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International Conference on parallel and distributed systems (ICPADS)

作者： Lin Wang Qingyang Zhang Youhuizi Li Hong Zhong Weisong Shi School of Computer Science and Technology Anhui University Hefei China Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou China Department of Computer Science Wayne State University Detroit MI USA

ISBN: (纸本)9781728125848

As the rapid growth of connected and autonomous vehicles (CAVs) and 5G intensifies, more third-party applications are increasingly being deployed on CAVs. They not only improve user experience but also provide more helpful services, for example, enhancing public safety by recognizing criminals in real-time videos. Current CAVs prefer to process collected data on the vehicle to avoid long transmission latency and extra network cost. However, due to the limitations of the on-board vehicle computing unit (VCU) and increasing use of computing-intensive in-vehicle applications, the burden of on-board VCU has sharply increased, which may affect driving safety. In particular, for existing vehicles on the road, adding more computing devices is a challenge if not impossible due to cost concerns. Inspired by edge computing, we propose a novel platform, MobileEdge, to enhance the computing capability of the unchangeable on-board VCU, which leverages mobile devices as edge nodes, e.g., the passengers' smartphones, by offloading computing tasks to them for collaboratively computing. Moreover, MobileEdge provides the dynamic management of mobile devices, monitoring device status and interfaces for customizable task offloading strategies and eventually achieves optimal task scheduling. We build a prototype to demonstrate the designed platform and evaluate three task offloading strategies which were implemented based on the developed interfaces. The results show that MobileEdge significantly reduces the application response latency. Compared with the baseline which does not employ task offloading, the response latency is almost near real-time when more computing resources are available. In addition, the proposed shortest response latency strategy outperforms the best overall task scheduling among the three strategies.

关键词： Edge computing Vehicular data analysis distributed computing Connected and autonomous vehicles distributed computing TASK SCHEDULING Response Latency UNLOADING vehicle vinyl chloride-urethane copolymer Mobile devices unchangeable quasi-realtime language Public safety

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Scalable Triangle Counting on distributed-Memory systems

Scalable Triangle Counting on Distributed-Memory Systems

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Seher Acer Abdurrahman Yaşar Sivasankaran Rajamanickam Michael Wolf Ümit V. Catalyürek Center for Computing Research Sandia National Laboratories Albuquerque NM U.S.A. College of Computing Georgia Institute of Technology Atlanta GA U.S.A.

Triangle counting is a foundational graph-analysis kernel in network science. It has also been one of the challenge problems for the “Static Graph Challenge”. In this work, we propose a novel, hybrid, parallel triangle counting algorithm based on its linear algebra formulation. Our framework uses MPI and Cilk to exploit the benefits of distributed-memory and shared-memory parallelism, respectively. The problem is partitioned among MPI processes using a two-dimensional (2D) Cartesian block partitioning. One-dimensional (1D) rowwise partitioning is used within the Cartesian blocks for shared-memory parallelism using the Cilk programming model. Besides exhibiting very good strong scaling behavior in almost all tested graphs, our algorithm achieves the fastest time on the 1.4B edge real-world twitter graph, which is 3.217 seconds, on 1,092 cores. In comparison to past distributed-memory parallel winners of the graph challenge, we demonstrate a speed up of 2.7× on this twitter graph. This is also the fastest time reported for parallel triangle counting on the twitter graph when the graph is not replicated.

