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TaskTorrent: A Lightweight distributed Task-Based Runtime System in C++ 3

TaskTorrent: A Lightweight Distributed Task-Based Runtime Sy...

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3rd ieee/ACM Annual Parallel Applications Workshop, Alternatives to MPI+X, PAW-ATM 2020

作者： Cambier, Leopold Qian, Yizhou Darve, Eric ICME Stanford University United States ME and ICME Stanford University United States

ISBN: (纸本)9781728154503

We present TaskTorrent, a lightweight distributed task-based runtime in C++.TaskTorrent uses a parametrized task graph to express the task DAG, and one-sided active messages to trigger remote tasks asynchronously. As a result the task DAG is completely distributed and discovered in parallel. It is a C++14 library and only depends on MPI. We explain the API and the implementation. We perform a series of benchmarks against StarPU and ScaLAPACK. Micro benchmarks show it has a minimal overhead compared to other solutions. We then apply it to two large linear algebra problems. TaskTorrent scales very well to thousands of cores, exhibiting good weak and strong scalings. © 2020 ieee.

关键词： Linear algebra

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Automatic Integration of Simulated systems into OPC UA Networks 16

Automatic Integration of Simulated Systems into OPC UA Netwo...

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16th ieee international conference on Automation Science and Engineering (CASE)

作者： Reitz, Jan Rossmann, Juergen Rhein Westfal TH Aachen Inst Man Machine Interact Aachen Germany

ISBN: (纸本)9781728169040

This paper demonstrates the integration of OPC Unified Architecture (OPC UA) communication capabilities in a simulation framework. The proposed approach is based on a data mapping from the simulation's meta data model to the OPC UA information model and a concurrent architecture based on message passing between the OPC UA server and the simulation software. The data mapping utilizes the simulation's and OPC UA's reflection mechanisms to automatically generate OPC UA address spaces from simulation models. The inverse mapping is used to automatically generate simulation model structure from queries to an existing OPC UA server. The concurrent architecture enables large numbers of simulated objects to be equipped with individual and independent OPC UA servers and clients. This allows the simulation of entire scenarios with simulated objects communicating with each other and with external software. The usefulness extends to distributed simulation use cases, where models populate individual instances of the simulation software and communicate over a real network. The implementation is applied to simulate an automotive production line, where multiple machines are equipped with individual OPC UA servers and communicate with 3rd party software. Stress tests show, that the implementation can handle significant network load.

关键词： OPC UA Data Mapping System Simulation distributed Simulation

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distributed Quantitative Vibration Demodulation with Direct-detection based Phase-sensitive OTDR Assisted by Acousto-optic Phase Shifting Technique 19

Distributed Quantitative Vibration Demodulation with Direct-...

引用

19th international conference on Optical Communications and Networks, ICOCN 2021

作者： Liu, Shuaiqi Yu, Feihong Xu, Weijie Vai, Mang I. Shao, Liyang Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau China

ISBN: (纸本)9781665424462

Quantitative demodulation of the external vibration is demonstrated with a direct-detection based Phase-sensitive OTDR system. An acousto-optic modulator is utilized for both probe pulse generation, and optical phase shifting simultaneously. The fiber under test is probed with a phase shifted double pulse pair scheme, with the phase difference between the two pulses periodically shifted with a π/2 step. The optical phase information is retrieved using the homodyne inter-pulse interference signal with direct detection only. Experimental results show good consistence between the phase demodulation result and the applied vibration amplitude under a cost-effective system configuration. © 2021 ieee.

关键词： Demodulation

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Feature Extraction and Classification of Odor Using Attention Based Neural Network 16th

Feature Extraction and Classification of Odor Using Attentio...

引用

16th international Symposium on distributed computing and Artificial Intelligence (DCAI)

作者： Fukuyama, Kohei Matsui, Kenji Omatsu, Sigeru Rivas, Alberto Manuel Corchado, Juan Osaka Inst Technol Fac Robot & Design Osaka Japan Univ Salamanca BISITE Digital Innovat Hub Salamanca Spain

ISBN: (纸本)9783030238872;9783030238865

In this paper, we explored a new approach of odor identification using deep learning. We also verified whether it is possible to classify odors in real time. Attention recurrent neural network (ARNN) is mainly used in the sequence to sequence model in terms of natural language processing field. We applied the simple ARNN embedding part with auto encoder to the deep learning model. This model can visualize the attention part of input data. Six types of aroma oil were measured using five types of metal oxide semiconductor gas sensors. Using the measured data, the accuracy was 96.83 [%] for training data and 96.88 [%] for the verification data.

关键词： Deep learning Sequence to sequence Attention mechanism Odor classification Visualization Metal oxide gas sensor

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3rd Workshop on Education for High Performance computing (EduHiPC - 2021)

3rd Workshop on Education for High Performance Computing (Ed...

