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IEEE 36th International conference on Data Engineering (ICDE)

作者： Song, Chonggang Lin, Qian Ling, Guohui Zhang, Zongyi Chen, Hongzhao Liao, Jun Chen, Chuan Tencent Inc Shenzhen Peoples R China Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9781728129037

Relationships in online social networks often imply social connections in real life. An accurate understanding of relationship types benefits many applications, e.g. social advertising and recommendation. Some recent attempts have been proposed to classify user relationships into predefined types with the help of pre-labeled relationships or abundant interaction features on relationships. Unfortunately, both relationship feature data and label data are very sparse in real social platforms like WeChat, rendering existing methods inapplicable. In this paper, we present an in-depth analysis of WeChat relationships to identify the major challenges for the relationship classification task. To tackle the challenges, we propose a Local Community-based Edge Classification (LoCEC) framework that classifies user relationships in a social network into real-world social connection types. LoCEC enforces a three-phase processing, namely local community detection, community classification and relationship classification, to address the sparsity issue of relationship features and relationship labels. Moreover, LoCEC is designed to handle large-scale networks by allowing parallel and distributed processing. We conduct extensive experiments on the real-world WeChat network with hundreds of billions of edges to validate the effectiveness and efficiency of LoCEC.

关键词： WeChat edge classification social networks

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EQueue: Elastic Lock-Free FIFO Queue for Core-to-Core Communication on Multi-Core Processors

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IEEE ACCESS 2020年 8卷 98729-98741页

作者： Wang, Junchang Tian, Yangfeng Fu, Xiong Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210003 Peoples R China Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing 210003 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

In recent years, the number of CPU cores in a multi-core processor keeps increasing. To leverage the increasing hardware resource, programmers need to develop parallelized software programs. One promising approach to parallelizing high-performance applications is pipeline parallelism, which divides a task into a serial of subtasks and then maps these subtasks to a group of CPU cores, making the communication scheme between the subtasks running on different cores a critical component for the parallelized programs. One widely-used implementation of the communication scheme is software-based, lock-free first-in-first-out queues that move data between different subtasks. The primary design goal of the prior lock-free queues was higher throughput, such that the technique of batching data was heavily used in their enqueue and dequeue operations. Unfortunately, a lock-free queue with batching heavily depends on the assumption that data arrive at a constant rate, and the queue is in an equilibrium state. Experimentally we found that the equilibrium state of a queue rarely happens in real, high-performance use cases (e.g., 10Gbps & x002B;network applications) because data arriving rate fluctuates sharply. As a result, existing queues suffer from performance degradation when used in real applications on multi-core processors. In this paper, we present EQueue, a lock-free queue to handle this robustness issue in existing queues. EQueue is lock-free, efficient, and robust. EQueue can adaptively (1) shrink its queue size when data arriving rate is low to keep its memory footprint small to utilize CPU cache better, and (2) enlarge its queue size to avoid overflow when data arriving rate is in burstiness. Experimental results show that when used in high-performance applications, EQueue can always perform an enqueue/dequeue operation in less than 50 CPU cycles, which outperforms FastForward and MCRingBuffer, two state-of-the-art queues, by factors 3 and 2, respectively.

关键词： parallel processing Task analysis Multicore processing Pipelines Robustness Throughput Licenses Lock-free queue pipeline parallelism multi-core processors

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Scale Drone Mapping on K8S: Auto-scale Drone Imagery processing on Kubernetes-orchestrated On-premise Cloud-computing Platform 6

Scale Drone Mapping on K8S: Auto-scale Drone Imagery Process...

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6th International conference on Geographical Information Systems Theory, Applications and Management (GISTAM)

作者： Vithlani, Hemang Narendra Dogotari, Marcel Lam, Olee Hoi Ying Pruem, Moritz Melville, Bethany Zimmer, Frank Becker, Rolf Rhine Waal Univ Appl Sci Fac Commun & Environm D-47475 Kamp Lintfort Germany Astron Environm Serv 129 Royal St East Perth WA 6004 Australia

ISBN: (纸本)9789897584251

Aerial images acquired using drone-based imaging sensors can be processed by photogrammetry toolkits to create geometrically corrected 2D orthophoto and/or 3D models. This is a crucial step for many of the ever-evolving civil applications of drones such as precision agriculture and surveying. Nevertheless, limited computational resources become bottleneck in providing these results quickly. Cloud computing helps in such scenarios because of its value-added features, namely virtualization, elasticity, high performance and distributed computing for the web-based image processing. The containerization approach plays a vital role in cloud computing by providing operational efficiency. Container orchestration engine, Kubernetes, not only provides template-based or GUI-based service deployment but also better monitoring, log querying and autoscaling. The present work displays a scalable photogrammetry service, deployed on a Kubernetes-orchestrated on-premise cluster. This reference implementation on Kubernetes enables the parallel processing of large datasets in less time than a single computer using the free and open-source toolkit OpenDroneMap.

