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A distributed Path Query Engine for Temporal Property Graphs 20

A Distributed Path Query Engine for Temporal Property Graphs

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20th IEEE/ACM international symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Ramesh, Shriram Baranawal, Animesh Simmhan, Yogesh Indian Inst Sci Dept Computat & Data Sci Bangalore 560012 Karnataka India

ISBN: (纸本)9781728160955

Property graphs are a common form of linked data, with path queries used to traverse and explore them for enterprise transactions and mining. Temporal property graphs are a recent variant where time is a first-class entity to be queried over, and their properties and structure vary over time. These are seen in social, telecom and transit networks. However, current graph databases and query engines have limited support for temporal relations among graph entities, no support for time-varying entities and/or do not scale on distributed resources. We address this gap by extending a linear path query model over property graphs to include intuitive temporal predicates that operate over temporal graphs. We design a distributed execution model for these temporal path queries using the interval-centric computing model, and develop a novel cost model to select an efficient execution plan from several. We perform detailed experiments of our Granite distributed query engine using temporal property graphs as large as 52M vertices, 218M edges and 118M properties, and an 800-query workload, derived from the LDBC benchmark. We offer sub-second query latencies in most cases, which is 149x-1140x faster compared to industry-leading Neo4J shared-memory graph database and the JanusGraph/Spark distributed graph query engine. Further, our cost model selects a query plan that is within 10% of the optimal execution time in 90% of the cases. We also scale well, and complete 100% of the queries for all graphs, compared to only 32-92% by baseline systems.

关键词： Graph databases

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AQUILA: Adaptive parallel Computation of Graph Connectivity Queries 20

AQUILA: Adaptive Parallel Computation of Graph Connectivity ...

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29th international Conference on High-Performance parallel and distributed Computing (HPDC)

作者： Ji, Yuede Huang, H. Howie George Washington Univ Graph Comp Lab Washington DC 20052 USA

ISBN: (纸本)9781450370523

Graph connectivity algorithms answer whether two nodes in a graph are connected under specific conditions, which are beneficial to a number of applications, such as pattern recognition and cybersecurity. Unfortunately, existing graph computing frameworks support only a small number of connectivity algorithms and achieve low computation parallelism. In this paper, we have designed an adaptive parallel computation framework, AQILA, that covers a wide range of different highly optimized graph connectivity algorithms. Given a graph, AQILA first transforms the query if it can be answered with partial computation. During the computation, AQILA is able to greatly reduce the workload by up to 98%. Furthermore, AQILA identifies the irregular tasks in the connectivity algorithms and applies different parallel strategies for different tasks. As a result, AQILA significantly outperforms existing systems such as Multistep, Galois, Ligra, GraphChi, X-Stream, DFS, and Boost, by average 13x, 53x, 264x, 364x, 1, 369x, 45x, and 255x, respectively.

关键词： graph connectivity parallel computation

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EXPPO: EXecution Performance Profiling and Optimization for CPS Co-simulation-as-a-Service 23

EXPPO: EXecution Performance Profiling and Optimization for ...

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IEEE 23rd international symposium on Real-Time distributed Computing (ISORC)

作者： Barve, Yogesh D. Neema, Himanshu Kang, Zhuangwei Sun, Hongyang Gokhale, Aniruddha Roth, Thomas Vanderbilt Univ Dept Elect Engn & Comp Sci Nashville TN 37212 USA NIST Smart Grid & Cyber Phys Syst Program Off Engn Lab Gaithersburg MD 20899 USA

ISBN: (纸本)9781728169583

A co-simulation may comprise several heterogeneous federates with diverse spatial and temporal execution characteristics. In an iterative time-stepped simulation, a federation exhibits the Bulk Synchronous parallel (BSP) computation paradigm in which all federates perform local operations and synchronize with their peers before proceeding to the next round of computation. In this context, the lowest performing (i.e., slowest) federate dictates the progression of the federation logical time. One challenge in co-simulation is performance profiling for individual federates and entire federations. The computational resource assignment to the federates can have a large impact on federation performance. Furthermore, a federation may comprise federates located on different physical machines as is the case for cloud and edge computing environments. As such, distributed profiling and resource assignment to the federation is a major challenge for operationalizing the co-simulation execution at scale. This paper presents the Execution Performance Profiling and Optimization (EXPPO) methodology, which addresses these challenges by using execution performance profiling at each simulation execution step and for every federate in a federation. EXPPO uses profiling to learn performance models for each federate, and uses these models in its federation resource recommendation tool to solve an optimization problem that improves the execution performance of the co-simulation. Using an experimental testbed, the efficacy of EXPPO is validated to show the benefits of performance profiling and resource assignment in improving the execution runtimes of co-simulations while also minimizing the execution cost.

