dominating the execution time, the CA-PaRSEC version achieved up to 57% and 33% speedup over base-PaRSEC implementation on NaCL and Stampede2 respectively.

关键词： 2D stencil communication avoiding parallel programming models

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TorqueDB: distributed Querying of Time-Series Data from Edge-local Storage 26th

TorqueDB: Distributed Querying of Time-Series Data from Edge...

引用

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

作者： Garg, Dhruv Shirolkar, Prathik Shukla, Anshu Simmhan, Yogesh Ericsson Res Bangalore Karnataka India Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9783030576752;9783030576745

The rapid growth in edge computing devices as part of Internet of Things (IoT) allows real-time access to time-series data from 1000's of sensors. Such observations are often queried to optimize the health of the infrastructure. Recently, edge storage systems allow us to retain data on the edge rather than moving them centrally to the cloud. However, such systems do not support flexible querying over the data spread across 10-100's of devices. There is also a lack of distributed time-series databases that can run on the edge devices. Here, we propose TorqueDB, a distributed query engine over time-series data that operates on edge and fog resources. TorqueDB leverages our prior work on ElfStore, a distributed edge-local file store, and InfluxDB, a time-series database, to enable temporal queries to be decomposed and executed across multiple fog and edge devices. Interestingly, we move data into InfluxDB on-demand while retaining the durable data within ElfStore for use by other applications. We also design a cost model that maximizes parallel movement and execution of the queries across resources, and utilizes caching. Our experiments on a real edge, fog and cloud deployment show that TorqueDB performs comparable to InfluxDB on a cloud VM for a smart city query workload, but without the associated monetary costs.

关键词： Edge storage Time-series database distributed querying Internet of Things Cloud computing

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Modeling The Temporally Constrained Preemptions of Transient Cloud VMs 20

Modeling The Temporally Constrained Preemptions of Transient...

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29th international conference on High-Performance parallel and distributed computing (HPDC)

作者： Kadupitiya, J. C. S. Jadhao, Vikram Sharma, Prateek Indiana Univ Bloomington IN 47405 USA

ISBN: (纸本)9781450370523

Transient cloud servers such as Amazon Spot instances, Google Preemptible VMs, and Azure Low-priority batch VMs, can reduce cloud computing costs by as much as 10x, but can be unilaterally preempted by the cloud provider. Understanding preemption characteristics (such as frequency) is a key first step in minimizing the effect of preemptions on application performance, availability, and cost. However, little is understood about temporally constrained preemptions-wherein preemptions must occur in a given time window. We study temporally constrained preemptions by conducting a large scale empirical study of Google's Preemptible VMs (that have a maximum lifetime of 24 hours), develop a new preemption probability model, new model-driven resource management policies, and implement them in a batch computing service for scientific computing workloads. Our statistical and experimental analysis indicates that temporally constrained preemptions are not uniformly distributed, but are time-dependent and have a bathtub shape. We find that existing memoryless models and policies are not suitable for temporally constrained preemptions. We develop a new probability model for bathtub preemptions, and analyze it through the lens of reliability theory. To highlight the effectiveness of our model, we develop optimized policies for job scheduling and checkpointing. Compared to existing techniques, our model-based policies can reduce the probability of job failure by more than 2x. We also implement our policies as part of a batch computing service for scientific computing applications, which reduces cost by 5x compared to conventional cloud deployments and keeps performance overheads under 3%.

关键词： preemption dynamics Google preemptible VMs cloud computing cloud VMs

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Collaborative computing in Vehicular Networks: A Deep Reinforcement Learning Approach

Collaborative Computing in Vehicular Networks: A Deep Reinfo...