引用

ieee international conference on High Performance computing Workshops (HiPCW)

High Performance computing (HPC) and, in general, Parallel and distributed computing (PDC) is ubiquitous. Every computing device, from a smartphone to a supercomputer, relies on parallel processing. Compute clusters of multicore and manycore processors (CPUs and GPUs) are routinely used in many subdomains of computer science, such as data science, parallel machine learning and high performance computing. Therefore, it is important for every computing professional (and especially every programmer) to understand how parallelism and distributed computing affect problem solving. It is essential for educators to impart a range of PDC and HPC skills and knowledge at multiple levels within the curriculum of Computer Science (CS), Computer Engineering (CE), and related disciplines such as computational data science. Software industry and research laboratories require people with these skills, more so now. Therefore, they now engage in extensive on-the-job training. Additionally, rapid changes in hardware platforms, languages, and programming environments increasingly challenge educators to decide what to teach and how to teach it, in order to prepare students for careers that are increasingly likely to involve PDC and HPC. EduHiPC aims to provide a forum that brings together academia, industry, government, and non-profit organizations – especially from India, its vicinity, and Asia – for exploring and exchanging experiences and ideas about the inclusion of high-performance, parallel, and distributed computing into undergraduate and graduate curriculum of Computer Science, Computer Engineering, Computational Science, Computational Engineering, and computational courses for STEM and business and other non-STEM disciplines.

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distributed Machine Learning for Resilient Operation of Electric systems 3

Distributed Machine Learning for Resilient Operation of Elec...

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3rd international conference on Smart Energy systems and Technologies (SEST)

作者： Amini, M. Hadi Imteaj, Ahmed Mohammadi, Javad Florida Int Univ Sch Comp & Informat Sci Miami FL 33199 USA FIU Sustainabil Optimizat & Learning InterDependent N Miami FL 33199 USA Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781728147017

Power system resilience is crucial to ensure secure energy delivery to electricity consumers. Power system outages lead to economical and societal burdens for the society and industries. To mitigate the socio-economical impacts of a power outage, we need to develop efficient algorithms to ensure resilient operation of the power system. In this paper, we first explain the notion of data-driven resilience. Then, we present a pathway of leveraging edge intelligence to improve resilience. To this end, we propose a novel distributed machine learning paradigm. Our proposed structure relies on local Resilience Management systems (RMS) that serve as intelligent decision-making entities in each area, e.g. an autonomous micro-grid or a smart home can act as RMS. The RMS agents, which are available in different areas, can share their local data (i.e., a microgrid's operational data) with their neighboring RMS to coordinate their decisions in a distributed fashion. This will provide two major advantages: 1) distributed intelligence replaces centralized decision-making leading to robust decision-making and enhanced resilience;2) since local data are locally shared among all entities within an RMS, if one of the RMS agents fails to communicate with the rest of network, we still can maintain a feasible solution (which is not necessarily optimal). Finally, we presents different scenarios in the simulation results section that showcases the system performance for two buildings under various outage scenarios.

关键词： distributed intelligence power system resilience information sharing natural hazards resilience value collaborative learning distributed storage demand response

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EG-ISFET Interface with ADC Conversion and Programmable Calibration

EG-ISFET Interface with ADC Conversion and Programmable Cali...

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ieee international conference on Circuits and systems (ICCS)

作者： R. M. Woo-García P. Argüelles-Lucho L. García-González Nerio Montoya-Giraldo A. L. Herrera-May F. López-Huerta Universidad Veracruzana Boca del Río Veracruz Mexico National University of Colombia Medellin Colombia Faculty of Construction and Habitat Engineering Universidad Veracruzana Boca del Río Veracruz Mexico

ISBN: (纸本)9781665429634

Currently the integration of sensors with a readout circuit and display are implemented in embedded systems for industrial, medical, chemical, and agricultural applications. This study presents the development of the conversion stage from analogical to digital, the calibration and the signal conditioning for an extended gate ion sensitive field-effect-transistor (EG-ISFET), based on an eight-bit microcontroller which offers a fast and reliable answer, the system shows the result of the ionic concentration of hydrogen (pH) of an electrolyte with a 3-display arrangement. The embedded system uses a calibration algorithm based on functions of mathematical correction beginning with the calculation of calibration coefficients and also shows a pH measurement in the range of 4 to 9, with a resolution of 51.6 mV/pH.