关键词： cyber-physical systems distributed simulation cloud computing latency performance resource management gang scheduling

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Tiled algorithms for efficient task-parallel ℌ-matrix solvers 34

Tiled algorithms for efficient task-parallel ℌ-matrix solve...

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34th IEEE international parallel and distributed Processing symposium Workshops, IPDPSW 2020

作者： Carratala-Saez, Rocio Faverge, Mathieu Pichon, Gregoire Sylvand, Guillaume Quintana-Orti, Enrique S. Universitat Jaume i Castelló de la Plana Spain Univ. Bordeaux Bordeaux INP CNRS Inria Talence France Univ Lyon EnsL UCBL CNRS Inria LIP Lyon France Airbus Central RT / Inria Bordeaux France Universitat Politècnica de València València Spain

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174457

In this paper, we describe and evaluate an extension of the CHAMELEON library to operate with hierarchical matrices (H-Matrices) and hierarchical arithmetic (H-Arithmetic), producing efficient solvers for linear systems arising in Boundary Element Methods (BEM). Our approach builds upon an open-source H-Matrices library from Airbus, named HMAT-OSS, that collects sequential numerical kernels for both hierarchical and low-rank structures;the tiled algorithms and task-parallel decompositions available in CHAMELEON for the solution of linear systems;and the STARPU runtime system to orchestrate an efficient task-parallel (multi-threaded) execution on a multicore architecture. Using an application producing matrices with features close to real industrial applications, we present shared-memory results that demonstrate a fair level of performance, close to (and sometimes better than) the one offered by a pure H-Matrix approach, as proposed by Airbus HMAT proprietary (and non open-source) library. Hence, this combination CHAMELEON + HMAT-oss proposes the most efficient fully open-source software stack to solve dense compressible linear systems on shared memory architectures (distributed memory is under development). © 2020 IEEE.

关键词： Linear systems

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Remote Procedure Call Optimization of Big Data systems Based on Data Awareness 18

Remote Procedure Call Optimization of Big Data Systems Based...

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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： Wang, Jin Yang, Yaqiong Zhang, Jingyu Wang, Lei Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha Peoples R China Fujian Univ Technol Sch Informat Sci & Engn Fujian Peoples R China Natl Univ Def Technol Sch Syst Engn Changsha Peoples R China Changsha Univ Sci & Technol Sch Civil Engn Changsha Peoples R China

ISBN: (纸本)9781665414852

In recent years, with the development of artificial intelligence technologies, mobile smart terminals, and high-bandwidth wireless internet, the scale of data generated by various data sources has continued to expand. Huge amounts of data contain huge economic value, therefore how to store and process the data efficiently becomes very significant. HDFS (Hadoop distributed File System) has emerged as a typical representative of data-intensive distributed file systems, and it has features such as high fault tolerance, high throughput, and can be deployed on low-cost hardwares. However, the remote procedure call in HDFS is still not good enough to work better in terms of network throughput and abnormal response. This paper presents an optimization method to improve the performance of HDFS. The proposed method dynamically adjusts the RPC (Remote Procedure Call) configurations between NameNode and Datallodes by sensing the data characters that stored in Datallodes. This method can effectively reduce the processing pressure of the NameNode and reduce the network throughput generated by the information transmission between NameNode and Datallodes. It can also reduce the abnormal response time of the whole system. Finally, the extensive experiments show the effectiveness and efficiency of our proposed method.

关键词： distributed file system HDFS information interaction adaptive RPC mechanism

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Campus safety monitoring system based on deep learning

Campus safety monitoring system based on deep learning

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international symposium on distributed Computing and Applications to Business, Engineering and Science (DCABES)

作者： Qianying Zhang Jin Hang Boxuan Zhang Mengzhong Wei Quanyin Zhu Xiang Li Faculty of Computer & Software Engineering Huaiyin Institute of Technology Huai'an China

ISBN: (纸本)9781665428903

In view of the safety hazards existing on campus, a campus safety detection system based on deep learning behavior recognition, license plate recognition and speed detection is proposed and designed. The design divides the campus safety monitoring system into three functional modules: hazard behavior identification, license plate tracking and vehicle speed measurement, and on this basis, designs the database of storage information and the warning system of speed too fast to realize the monitoring function of campus safety.

关键词： Deep learning databases Licenses Alarm systems Hazards Velocity measurement Optical character recognition software

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Proceedings - 2020 IEEE international symposium on parallel and distributed Processing with Applications, 2020 IEEE international Conference on Big Data and Cloud Computing, 2020 IEEE international symposium on Social Computing and Networking and 2020 IEEE international Conference on Sustainable Computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020

Proceedings - 2020 IEEE International Symposium on Parallel ...

