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

JOURNAL OF parallel AND distributed COMPUTING 2018年第Part1期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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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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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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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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VOLAP: A Scalable distributed real-time OLAP System for High-Velocity Data

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IEEE TRANSACTIONS ON parallel AND distributed systems 2018年第1期29卷 226-239页

作者： Dehne, Frank Robillard, David Edward Rau-Chaplin, Andrew Burke, Neil Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada Dalhousie Univ Fac Comp Sci Halifax NS B3H 4R2 Canada

This paper presents VelocityOLAP (VOLAP), a distributed real-time OLAP system for high-velocity data. VOLAP makes use of dimension hierarchies, is highly scalable, exploits both multi-core and multi-processor parallelism, and can guarantee serializable execution of insert and query operations. In contrast to other high performance OLAP systems such as SAP HANA or IBM Netezza that rely on vertical scaling or special purpose hardware, VOLAP supports cost-efficient horizontal scaling on commodity hardware or modest cloud instances. Experiments on 20 Amazon EC2 nodes with TPC-DS data show that VOLAP is capable of bulk ingesting data at over 600 thousand items per second, and processing streams of interspersed insertions and aggregate queries at a rate of approximately 50 thousand insertions and 20 thousand aggregate queries per second with a database of 1 billion items. VOLAP is designed to support applications that perform large aggregate queries, and provides similar high performance for aggregations ranging from a few items to nearly the entire database.

关键词： Servers Aggregates real-time systems distributed databases Hardware Indexes

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An efficient Swarm-Intelligence approach for task scheduling in cloud-based internet of things applications

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第9期10卷 3469-3479页

作者： Boveiri, Hamid Reza Khayami, Raouf Elhoseny, Mohamed Gunasekaran, M. Shiraz Univ Technol Comp Engn & IT Dept Shiraz Iran Mansoura Univ Fac Comp & Informat Mansoura Egypt VIT Univ Vellore Tamil Nadu India

In our rapidly-growing big-data area, often the big sensory data from Internet of Things (IoT) cannot be sent directly to the far data-center in an efficient way because of the limitation in the network infrastructure. Fog computing, which has increasingly gained popularity for real-time applications, offers the utilization of local mini data-centers near the sensors to release the burden from the main data-center, and to exploit the full potential of cloud-based IoT. In this paper, a high-performance approach based on the Max-Min Ant System (MMAS), which is an efficient variation in the family of ant colony optimization algorithms, is proposed to tackle the static task-graph scheduling in homogeneous multiprocessor environments, the predominant technology used as mini-servers in fog computing. The main duty of the proposed approach is to properly manipulate the priority values of tasks so that the most optimal task-order can be achieved. Leveraging background knowledge of the problem, as heuristic values, has made the proposed approach very robust and efficient. Different random task-graphs with different shape parameters have been utilized to evaluate the proposed approach, and the results show its efficiency and superiority versus traditional counterparts from the performance perspective.

关键词： Cloud-based internet of things (IoT) Fog computing Max-min ant system (MMAS) Metaheuristics Multiprocessor task-graph scheduling parallel and distributed systems

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parallel Simulation of Virtual Testbed Applications 7th

Parallel Simulation of Virtual Testbed Applications

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7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2017

作者： Wahl, Arthur Waspe, Ralf Schluse, Michael Rossmann, Juergen Institute for Man-Machine Interaction RWTH Aachen University Ahornstarße 55 Aachen Germany

ISBN: (纸本)9783030014698

To enable the systematic evaluation of complex technical systems by engineers from various disciplines advanced 3D simulation environments that model all relevant aspects are used. In these Virtual Testbeds real-time capabilities are very hard to achieve without applying multi-threading strategies, due to the high complexity of the simulation. We present a novel simulation architecture that facilitates a modular approach to perform parallel simulations of arbitrary environments, where no explicit knowledge of the underlying simulation algorithms or model partitioning is needed by the engineer. Simulation components such as rigid-body dynamics, kinematics, renderer, controllers etc. can be distributed among different threads without concern about the specific technical realization. We achieve this by managing self-contained independent copies of the simulation model, each bound to one thread. © 2019, Springer Nature Switzerland AG.