关键词： Drone Imagery Opendronemap Photogrammetry Open-source Kubernetes Cloud Computing

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Accelerating pagerank in shared-memory for efficient social network graph analytics 26

Accelerating pagerank in shared-memory for efficient social ...

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26th IEEE International conference on parallel and distributed Systems, ICPADS 2020

作者： Huang, Baofu Liu, Zhidan Wu, Kaishun College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781728190747

PageRank has a wide applications in online social networks and serves as an important benchmark to examine graph processing frameworks. Many efforts have been made to improve the computation efficiency of PageRank in shared-memory platforms, where a single machine can be sufficiently powerful to handle a large-scale graph. Existing methods, however, still suffer from synchronization issues and irregular memory accesses, which will deteriorate their overall performance. In this paper, we present an accelerated parallel PageRank computation approach, named APPR. By investigating the characteristics of parallel PageRank computation and degree distributions of social network graphs, APPR proposes a series of optimization techniques to improve the efficiency of PageRank computation. Specifically, a destination-centric graph partitioning scheme is designed to avoid the synchronization issues when concurrently updating the common vertex data. By exploiting power-law structure of social network graphs, APPR can intelligently schedule the computations of vertices to save computing operations. The vertex messages are adjusted by APPR for transmission to further improve the locality of memory accesses. Empirical evaluations are performed based on a set of large real-world graphs. Experimental results show that APPR significantly outperforms the state-of-the-art methods, with on average 2.4x speedup in execution time and 16.4x reduction in DRAM communication. © 2020 IEEE.

关键词： Social networking (online)

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Efficient Graph Query processing over Geo-distributed Datacenters 20

Efficient Graph Query Processing over Geo-Distributed Datace...

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43rd International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR)

作者： Yuan, Ye Ma, Delong Wen, Zhenyu Ma, Yuliang Wang, Guoren Chen, Lei Beijing Inst Technol Beijing Peoples R China Northeastern Univ Shenyang Peoples R China Newcastle Univ Newcastle Upon Tyne Tyne & Wear England Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781450380164

Graph queries have emerged as one of the fundamental techniques to support modern search services, such as PageRank web search, social networking search and knowledge graph search. As such graphs are maintained globally and very huge (e.g., billions of nodes), we need to efficiently process graph queries across multiple geographically distributed datacenters, running geo-distributed graph queries. Existing graph computing frameworks may not work well for geographically distributed datacenters, because they implement a Bulk Synchronous parallel model that requires excessive inter-datacenter transfers, thereby introducing extremely large latency for query processing. In this paper, we propose GeoGraph-a universal framework to support efficient geo-distributed graph query processing based on clustering datacenters and meta-graph, while reducing the inter-datacenter communication. Our new framework can be applied to many types of graph algorithms without any modification. The framework is developed on the top of Apache Giraph. The experiments were conducted by applying four important graph queries, i.e., shortest path, graph keyword search, subgraph isomorphism and PageRank. The evaluation results show that our proposed framework can achieve up to 82% faster convergence, 42% lower WAN bandwidth usage, and 45% less total monetary cost for the four graph queries, with input graphs stored across ten geo-distributed datacenters.

关键词： Graph search Geo-distributed Datacenters

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Co-designing the Topology/Algorithm to Accelerate distributed Training

Co-designing the Topology/Algorithm to Accelerate Distribute...