关键词： parallel processing Two dimensional displays Task analysis Computer architecture Message systems Kernel Partitioning algorithms

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parallel algorithms for computing the smallest binary tree size in unit simplex refinement

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JOURNAL OF parallel AND distributed COMPUTING 2018年 112卷 166-178页

作者： Aparicio, G. Salmeron, J. M. G. Casado, L. G. Asenjo, R. Hendrix, E. M. T. Univ Almeria Res Grp TIC146 High Performance Comp Algorithms Almeria Spain Univ Almeria CeiA3 Informat Dept Almeria Spain Univ Malaga Comp Architecture Dept Malaga Spain

Refinement of the unit simplex by iterative longest edge bisection (LEB) up to sub-simplices have a size smaller or equal to a given accuracy, generates a binary tree. For a dimension higher than three, the size of the generated tree depends on the bisected LE. There may exist more than one selection sequence of LE that solves the Smallest Binary Tree Size Problem (SBTSP). Solving SBTSP by full enumeration requires considering every possible LE bisection in each sub-simplex. This is an irregular Combinatorial Optimization problem with an increasing computational burden in the dimension and the stopping criterion. Therefore, parallel computing is appealing to find the minimum size for hard instances in a reasonable time. The aim of this study is to develop and compare threaded algorithms running on multicore systems to solve the SBTS problem. Versions running on multicore systems with a static number of threads using TBB, and a dynamic number of threads using Pthread are compared. Interestingly, TBB scales better than the Pthread implementations for lower dimensional problems. However, when the problem dimension is higher than six, the Pthread approach with a dynamic number of threads finds a solution, where the TBB version fails. This is caused by the smaller memory footprint of the Pthread version, as it traverses deeper branches of the tree than the TBB work-stealing approach. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Regular simplex Longest edge bisection Binary tree TBB Pthreads Dynamic number of threads Shared memory

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distributed Reinforcement Learning in Emergency Response Simulation

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IEEE ACCESS 2018年 6卷 67261-67276页

作者： Lopez, Cesar Marti, Jose R. Sarkaria, Sarbjit Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada

This paper presents the implementation of a coordinated decision-making agent for emergency response scenarios. The agent's implementation uses reinforcement learning (RL). RL is a machine learning technique that enables an agent to learn from experimenting. The agent's learning is based on rewards, and feedback signals proportional to how good its actions are. The simulation platform used was infrastructure interdependencies simulator, in which, we have tested suitability of the approach in previous studies. In this paper, we have added new features to our previous solution, for enabling faster convergence and distributed processing. These additions include an enhanced reward scheme and a scheduler for orchestrating the distributed training. We include two test cases. The first case is a compact model with four critical infrastructures. In this model, the agent's training required only 10% of the attempts needed in our previous version. Improvements in convergence come from adding a shaping reward scheme. We trained the agent across 24 simultaneous configurations of our model. The training process elapsed 4 min. The extended case included more infrastructures and a higher level of detail. The dimensionality of the problem grew by a factor of 4000, but the training converged in less episodes. We tested the extended model over 96 parallel instances (potential scenarios) with completion in 2.87 min. The results show a fast and stable convergence. This agent can help during multiple stages of emergency response including real-time situations.

关键词： distributed reinforcement learning agent interdependencies simulation decision making in emergency response complex systems disaster response

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HPDC 2018 - Proceedings of the 2018 international symposium on high-performance parallel and distributed computing

HPDC 2018 - Proceedings of the 2018 international symposium ...

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27th ACM International Symposium on High-Performance parallel and distributed Computing, HPDC 2018

ISBN: (纸本)9781450357852

The proceedings contain 22 papers. The topics discussed include: hard real-time scheduling for parallel run-time systems;ABFR: convenient management of latent error resilience using application knowledge;improving performance of iterative methods by lossy checkponting;CommAnalyzer: automated estimation of communication cost and scalability on HPC clusters from sequential code;a high-performance connected components implementation for GPUs;process-in-process: techniques for practical address-space sharing;thread-local concurrency: a technique to handle data race detection at programming model abstraction;and profiling distributed systems in lightweight virtualized environments with logs and resource metrics.

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Extending the Life of Legacy Robots: MDS-Ach, a real-time, Process Based, Networked, Secure Middleware based on the x-Ach Methodology 15

Extending the Life of Legacy Robots: MDS-Ach, a Real-Time, P...