关键词： Testbeds

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Speedup in parallel Rendering System with Computational Best-effort Scheme

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IEEE LATIN AMERICA TRANSACTIONS 2018年第3期16卷 987-995页

作者： Perez-Monte, C. Farias, F. Rizzi, S. Luciano, C. Piccoli, F. Univ Tecnol Nacl FRM Mendoza Argentina Argonne Natl Lab Argonne Leadership Comp Facil Lemont IL USA Univ Illinois Dept Bioengn Coll Med Chicago IL USA Univ Illinois Coll Med Dept Biomed & Hlth Informat Sci Chicago IL USA Univ Illinois Coll Med Dept Med Educ Chicago IL USA Univ Illinois Dept Urol Coll Med Chicago IL USA Univ Illinois Coll Engn Chicago IL USA Univ Illinois Coll Appl Hlth Sci Chicago IL USA Univ Nacl San Luis San Luis Argentina

Virtual reality (VR) surgical training and presurgical planning require the creation of 3D virtual models of patient anatomy from medical scan data. real-time head tracking in VR applications allows users to navigate in the virtual anatomy from any 3D position and orientation. The process of interactively rendering highly-detailed 3D volumetric data of anatomical models from a dynamically changing observer's perspective is extremely demanding for computational resources. parallel computing presents a solution to this problem, involving a distributed volume graphics rendering system composed of multiple nodes concurrently working on different portions of the output streaming, which are later integrated to form the final view. This paper presents a distributed graphics rendering system consisting of multiple GPU-based heterogeneous nodes running a best-effort rendering scheme. Experiments show promising results in terms of efficiency and performance for rendering medical volumes in real time.

关键词： Speedup parallel and distributed Graphic systems distributed Volume Rendering Monte Carlo Method real time VR 2DR AFR MFR

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distributed and automated machine learning in big data stream analytics 3

Distributed and automated machine learning in big data strea...

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3rd World Conference on Smart Trends in systems, Security and Sustainability, WorldS4 2019

作者： Alsahlee, Oraib H. Al-Zu’bi, Abdallah Alamro, Ibrahim I. Alwidian, Jaber Computer science Princess Sumaya university for technology Amman Jordan INFRASOFT Middle East /Big Data department United States

ISBN: (纸本)9781728137803

Distribution and automation of machine learning, a relatively new concepts occupying the scene of data science and its applications, in an era of massive data that it is growing by the second requiring complex processing and insight generation to feed the new industry of data-driven applications, utilizing the tools of machine learning and artificial intelligence built over big data infrastructures that support parallel and real-time computing, an approach that has fulfilled the requirements to a certain degree but yet requires much more optimization and a sense of autonomy to accommodate new developments and reduce human expertise dependency to achieve the bigger picture and fulfill the potential it has of revolutionizing the tech industry and overall scene. Here in this work we briefly investigate the components required to build such complex pipelines to fulfill autonomy and distribution requirements previously mentioned and propose some abstract concepts to integrate such components to achieve somewhat parallel machine learning pipeline that requires minimal human involvement. © 2019 IEEE.

关键词： Machine learning

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AutoSec: Multidimensional Timing-Based Anomaly Detection for Automotive Cybersecurity

AutoSec: Multidimensional Timing-Based Anomaly Detection for...

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IEEE International Conference on Embedded and real-time Computing systems and Applications (RTCSA)

作者： Milan Tepić Mohamed Abdelaal Marc Weber Kurt Rothermel Institute of Parallel and Distributed Systems University of Stuttgart Vector Informatik GmbH

ISBN: (数字)9781728144030

ISBN: (纸本)9781728144047

Nowadays, autonomous driving and driver assistance applications are being developed at an accelerated pace. This rapid growth is primarily driven by the potential of such smart applications to significantly improve safety on public roads and offer new possibilities for modern transportation concepts. Such indispensable applications typically require wireless connectivity between the vehicles and their surroundings, i.e. roadside infrastructure and cloud services. Nevertheless, such connectivity to external networks exposes the internal systems of individual vehicles to threats from remotely-launched attacks. In this realm, it is highly crucial to identify any misbehavior of the software components which might occur owing to either these threats or even software/hardware malfunctioning. In this paper, we introduce AutoSec, a host-based anomaly detection algorithm which relies on observing four timing parameters of the executed software components to accurately detect malicious behavior on the operating system level. To this end, AutoSec formulates the task of detecting anomalistic executions as a clustering problem. Specifically, AutoSec devises a hybrid clustering algorithm for grouping a set of collected timing traces resulted from executing the legitimate code. During the runtime, AutoSec simply classifies a certain execution as an anomaly, if its timing parameters are distant enough from the boundaries of the predefined clusters. To show the effectiveness of AutoSec, we collected timing traces from a testbed composed of a set of real and virtual control units communicating over a CAN bus. We show that using our proposed AutoSec, compared to baseline methods, we can identify up to 21% less false positives and 18% less false negatives.

关键词： Timing Software Clustering algorithms Task analysis Anomaly detection Software algorithms Safety

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