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IEEE international conference on Communications (IEEE ICC) / Workshop on NOMA for 5G and Beyond

作者： Li, Mushu Gao, Jie Zhang, Ning Zhao, Lian Shen, Xuemin (Sherman) Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada Texas A&M Univ Corpus Christi Dept Comp Sci Corpus Christi TX USA Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada

ISBN: (纸本)9781728150895

Mobile edge computing (MEC) has been recognized as a promising technology to support various emerging services in vehicular networks. With MEC, vehicle users can offload their computation-intensive applications (e.g., intelligent path planning and safety applications) to edge computing servers located at roadside units. In this paper, an efficient computing offloading and server collaboration approach is proposed to reduce computing service delay and improve service reliability for vehicle users. Task partition is adopted, whereby the computation load offloaded by a vehicle can be divided and distributed to multiple edge servers. By the proposed approach, the computation delay can be reduced by parallel computing, and the failure in computing results delivery can also be alleviated via cooperation among edges. The offloading and computing decision-making is formulated as a long-term planning problem, and a deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to achieve the optimal solution of the complex stochastic nonlinear integer optimization problem. Simulation results show that our collaborative computing approach can adapt to different service environments and outperform the greedy offloading approach.

关键词： mobile edge computing vehicular network deep reinforcement learning computing offloading

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On-line monitoring system of grounding wire condition based on data acquisition technology

On-line monitoring system of grounding wire condition based ...

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The international conference on Forthcoming Networks and Sustainability (FoNeS 2022)

作者： H. Li Q. Wang S. Xie State Grid Jibei Electric Power Company Limited Skills Training Center Baoding Technical College of Electric Power Baoding Hebei People's Republic of China

In the power system, the transmission lines are scattered and difficult to protect and repair. The traditional safety measures for maintenance of transmission lines are very monotonous. Most of the task process is directly involved in manual work, and there is no testing facility for hanging ground wires, which is inefficient and unsafe. The main purpose of this paper is to design and improve the monitoring of the state of the ground wire based on the data acquisition technology. A simulation model considering the grounding wire is built, and the influence of different frequency signal injection, multi-branch line, transformer no-load input, the same pole in parallel, the earth-coupled grounding equivalent resistance and other factors are analyzed by simulation. Experiments show that when the frequency of the detection signal is 120 Hz, if the length of the detected line is longer and the grounding resistance is greater, the measured value of the response current when the grounding wire is in the grounding state and the measured value of the response current when there is no grounding wire is the same the smaller the difference.

关键词： computerised monitoring data acquisition earthing Internet power engineering computing wires (electric)

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A Submatrix-Based Method for Approximate Matrix Function Evaluation in the Quantum Chemistry Code CP2K

A Submatrix-Based Method for Approximate Matrix Function Eva...

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international conference on High Performance computing, Networking, Storage and Analysis (SC)

作者： Lass, Michael Schade, Robert Kuhnet, Thomas D. Plessl, Christian Paderborn Univ Paderborn Ctr Parallel Comp Warburger Str 100 D-33098 Paderborn Germany Paderborn Univ Dept Comp Sci Warburger Str 100 D-33098 Paderborn Germany Paderborn Univ Dept Chem Warburger Str 100 D-33098 Paderborn Germany

ISBN: (纸本)9781728199986

Electronic structure calculations based on density-functional theory (DFT) represent a significant part of today's HPC workloads and pose high demands on high-performance computing resources. To perform these quantum-mechanical DFT calculations on complex large-scale systems, so-called linear scaling methods instead of conventional cubic scaling methods are required. In this work, we take up the idea of the submatrix method and apply it to the DFT computations in the software package CP2K. For that purpose, we transform the underlying numeric operations on distributed, large, sparse matrices into computations on local, much smaller and nearly dense matrices. This allows us to exploit the full floating-point performance of modern CPUs and to make use of dedicated accelerator hardware, where performance has been limited by memory bandwidth before. We demonstrate both functionality and performance of our implementation and show how it can he accelerated with GPM and FPGAs.

关键词： Accelerator architectures Approximate computing Approximation algorithms Chemistry Density functional theory Linear algebra Open source software parallel algorithms Reconfigurable architectures Scientific computing Solid-state physics

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