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15th IEEE International workshop on Advanced Motion Control (AMC)

作者： Lofaro, Daniel M. Ward, Colin Bugajska, Magdalena Sofge, Donald US Naval Navy Ctr Appl Res Artificial Intelligence Res Lab Washington DC 20375 USA George Mason Univ Elect & Comp Engn Fairfax VA 22030 USA US Naval Intelligent Syst Sect Res Lab Washington DC USA US Naval Distributed Autonomous Syst Grp Res Lab Washington DC USA

ISBN: (纸本)9781538619469

This work shows how to add modern tools to legacy robots while retaining the original tools and original calibration procedures/utilities through the use of a lightweight middleware connected to the communications level of the robot. MDS-Ach is a middleware made for the Xitome Mobile Dexterous Social (MDS) Robot originally released in 2008. The robot is being actively used at multiple locations including the U.S. Naval Research Laboratory's Laboratory for Autonomous systems Research (NRL-LASR). The MDS-Ach middleware gives the MDS Robot the software capabilities of modern robot systems using the x-Ach real-time processes based architecture. It controls the MDS Robot directly over the controller area network (CAN) bus via a dedicated real-time daemon. Each process communicates with the others over a network capable shared memory. The shared memory is a "first-in-last-out" (i.e. reads the newest data first) non-head-of-line blocking ring buffer which ensures readability of latest data first while retaining the ability to retrieve the older data. When running over a network, UDP or TCP protocol can be utilized depending on the timing and reliability requirements. SSH tunneling is used when secure connections between networked controllers are required. The MDS-Ach middleware is designed to allow for simple and easy development with modern robotic tools while adding accessibility and usability to our non-hardware-focused partners. real-time collision avoidance and a robust inverse kinematics solution are implemented within the MDS-Ach system. Examples of collision avoidance, inverse kinematics implementation, and the software architecture are given.

关键词： Robots Collision avoidance real-time systems Process control Middleware Hardware

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Proceedings of the 2018 IEEE/ACM 22nd International Symposium on distributed Simulation and real time Applications, DS-RT 2018

Proceedings of the 2018 IEEE/ACM 22nd International Symposiu...

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22nd IEEE/ACM International Symposium on distributed Simulation and real time Applications, DS-RT 2018

ISBN: (纸本)9781538650486

The proceedings contain 35 papers. The topics discussed include: LEATCH-L: low energy adaptive tier clustering hierarchy for large scale WSNs;anonymity and confidentiality in secure distributed simulation;performance implications of global virtual time algorithms on a knights landing processor;SEECSSim - a parallel and distributed simulation framework for mobile devices;data assimilation technique for social agent-based simulation by using reinforcement learning;towards a realistic 5G infrastructure emulator for experimental service deployment and performance evaluation;towards a schedulability-driven architecture exploration for mixed criticality multicore systems;SMILEY: a mixed-criticality real-time task scheduler for multicore systems;and outage probability analysis of power splitting power-beacon assisted energy harvesting relay wireless communication networks.

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Smart distributed Generation systems Using Artificial Neural Network-Based Event Classification

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IEEE Power and Energy Technology systems Journal 2018年第2期5卷 18-26页

作者： Haddad, Rami J. Guha, Bikiran Kalaani, Youakim El-Shahat, Adel Department of Electrical and Computer Engineering Georgia Southern University StatesboroGA30460 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

distributed generation (DG) sources have become an integral part of today's decentralized power systems. However, current DG systems are mostly passive and do not provide intelligent information to help detect power quality issues. In this paper, a novel and intelligent event classification scheme is proposed to provide the DG systems with real-time decision making capabilities. The proposed technique has the ability to provide information to help maintain the quality and reliability of the DG systems under various disturbances or operating conditions. This event classification technique was developed using artificial neural networks (ANNs) with a pre-defined set of local input parameters. The algorithm is implemented using four parallel ANNs that were designed to operate under a majority vote fusion algorithm representing the final classification output. A total of 310 event cases were generated to test the performance of the proposed technique. Simulation results showed that events were accurately classified within 10 cycles of their occurrences while achieving a 96.21% average classification accuracy. © 2018 IEEE.

