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Race Detection for Event-Driven *** applications 36

Race Detection for Event-Driven *** Applications

引用

36th IEEE/ACM international conference on Automated Software Engineering (ASE)

作者： Chang, Xiaoning Dou, Wensheng Wei, Jun Huang, Tao Xie, Jinhui Deng, Yuetang Yang, Jianbo Yang, Jiaheng Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Nanjing Inst Software Technol Nanjing Peoples R China Tencent Inc Guangzhou Peoples R China

ISBN: (纸本)9781665403375

*** has become a widely-used event-driven architecture for server-side and desktop applications. *** provides an effective asynchronous event-driven programming model, and supports asynchronous tasks and multi-priority event queues. Unexpected races among events and asynchronous tasks can cause severe consequences. Existing race detection approaches in *** applications mainly adopt random fuzzing technique, and can miss races due to large schedule space. In this paper, we propose a dynamic race detection approach NRace for *** applications. In NRace, we build precise happens-before relations among events and asynchronous tasks in *** applications, which also take multi-priority event queues into consideration. We further develop a predictive race detection technique based on these relations. We evaluate NRace on 10 real-world *** applications. The experimental result shows that NRace can precisely detect 6 races, and 5 of them have been confirmed by developers.

关键词： Node.js event-driven architecture race detection

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M3AT: Monitoring Agents Assignment Model for Data-Intensive applications 28

M3AT: Monitoring Agents Assignment Model for Data-Intensive ...

引用

28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Kashansky, Vladislav Kimovski, Dragi Prodan, Radu Agrawalt, Prateek Marozzo, Fabrizio Iuhaszl, Gabriel Justyna, Marek Garcia-Blas, Javier Univ Klagenfurt Inst Informat Technol Klagenfurt Austria South Ural State Univ Sch Elect Engn & Comp Sci Chelyabinsk Russia Lovely Profess Univ Phagwara India Univ Calabria DIMES Dept Calabria Italy West Univ Timisoara Timisoara Romania Poznan Supercomp & Networking Ctr Poznan Poland Univ Carlos III Madrid Dept Comp Sci & Engn Madrid Spain

ISBN: (纸本)9781728165820

Nowadays, massive amounts of data are acquired, transferred, and analyzed nearly in real-time by utilizing a large number of computing and storage elements interconnected through high-speed communication networks. However, one issue that still requires research effort is to enable efficient monitoring of applications and infrastructures of such complex systems. In this paper, we introduce an Integer Linear Programming (ILP) model called M3AT for optimized assignment of monitoring agents and aggregators on large-scale computing systems. We identified a set of requirements from three representative data-intensive applications and exploited them to define the model's input parameters. We evaluated the scalability of M3AT using the Constraint Integer Programing (SCIP) solver with default configuration based on synthetic data sets. Preliminary results show that the model provides optimal assignments for sub-systems composed of up to 200 monitoring agents with complex I/O policies, while keeping the number of aggregators constant and demonstrates variable sensitivity with respect to the scale of monitoring data aggregators and limitation policies imposed.

关键词： Monitoring systems high performance computing aggregation systems control data-intensive systems generalized assignment problem SCIP optimization suite

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GraBi: Communication-Efficient andWorkload-Balanced Partitioning for Bipartite Graphs 20

GraBi: Communication-Efficient andWorkload-Balanced Partitio...

引用

49th international conference on parallel processing (ICPP)

作者： Sheng, Feng Cao, Qiang Jiang, Hong Yao, Jie HUST KLISS WNLO Wuhan Hubei Peoples R China Univ Texas Arlington Arlington TX 76019 USA HUST Sch Comp Sci & Tech Wuhan Hubei Peoples R China

ISBN: (纸本)9781450388160

Machine Learning and Data Mining (MLDM) applications, such as recommendation and topic modeling, generally represent their input data in bipartite graphs with two disjoint vertex-subsets connected only by edges between them. Despite the prevalence of bipartite graphs, existing graph partitioning frameworks have rarely sufficiently exploited their unique structures, especially the highly lopsided subset sizes and extremely skewed vertex degrees. As a result of poor partitioning quality, problems, particularly of high communication cost and severe workload imbalance, arise during subsequent computation over these bipartite graphs in distributed environments such as datacenters or HPC systems, significantly hampering the performance of MLDM applications. In this paper, we approach these problems by communication-efficient and workload-balanced partitioning of bipartite graphs, which fully exploits the vertex vectorization in MLDM algorithms and inherent asymmetry in bipartite graphs. To this end, we present GraBi, a two-stage partitioning framework that partitions a bipartite graph first vertically and then horizontally. The first partitioning stage divides each vectored vertex into multiple vertex-chunks such that the bipartite graph is vertically partitioned into multiple layers, to strike an appropriate tradeoff between inter- and intra-vertex communication. In the second partitioning stage, for each layer, the vertex-chunks in the larger vertex-subset are first assigned to nodes, to minimize vertex replicas. To be specific, these vertex-chunks are horizontally decomposed into one or more sub-chunks with an upper-bounded number of edges, and then the sub-chunks are evenly assigned over nodes of a distributed system using a set of hash functions, to achieve workload balance among all computing nodes. GraBi is a lightweight partitioning framework for bipartite graphs, and can be generalizable to most MLDM applications. Our evaluation, driven by real-world bipartite gr

关键词： distributed graph processing graph partitioning bipartite graphs

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DQEMU: A Scalable Emulator with Retargetable DBT on distributed Platforms 20

DQEMU: A Scalable Emulator with Retargetable DBT on Distribu...

