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作者： Kabir, Tasnim Bangladesh University of Engineering and Technology

学位级别：硕士

Training machine learning models on large scale data to efficiently discover valuable information while maintaining the security and privacy of data remains an important research issue. Often many real-life applications such as health-care systems or financial organizations distribute data over many data centers and these data centers may have a different privacy policy. The joint decision over this dataset while not sharing the local information of the data centers is a must and becomes a challenging problem. The State-of-the-art method mainly relies on cryptographic technique to ensure the privacy for data communication between the data centers. This technique alone is not suitable for the large-scale geo-distributed datasets as this is designed for small-scale systems. To solve these problems, we propose a novel approach to achieve privacy-preserving Support Vector Machine (SVM) algorithm where the training set is distributed and each partition can contain large-scale data. We utilize the traditional SVM model to train the dataset of local data centers and with a few parameters sent by those training models, a centralized machine calculates the final result. We show that the proposed model is secure in an adverse environment and use experimental evaluation to demonstrate its correctness and computation speed compared to other parallel SVM training models. Our proposed distributed SVM (D-SVM) and time-constrained distributed SVM (TCD- SVM) algorithms scale the efficiency and speed of the learning network. We conduct exper- iments to compare our algorithms with traditional SVM and state-of-the-art algorithms using data sets collected from the UCI Machine Learning repository. We simulate these algorithms on Amazon Web Service (AWS) EC2 instances and show that using distributed SVM and time- constrained distributed SVM, we can achieve an improvement of 87. 5% and 90. 67% in task iv completion time, respectively, compared to the traditional SVM. We show that we can ac

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Multi-objective short-term scheduling of active distribution networks for benefit maximization of DisCos and DG owners considering demand response programs and energy storage system

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Journal of Modern Power systems and Clean Energy 2018年第1期6卷 95-106页

作者： Saeed ABAPOUR Sayyad NOJAVAN Mehdi ABAPOUR Faculty of Electrical and Computer Engineering University of Tabriz

This paper proposes a multi-objective benefit function for operation of active distribution systems considering demand response program(DRP)and energy storage system(ESS).In the active distribution system,active network management(ANM)is applied so that the distribution system equipment is controlled in real-time status based on the real-time measurements of system parameters(voltages and currents).The multi-objective optimization problem is solved using e-constraint method,and a fuzzy satisfying approach has been employed to select the best compromise *** different objective functions are considered as follows:benefit maximization of distribution company(DisCo);benefit maximization of distributed generation owner(DGO).To increase the benefits and efficient implementation of distributed generation(DG),DGO has installed battery as energy storage system(ESS)in parallel with DG ***,DGO decides for the battery charging/*** has the ability to exchange energy with the upstream network and ***,DisCo focuses to study the effect of demand response program(DRP)on total benefit function and consequently its influence on the load profile has been *** model is successfully applied to a 33-bus radial distribution network.

关键词： Multi-objective benefit function Active management distributed generation units Demand response programs Energy storage system

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Fast Virtual Prototyping of Cyber-Physical systems using SystemC and FMI: ADAS Use Case 30

Fast Virtual Prototyping of Cyber-Physical Systems using Sys...

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30th International Symposium on Rapid System Prototyping - Shortening the Path from Specification to Prototype (RSP)

作者： Saidi, Salah Eddine Charif, Amir Sassolas, Tanguy Le Guay, Pierre-Guillaume Souza, Henrique Vicente Ventroux, Nicolas CEA LIST Gif Sur Yvette France

ISBN: (纸本)9781450368476

The design of Cyber-Physical systems is becoming challenging due to their growing complexity. Simulation has proven to be effective for design validation of such complex systems at low cost. In particular, virtual prototyping allows early validation of hardware/software components. Considering the full cyber-physical context of a system allows more accurate testing and validation. This paper presents a fast virtual prototyping method for hardware and software co-validation through co-simulation of the whole Cyber-Physical System. The presented solution is based on FMI, a widely adopted standard for multi-domain co-simulation. The VPSim virtual prototyping tool is adapted to comply with the FMI standard. As such, an FMU that encapsulates a virtual hardware/-software platform can be easily and automatically generated using a high-level description of the hardware/software architecture. This FMU can then be incorporated in any FMI-compliant simulation tool. The proposed approach features a parallel implementation of the generated FMU to improve the co-simulation performance. An ADAS use-case is used to validate the proposed solution. Obtained results show that a real-time factor of 0.9 can be achieved using the proposed solution.