关键词： Embedded systems Microcontrollers Hydrogen Logic gates sensor systems and applications Ions Electrolytes

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Kullback-Leibler Averaging for Multitarget Density Fusion 16th

Kullback-Leibler Averaging for Multitarget Density Fusion

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16th international Symposium on distributed computing and Artificial Intelligence (DCAI)

作者： Da, Kai Li, Tiancheng Zhu, Yongfeng Fan, Hongqi Fu, Qiang Natl Univ Def Technol Coll Elect Sci Natl Key Lab Sci & Technol ATR Changsha 410073 Hunan Peoples R China Northwestern Polytech Univ Sch Automat Key Lab Informat Fus Technol Minist Educ Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9783030238872;9783030238865

This paper addresses the linear and log-linear fusion approaches to multitarget density fusion which yield arithmetic average (AA) and geometric average (GA), respectively. We reaffirm Abbas's finding in 2009 that both AA and GA can be related to the minimization of the Kullback-Leibler divergence (KLD) between the fusing densities and the fused result, which differ from each other in the reference used to measure the KLD: the AA uses the fusing densities while the GA uses the fused density. We derive the explicit AA expressions for fusing some known multitarget densities and discuss the implementation issues. The results serve as the theoretical basis for designing distributed random finite set filters for distributed multitarget tracking.

关键词： Average consensus Arithmetic average Linear fusion Random finite set sensor network Target tracking

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Multi-Agent Pathfinding with Hierarchical Evolutionary Hueristic A 3

Multi-Agent Pathfinding with Hierarchical Evolutionary Hueri...

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3rd ieee international conference on Artificial Intelligence and Knowledge Engineering AIKE 2020

作者： Yiu, Ying Fung Mahapatra, Rabi Texas A&M Univ Comp Sci & Engn Dept College Stn TX 77843 USA

ISBN: (纸本)9781728187082

Multiagent pathfinding (MAPF) problem is an important topic to various domains including video games, robotics, logistics, and crowd simulation. The goal of a pathfinding algorithm for MAPF is to find the shortest possible path for each agent without collisions with other agents. Search is among the most fundamental techniques for problem solving, and A* is the best known heuristic search algorithm. While A* guarantees to find the shortest path using a heuristic function, it cannot handle the large scale and many uncertainties in MAPF. The main challenge of MAPF is the scalability. The problem complexity grows exponentially as both the size of environments and the number of autonomous agents increase, which becomes more difficult for A* to compute results in real time under the constraints of memory and computing resources. To overcome the challenges in MAPF, distributed approaches are introduced to reduce the computational time and complexity. Contrast to centralized approaches, which use a single controller to determine every move of all agents, distributed approaches allow each agent to search for its own solution. distributed MAPF algorithms need to refine solutions for all agents that are collision-free. The algorithm should lead agents to take another path, or standby on the same node at the moment, to avoid conflicts between any two paths. Under the circumstances, an optimal solution is no longer simply finding the shortest path for each agent. Instead, it should contain a collision-free path for every agent, with the lowest makespan, which is the number of time steps required for all agents to reach their target. However, minimizing the makespan and the sum of cost for all agents is a NP-hard problem. Given MAPF problems often require to be solved in real time with limited resources, minimizing only the makespan is a more practical approach. To achieve accurate search and high scalability, a MAPF algorithm must fulfill the following requirements: 1) it is ca

关键词： Hierarchical A* search multi-agents pathfinding distributed computing genetic algorithm

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A Deep Learning Framework Supporting Model Ownership Protection and Traitor Tracing 26

A Deep Learning Framework Supporting Model Ownership Protect...

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26th ieee international conference on Parallel and distributed systems (ieee ICPADS)

作者： Xu, Guowen Li, Hongwei Zhang, Yuan Lin, Xiaodong Deng, Robert H. Shen, Xuemin Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Univ Guelph 50 Stone Rd E Guelph ON N1G 2W1 Canada Singapore Management Univ Singapore 178902 Singapore Univ Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781728190747

Cloud-based deep learning (DL) solutions have been widely used in applications ranging from image recognition to speech recognition. Meanwhile, as commercial software and services, such solutions have raised the need for intellectual property rights protection of the underlying DL models. Water-marking is the mainstream of existing solutions to address this concern, by primarily embedding pre-defined secrets in a model's training process. However, existing efforts almost exclusively focus on detecting whether a target model is pirated, without considering traitor tracing. In this paper, we present SecureMark_DL, which enables a model owner to embed a unique fingerprint for every customer within parameters of a DL model, extract and verify the fingerprint from a pirated model, and hence trace the rogue customer who illegally distributed his model for profits. We demonstrate that SecureMark_DL is robust against various attacks including fingerprints collusion and network transformation (e.g., model compression and model fine-tuning) Extensive experiments conducted on MNIST and CIFAR10 datasets, as well as various types of deep neural network show the superiority of SecureMark_DL in terms of training accuracy and robustness against various types of attacks.

关键词： Ownership Protection Traitor Tracing Water-marking Deep Learning Cloud computing

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