引用

49th international conference on parallel processing (ICPP)

作者： Zhao, Ziyi Jiang, Zhang Liu, Ximing Gong, Xiaoli Wang, Wenwen Yew, Pen-Chung Nankai Univ Tianjin Peoples R China Univ Georgia Athens GA USA Univ Minnesota Minneapolis MN USA

ISBN: (纸本)9781450388160

The scalability of a dynamic binary translation (DBT) system has become important due to the prevalence of multicore systems and large multi-threaded applications. Several recent efforts have addressed some critical issues in extending a DBT system to run on multicore platforms for better scalability. In this paper, we present a distributed DBT framework, called DQEMU, that goes beyond a single-node multicore processor and can be scaled up to a cluster of multi-node servers. In such a distributed DBT system, we integrate a page-level directory-based data coherence protocol, a hierarchical locking mechanism, a delegation scheme for system calls, and a remote thread migration approach that are effective in reducing its overheads. We also proposed several performance optimization strategies that include page splitting to mitigate false data sharing among nodes, data forwarding for latency hiding, and a hint-based locality-aware scheduling scheme. Comprehensive experiments have been conducted on DQEMU with micro-benchmarks and the PARSEC benchmark suite. The results show that DQEMU can scale beyond a single-node machine with reasonable overheads. For "embarrassingly-parallel" benchmark programs, DQEMU can achieve near-linear speedup when the number of nodes increases - as opposed to flattened out due to lack of computing resources as in current single-node, multi-core version of QEMU.

关键词： Dynamic binary translator distributed system distributed emulator

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Analysis of Smart City Sustainability Based on Big Data Integrated Edge Computing Modeling with Neural Network

Analysis of Smart City Sustainability Based on Big Data Inte...

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Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC), international conference on

作者： Sarah Ali Abdulkareem Zahraa N.Abdulhussain Ragheed Hussam Khaled Farhan Mohammed Al-Farouni Computer Technologies Engineering Al-Turath University College Baghdad Iraq Department of Medical Devices Engineering Technologies National University of Science and Technology Dhi Qar Nasiriyah Iraq Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq Department of Computer Engineering Technologies Al-Kitab University Kirkuk Iraq Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

The fact that urban areas are preferred places to live is evidence of a sophisticated system in which authorities can effectively satisfy citizens' needs and address economic, social, and environmental sustainability issues while facing several obstacles. The smart city idea improves sustainable development, human flourishing, and economic expansion. The smart city concept can potentially enhance a wide range of municipal operations, including transportation, energy, and healthcare, education, and resource management. Many network quality, stability, and security issues have arisen from developing smart cities and IoT applications. The dispersed nature of smart cities and IoT technology has led to problems with service reliability, longevity, and safety. This paper proposes Big Data Integrated Edge Computing Modeling (BDIECM) to improve data management interfaces' security and sustainability elements in various smart city applications to eliminate hazards. Edge computing allows for distributed processing and communication, useful in various contexts, including intelligent transportation systems, health and social services. Edge computing's ability to reduce latency is particularly useful for mission-critical applications. The system coordinates between dispersed edge servers and security mediator nodes for smart city use cases. The simulation results validate the suggested approach can significantly enhance the security ratio of95.2%, sustainability ratio of94.1 %,reliability ratio of 91.4 %, scalability ratio of 93.5 %, latency ratio of 21.1 %, and data transmission ratio of 96.2 % compared to other methods.

关键词： Smart cities Computational modeling Scalability Computer architecture Big Data Data models Security

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Accelerating cryo-EM Reconstruction of RELION on the New Sunway Supercomputer

Accelerating cryo-EM Reconstruction of RELION on the New Sun...

引用

IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Jingle Xu Jiayu Fu Lin Gan Yaojian Chen Zhenchun Huang Guangwen Yang Department of Computer Science and Technology Tsinghua University China National Supercomputing Center in Wuxi China Zhejiang Lab China

Over the long journey of understanding the micro-world, cryo- EM has become an effective technique for biomolec-ular structure determinations. However, due to the complex algorithmic features and large amounts of computing data, so-phisticated HPC solutions are in urgent demand. In this paper, we present our efforts of porting RELION to the new generation of Sunway supercomputer. Optimizations that fit well with the new hardware architecture have been proposed, including a multi-level parallel scheme that smartly maps and scales RELION onto the novel Sun way architecture, optimizations that address memory bottlenecks and improve memory efficiency, and a pipeline approach that obtains excellent computation and communication overlapping. Combining all proposed optimizations, the calculation time for one iteration is greatly reduced from 7,577 seconds to 2,017 seconds, with a speedup of 3.757x. The overall design is scaled to over 131,072 cores with a parallel efficiency of 95 %.