关键词： prototyping co-simulation SystemC FMI parallel ADAS

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Building web-based services for practical exercises in parallel and distributed computing

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JOURNAL OF parallel AND distributed COMPUTING 2018年 118卷 177-188页

作者： Sukhoroslov, Oleg Russian Acad Sci Inst Informat Transmiss Problems Kharkevich Inst Moscow Russia Natl Res Univ Higher Sch Econ Moscow Russia

The paper presents an approach to the design and implementation of web-based environments for practical exercises in parallel and distributed computing (PDC). The presented approach introduces minimal development and operational costs by relying on Everest, a general-purpose platform for building computational web services. The flexibility of proposed service-oriented architecture enables the development of different types of services targeting various use cases and PDC topics. The generic execution services support the execution of different types of parallel and distributed programs on corresponding computing systems, while the assignment evaluation services implement the execution and evaluation of solutions to programming assignments. As was demonstrated by teaching two introductory PDC courses, the presented approach helps to enhance students' practical experience while avoiding low-level interfaces, reducing the grading time and providing a level of automation necessary for scaling the course to a large number of students. In contrast to other efforts, the exploited Platform as a Service model provides the ability to quickly reuse this approach by other PDC educators without installation of the Everest platform. (C) 2018 Elsevier Inc. All rights reserved.

关键词： parallel and distributed computing education Programming assignments Program testing Web-based environment Web services Platform as a service

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Scheduling parallel real-time Recurrent Tasks on Multicore Platforms

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IEEE TRANSACTIONS ON parallel AND distributed systems 2018年第4期29卷 915-928页

作者： Pathan, Risat Voudouris, Petros Stenstrom, Per Chalmers Univ Technol Dept Comp Sci & Engn SE-41296 Gothenburg Sweden

We consider the scheduling of a real-time application that is modeled as a collection of parallel and recurrent tasks on a multicore platform. Each task is a directed-acyclic graph (DAG) having a set of subtasks (i.e., nodes) with precedence constraints (i.e., directed edges) and must complete the execution of all its subtasks by some specified deadline. Each task generates potentially infinite number of instances where the releases of consecutive instances are separated by some minimum inter-arrival time. Each DAG task and each subtask of that DAG task is assigned a fixed priority. A two-level preemptive global fixed-priority scheduling (GFP) policy is proposed: a task-level scheduler first determines the highest-priority ready task and a subtask-level scheduler then selects its highestpriority subtask for execution. To our knowledge, no earlier work considers a two-level GFP scheduler to schedule recurrent DAG tasks on a multicore platform. We derive a schedulability test for our proposed two-level GFP scheduler. If this test is satisfied, then it is guaranteed that all the tasks will meet their deadlines under GFP. We show that our proposed test is not only theoretically better but also empirically performs much better than the state-of-the-art test in scheduling randomly generated parallel DAG task sets.

关键词： real-time systems parallel DAG tasks global fixed-priority scheduling schedulability analysis multicore processors

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Disperse Access Considered Energy Inefficiency in Intel Optane DC Persistent Memory Servers

Disperse Access Considered Energy Inefficiency in Intel Opta...

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International Conference on distributed Computing systems

作者： Daping Li Jiguang Wan Jun Wang Jian Zhou Kai Lu Peng Xu Fei Wu Changsheng Xie Wuhan National Laboratory for Optoelectronics HUST China University of Central Florida USA

ISBN: (数字)9781728170022

ISBN: (纸本)9781728170039

The Intel Optane DC Persistent Memory Module (AEP), which is the first commercial available Non-Volatile Memory (NVM) product, offers comparable performance with DRAM while providing larger capacities and data persistence. Existing researches that substitute NVM with DRAM or hybridize them are either emulator-based or focused on how to improve the energy efficiency for writes. Unfortunately, the energy efficiency of the real AEP system is less explored. Based on real AEP, we observe that even though eliminating the DRAM-like refresh energy consumptions, AEP consumes significant different energy at different performance levels. Specifically, requests with time intervals (dispersed) underperform in both performance and energy efficiency when compared with the case of requests without time intervals (compact). This disparity and parallelism exploitation potentials motivate us to propose Sprint-AEP, an energy-efficiency-oriented scheduling method for AEP-equipped servers. Sprint-AEP fully activates adequate AEPs to serve most of the requests by deferring the write requests and prefetching the hottest data. The remaining AEPs will stay in idle mode with a low idle power to save energy. Besides, we also utilize the read parallelism to accelerate the sync and prefetching processes. Compared with energy-unaware AEP usages, our experimental results show that Sprint-AEP saves up to 26% energy with little performance degradation.

关键词： Nonvolatile memory Prefetching Random access memory parallel processing Energy efficiency Synchronization Servers

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distributed Algorithms for Fully Personalized PageRank on Large Graphs 19

Distributed Algorithms for Fully Personalized PageRank on La...

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World Wide Web Conference (WWW)

作者： Lin, Wenqing Tencent Inc Interact Entertainment Grp Shenzhen Peoples R China

ISBN: (纸本)9781450366748

Personalized PageRank (PPR) has enormous applications, such as link prediction and recommendation systems for social networks, which often require the fully PPR to be known. Besides, most of real-life graphs are edge-weighted, e.g., the interaction between users on the Facebook network. However, it is computationally difficult to compute the fully PPR, especially on large graphs, not to mention that most existing approaches do not consider the weights of edges. In particular, the existing approach cannot handle graphs with billion edges on a moderate-size cluster. To address this problem, this paper presents a novel study on the computation of fully edge-weighted PPR on large graphs using the distributed computing framework. Specifically, we employ the Monte Carlo approximation that performs a large number of random walks from each node of the graph, and exploits the parallel pipeline framework to reduce the overall running time of the fully PPR. Based on that, we develop several optimization techniques which (i) alleviate the issue of large nodes that could explode the memory space, (ii) pre-compute short walks for small nodes that largely speedup the computation of random walks, and (iii) optimize the amount of random walks to compute in each pipeline that significantly reduces the overhead. With extensive experiments on a variety of real-life graph datasets, we demonstrate that our solution is several orders of magnitude faster than the state-of-the-arts, and meanwhile, largely outperforms the baseline algorithms in terms of accuracy.