引用

18th IEEE international symposium on parallel and distributed Processing with Applications, 10th IEEE international Conference on Big Data and Cloud Computing, 13th IEEE international symposium on Social Computing and Networking and 10th IEEE international Conference on Sustainable Computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020

ISBN: (纸本)9781665414852

The proceedings contain 202 papers. The topics discussed include: multi-relational graph convolutional networks for skeleton-based action recognition;identifying the right person in social networks with double metaphone codes;a novel strong cache consistency mechanism in ICN based on role division and lease model;demystifying the largest live game streaming platform via black-box measurement;framework-agnostic optimization of repeated skewed joins at massive scale;remote procedure call optimization of big data systems based on data awareness;MCSEC: secure coded matrix multiplication scheme for edge computing with minimum communication cost;low-rate DoS attack detection based on WPD-EE algorithm;user identity linkage across location-based social networks with spatio-temporal check-in patterns;optimizing 3-D placement of multiple UAVs based on Taguchiâ€™s method;and feature envy detection based on Bi-LSTM with self-attention mechanism.

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Planning Paths for UAVs to Collect Data from Disconnected Sensor Networks

Planning Paths for UAVs to Collect Data from Disconnected Se...

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international Conference on parallel and distributed systems (ICPADS)

作者： Xuemeng Li Xiaojun Zhu Chao Dong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Laboratory of Novel Software Technology Nanjing University China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics China

ISBN: (纸本)9781665408790

In case of energy depletion at some nodes, a sensor network may become disconnected, and data from live nodes may not be sent to the sink. This problem can be solved by a UAV acting as a mobile data collector. However, the traditional approach is to treat live sensor nodes as independent data sources. In this paper, we consider a cooperative approach where live sensors form sub-networks, instead of acting indepen-dently. Sensors within a subnetwork can send data to each other. A UAV only needs to visit subnetworks, instead of individual sensor nodes, to collect data. We formulate the problem and propose an iterative optimization algorithm to find the shortest data collection trajectory. Given any trajectory, our algorithm iteratively merges nodes within the same subnetwork and adjusts hovering locations to minimize trajectory length. Simulations show that, compared with the traditional TSP formulation, our approach can significantly reduces the length of the trajectory.

关键词： Soft sensors Conferences distributed databases Data collection Iterative algorithms Data models Trajectory

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Current Ripple Compensation Algorithm for paralleled Three-Phase Three-Wire Hybrid Frequency Inverter systems

Current Ripple Compensation Algorithm for Paralleled Three-P...

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IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Tsai-Fu Wu Temir Sakavov Yen-Hsiang Huang Elegant Power Electronics Applied Research Laboratory National Tsing Hua University Hsinchu Taiwan

This paper presents a current ripple compensation algorithm for paralleled three-phase three-wire (3P3W) hybrid frequency inverter systems (HbFIS) which are feasible for high power applications when using hybrid low- and high-speed switching devices. An HbFIS consists of two inverters connected in parallel, in which one is a low-frequency-high-power inverter (LFHPI), the other is a high-frequency-low-power inverter (HFLPI). Using direct digital control for HFLPI together with compensating the current ripple caused by LFHPI, the HbFIS can reduce the amplitude of its overall output current ripples. An HbFIS supplied by the shared dc voltage source is studied based on the proposed ripple compensation algorithm. Simulated results have verified the compensation algorithm with reduced THD at its output currents.

关键词： Digital control Process control Switches Inverters Hybrid power systems Topology distributed power generation

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Combining Thread Throttling and Mapping to Optimize the EDP of parallel Applications

Combining Thread Throttling and Mapping to Optimize the EDP ...

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Euromicro Conference on parallel, distributed and Network-Based Processing

作者： Gustavo P. Berned Thiarles S. Medeiros Matheus Serpa Fábio D. Rossi Marcelo C. Luizelli Philippe O. Navaux Antonio Carlos S. Beck Arthur F. Lorenzon Federal University of Pampa - Campus Alegrete Brazil Institute of Informatics - Federal University of Rio Grande do Sul Brazil Science and Technology Farroupilha Federal Institute of Education Brazil

Thread-throttling and mapping strategies have been used together to make better use of hardware resources and improve the energy-delay product (EDP) of high-performance computing (HPC) systems. However, the design space exploration significantly grows with the increasing number of cores in those systems, making the task of finding the ideal number of active threads and allocating strategy a challenging task. On top of that, parallel applications present various patterns, such as irregularity, unbalanced computations, or high rates of communications. Given these considerations, we propose ETTM, an EDPaware thread-throttling and mapping optimization strategy that automatically finds an ideal combination of number of threads and thread mapping strategy. With the execution of eighteen well-known benchmarks on three multicore architectures, we show that EDP can be significantly improved when running applications with the solution found by EETM¹.

关键词： Multicore processing Instruction sets distributed databases Computer architecture Benchmark testing Hardware Space exploration

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