关键词： Three-dimensional displays Scalability Memory management Pipelines parallel processing Supercomputers Biology

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ODCP: Optimizing Data Caching and Placement in distributed File System Using Erasure Coding 17th

ODCP: Optimizing Data Caching and Placement in Distributed F...

引用

17th IFIP WG 10.3 international conference on Network and parallel Computing, NPC 2020

作者： Wu, Shuhan Li, Yunchun Yang, Hailong Luan, Zerong Li, Wei School of Computer Science and Engineering Beihang University Beijing100191 China College of Life Sciences and Bioengineering Beijing University of Technology Beijing100083 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi214125 China

ISBN: (纸本)9783030794774

Many current distributed file systems use erasure-coding based data redundancy techniques to improve the reliability of data storage. Such techniques can significantly improve the effective storage utilization. However, there are several drawbacks to the above techniques. Firstly, they introduce non-negligible computation overhead for decoding. Secondly, traditional data caching and placement strategies become less effective in such cases. To solve the above drawbacks, this paper proposes a new data cache allocation mechanism based on simulated annealing and a new data placement strategy based on convex optimization, which effectively reduces data block transmission delay and decoding delay. We have implemented the proposed data placement strategy in the real-world distributed file system Alluxio, and evaluated the performance of our strategy. Experiment results show that our strategy can significantly reduce the file read delay compared to traditional data placement strategies. © 2021, IFIP international Federation for Information processing.

关键词： Convex optimization

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distributed Top-k Pattern Mining 5th

Distributed Top-k Pattern Mining

引用

5th Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) international Joint conference on Web and Big Data

作者： Wang, Xin Xiang, Mingyue Zhan, Huayi Lan, Zhuo He, Yuang He, Yanxiao Sha, Yuji Southwest Petr Univ Chengdu Peoples R China Southwest Jiaotong Univ Chengdu Peoples R China Sichuan ChangHong Elect Co Ltd Mianyang Sichuan Peoples R China

ISBN: (纸本)9783030858995;9783030858988

Frequent pattern mining (FPM) on a single large graph has been receiving increasing attention since it is crucial to applications in a variety of domains including e.g., social network analysis. The FPM problem is defined as finding all the subgraphs (a.k.a. patterns) that appear frequently in a large graph according to a user-defined frequency threshold. In recent years, a host of techniques have been developed, while most of them suffers from high computational cost and inconvenient result inspection. To tackle the issues, in this paper, we propose an approach tomining top-k patterns from a single graph G under the distributed scenario. We formalize the distributed top-k pattern mining problem by incorporating viable support and interestingness metrics. We then develop a parallel algorithm, that preserves early termination property, to efficiently discover top-k patterns. Using real-life and synthetic graphs, we experimentally verify that our algorithm is rather effective and outperforms traditional counterparts in both efficiency and scalability.

关键词： Data mining

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State of Charge Estimation of Low-speed Electric Vehicle Battery using Back Propagation Neural Network 18

State of Charge Estimation of Low-speed Electric Vehicle Bat...

引用

18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Xu, Menglong Wu, Wenguang Zhou, Wenwen Ma, Yuanyuan Shi, Xiaolei Li, Jun PONOVO Co Ltd Res & Dev Ctr Beijing Peoples R China Changsha Univ Sci & Technol Changsha Peoples R China

ISBN: (纸本)9781665414852

This paper introduces a back propagation (BP) neural network for estimating the state of charge (SoC) of the battery pack in low-speed electric vehicles (LSEV), such as electric bicycles, tricycles, electric motorcycles, and so on. From the perspective of test and maintenance, the SoC estimation with machine learning techniques provides a method of predicting the health status and life-span of the battery in LSEV. Typically, SoC is associated with the factors of open circuit voltage (OCV), charging and discharging current, temperature and charging cycle. Data set collection have been finished by a smart electric bicycle charging platform provided by POWER+WWW. In this paper, the BP neural network theory including the training process is introduced briefly. The improvement of prediction accuracy and the model optimization are discussed. In the end, the experimental results in comparison with the real test value are presented.

关键词： low speed electric vehicle electric bicycle battery pack BP neural network SoC estimation machine learning techniques model optimization

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The Research of parallel Wavelet Transform Algorithms on Remote Sensing Image 5

The Research of Parallel Wavelet Transform Algorithms on Rem...

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5th international conference on Electromechanical Control Technology and Transportation (ICECTT)

作者： Zhang, Shengzhong China Univ Geosci Beijing Informat Network Ctr Beijing 100083 Peoples R China

ISBN: (纸本)9781728199283

Focusing on the Wavelet Transform, the paper explores four parallel Wavelet Transform algorithms and techniques from the perspectives of data parallel and algorithm parallel for remote sensing images. Among them, the algorithm based on "Working Pool parallel" achieves dynamic load balance without any limits to the scale of the data and the number of the Slaves. Therefore, this algorithm is easier to achieve the goal of processing the vast data of remote sensing images rapidly in the distributed network systems.

关键词： Wavelet Transform parallel processing Remote Sensing

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