关键词： distributed Algorithm Graph Personalized PageRank

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Power-efficient scheduling of parallel real-time tasks on performance asymmetric multicore processors

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SUSTAINABLE COMPUTING-INFORMATICS & systems 2018年 17卷 81-95页

作者： Mahmood, Basharat Ahmad, Naveed Malik, Saif U. R. Anjum, Adeel Ul Islam, Saif COMSATS Inst Informat Technol Islamabad 46000 Pakistan

The use of computing devices has increased dramatically in recent time, which results in huge power consumption. This situation has made the power consumption a critical metric for evaluating the performance of a computing device. In this paper, we have addressed the real-time scheduling problem of parallel tasks on a performance asymmetric multicore processor with m cores with intent to reduce the power consumption. The proposed algorithm - parallel EDF - first divides the tasks into m segments and then executes these distributed tasks in earliest deadline first (EDF) fashion. Dynamic voltage and frequency scaling (DVFS) is also applied for power savings. We have evaluated the performance of the parallelEDF scheduling algorithm with Equally Fit (EF) algorithm on 70 nm based performance asymmetric multicore processor. The results reveal that up to 28% power can be saved at high system utilization level (about 80% system utilization). We have formally modeled the parallelEDF algorithm using high-level Petri nets (HLPN) while these models are also verified using the Satisfiability Modulo Theory (SMT), and Z3 Solver. (C) 2017 Elsevier Inc. All rights reserved.

关键词： real-time scheduling parallel tasks Power consumption DVFS Performance asymmetric multicore processor

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distributed Text Feature Selection Based On Bat Algorithm Optimization 10

Distributed Text Feature Selection Based On Bat Algorithm Op...

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10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems - Technology and Applications (IDAACS)

作者： Chen, Hongwei Hou, Qiao Han, Lin Hu, Thou Ye, Zhiwei Zeng, Jun Yuan, Jiansen Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Wuhan Fiberhome Tech Serv Co Ltd Wuhan 430074 Peoples R China

ISBN: (纸本)9781728140698

The feature selection effect directly affects the classification accuracy of the text. This paper introduces a new text feature selection method based on bat optimization. This method uses the traditional feature selection method to pre-select the original features, and then uses the bat group algorithm to optimize the pre-selected features in binary code form, and uses the classification accuracy as the individual fitness. However, when the amount of text information is large, the execution time of the single machine is long. According to this shortcoming, combining the Bat Algorithm and the Spark parallel computing framework, the text feature selection algorithm SBATFS is proposed. The algorithm combines the good search performance of the bat algorithm with the distributed and efficient calculation speed to realize the efficient solution of the text feature selection optimization model. The results show that compared with the traditional feature selection method, after SBATFS is used for feature optimization, the classification accuracy is effectively improved.

关键词： text classification feature selection bat algorithm Spark

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一种改进的基于深度神经网络的偏微分方程求解方法

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计算机工程与科学 2022年第11期44卷 1932-1940页

作者：陈新海刘杰万仟龚春叶国防科技大学并行与分布处理国家重点实验室湖南长沙410073 复杂系统软件工程湖南省重点实验室湖南长沙410073

偏微分方程求解是计算流体力学等科学与工程领域中数值分析的计算核心。由于物理的多尺度特性和对离散网格质量的敏感性,传统的数值求解方法通常包含复杂的人机交互和昂贵的网格剖分开销,限制了其在许多实时模拟和优化设计问题上的应用... 详细信息

偏微分方程求解是计算流体力学等科学与工程领域中数值分析的计算核心。由于物理的多尺度特性和对离散网格质量的敏感性,传统的数值求解方法通常包含复杂的人机交互和昂贵的网格剖分开销,限制了其在许多实时模拟和优化设计问题上的应用效率。提出了一种改进的基于深度神经网络的偏微分方程求解方法TaylorPINN。该方法利用深度神经网络的万能逼近定理和泰勒公式的函数拟合能力,实现了无网格的数值求解过程。在Helmholtz、Klein-Gordon和Navier-Stokes方程上的数值实验结果表明,TaylorPINN能够很好地拟合计算域内时空点坐标与待求函数值之间的映射关系,并提供了准确的数值预测结果。与常用的基于物理信息神经网络方法相比,对于不同的数值问题,TaylorPINN将预测精度提升了3~20倍。

关键词：偏微分方程数值分析神经网络泰勒公式无网格

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