关键词： distributed power generation

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Scalable Collectives for distributed Asynchronous Many-Task Runtimes 32

Scalable Collectives for Distributed Asynchronous Many-Task ...

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27th International Heterogeneity in Computing workshop in conjunction with 32nd IEEE International parallel and distributed Processing Symposium (IPDPS)

作者： Whitlock, Matthew Kolla, Hemanth Treichler, Sean Pebay, Philippe Bennett, Janine C. Oklahoma State Univ Stillwater OK 74078 USA Sandia Natl Labs Livermore CA 94550 USA Stanford Univ Stanford CA 94305 USA NexGen Analyt Sheridan WY USA

ISBN: (纸本)9781538655559

Global collectives (reductions/aggregations) are ubiquitous and feature in nearly every application of distributed high-performance computing (HPC). While it is advisable to devise algorithms by placing collectives off the critical path of execution, they are sometimes unavoidable for correctness, numerical convergence and analyses purposes. Scalable algorithms for distributed collectives are well studied and have become an integral part of MPI, but new and emerging distributed computing frameworks and paradigms such as Asynchronous Many-Task (AMT) models lack the same sophistication for distributed collectives. Since the central promise of AMT runtimes is that they automatically discover, and expose, task dependencies in the underlying program and can schedule work optimally to minimize idle time and hide data movement, a naively designed collectives protocol can completely offset any gains made from asynchronous execution. In this study we demonstrate that scalable distributed collectives are indispensable for performance in AMT models. We design, implement and test the performance of a scalable collective algorithm in Legion, an exemplar data-centric AMT programming model. Our results show that AMT systems contain the necessary primitives that allow for fully scalable collectives without breaking the transparent data movement abstractions. Scalabiity tests of an integrated Legion 1D stencil mini-application show the clear benefit of implementing scalable collectives and the performance degradation when a naive collectives alternative is used instead.

关键词： Programming Models Asynchronous Many-Task parallel Computing distributed Collectives Computational Statistics

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distributed Asynchronous Fusion Estimator for Stochastic Uncertain systems With Multiple Sensors of Different Fading Measurement Rates

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第3期66卷 641-653页

作者： Sun, Shuli Peng, Fangfang Lin, Honglei Heilongjiang Univ Sch Elect Engn Harbin 150080 Heilongjiang Peoples R China

This paper is concerned with the distributed fusion estimation problem for time-varying stochastic uncertain systems with multiple asynchronous sampling sensors of different fading measurement rates. Stochastic uncertainties of multiplicative noises exist in the state and measurement equations. The phenomena of fading measurements of different sensors are depicted by a group of stochastic variables with known statistics. Different sensors have different sampling rates. Sampling period of each sensor is uniform and an integer multiple of the state update period. By transforming the multiplicative noises into the additive noises, local estimators (LEs) and estimation error cross-covariance matrices between any two LEs are derived at the state update points. A real-time distributed fusion estimator (DFE) is obtained by using the matrix-weighted fusion estimation algorithm in the linear unbiased minimum variance sense. The asymptotic stability and steady-state property are analyzed. A new conception of period steady state is presented and the period steady-state properties of the LEs, cross-covariance matrices, and DFE are proven. Though DFE has lower accuracy than the centralized fusion estimator, it has better robustness and flexibility since it has a parallel structure. To avoid the calculation of cross-covariance matrices, a sequential covariance intersection fusion estimator is given by using two-sensor covariance intersection fusion algorithm. It has lower accuracy but smaller computational cost than DFE, and better accuracy than LEs. Two examples are given to show the effectiveness of the proposed algorithms.

关键词： distributed asynchronous fusion estimator multisensor multiplicative noise fading measurement stochastic uncertainty

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