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

作者： Xiang Hou Rui Xu Sheng Ma Qiong Wang Wei Jiang Hongyi Lu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

With the development of Deep Learning (DL), Deep Neural network (DNN) models have become more complex. At the same time, the development of the Internet makes it easy to obtain large data sets for DL training. Large-scale model parameters and training data enhance the level of AI by improving the accuracy of DNN models. But on the other hand, they also present more severe challenges to the hardware training platform because training a large model needs a lot of computing and memory resources that can easily exceed the capacity of a single processor. In this context, integrating more processors on a hierarchical system to conduct distributed training is a direction for the development of training platforms. In distributed training, collective communication operations (including all-to-all, all-reduce, and all-gather) take up a lot of training time, making the interconnection network between computing nodes one of the most critical factors affecting the system performance. The hierarchical torus topology, combined with the Ring All-Reduce collective communication algorithm, is one of the current mainstream distributed interconnection networks. However, we believe that its communication performance is not the best. In this work, we first designed a new intra-package communication topology, i.e. the switch-based fully connected topology, which shortens the time consumed by cross-node communication. Then, considering the characteristics of this topology, we carefully devised more efficient all-reduce and all-gather communication algorithms. Finally, combined with the torus topology, we implemented a novel distributed DL training platform. Compared with the hierarchical torus, our platform improves communication efficiency and provides 1.16-2.68 times speedup in distributed training of DNN models.

关键词： Training Deep learning network topology Multiprocessor interconnection Computational modeling Training data Switches

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Rammed, or What RAM³S Taught Us 22

Rammed, or What RAM<SUP>3</SUP>S Taught Us

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22nd Annual International conference on Information Integration and Web-based Applications and Services (IIWAS)

作者： Bartolini, Ilaria Patella, Marco Univ Bologna Alma Mater Studiorum Dept Comp Sci & Engn DISI Bologna Italy

ISBN: (纸本)9781450389228

RAM(3)S (Real-time Analysis of Massive MultiMedia Streams) is a framework that acts as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, so as to facilitate the implementation of the former on top of the latter. Indeed, the use of Big Data platforms can give way to the efficient management and analysis of large data amounts, but they require the user to concentrate on issues related to distributed computing, since their services are often too raw. The use of RAM(3)S greatly simplifies deploying non-parallel techniques to platforms like Apache Storm or Apache Flink, a fact that is demonstrated by the four different use cases we describe here. We detail the lessons we learned from exploiting RAM(3)S to implement the detailed use cases.

关键词： Stream processing Real-Time Analysis Big Data

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Fden: Mining Effective Information of Features in Detecting network Anomalies

Fden: Mining Effective Information of Features in Detecting ...

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IEEE International conference on Acoustics, Speech and Signal processing

作者： Bin Li Yijie Wang Mingyu Liu Kele Xu Zhongyang Wang Li Cheng Yizhou Li Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

network anomaly detection is important for detecting and reacting to the presence of network attacks. In this paper, we propose a novel method to effectively leverage the features in detecting network anomalies, named FDEn, consisting of flow-based Feature Derivation (FD) and prior knowledge incorporated Ensemble models (En pk). To mine the effective information in features, 149 features are derived to enrich the feature set of the original data with covering more characteristics of network traffic. To leverage these features effectively, an ensemble model En pk, including CatBoost and XGBoost, based on the bagging strategy is proposed to first detect anomalies by combining numerical features and categorical features. And then, En pk adjusts the predicted label of specific data by incorporating the prior knowledge of network security. We conduct empirically experiments on the data set provided by the network Anomaly Detection Challenge (NADC), in which we obtain average improvement up to 61.6%, 31.7%, 50.2%, and 45.0%, in terms of the cost score, precision, recall and F1-score, respectively.

关键词： Knowledge engineering conferences Telecommunication traffic Signal processing Feature extraction Numerical models Security

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Decreasing the Learning Cost of Offline parallel Application Optimization Strategies

Decreasing the Learning Cost of Offline Parallel Application...

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euromicro conference on parallel, distributed and network-based processing

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

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Many parallel applications do not scale as the number of threads increases, which means that executing them with the maximum possible number of threads will not always deliver the best outcome in performance, energy consumption, or the tradeoff between both (represented by the energy-delay product- EDP). Given that, several strategies, online and offline, have already been proposed to rightly tune the number of threads according to the application. While the former can capture some behaviors that can only be known at runtime, the latter do not impose any execution overhead and can use more efficient and costly algorithms. However, these learning algorithms in static strategics may take several hours, precluding their use or a smooth migration across different systems. In this scenario, we propose a generic methodology for such offline strategies to significantly decrease the learning time by inferring the execution behavior of parallel applications using smaller input sets than the ones used by the target applications. Through the execution of eighteen well-known benchmarks on two multicore processors, we show that our methodology is capable of converging to results that are very close to those that use the regular input set, but converging 84.7% faster, on average. We also show that such a strategy delivers better results than a dynamic one, presenting an EDP 7.7% lower, on average, when executing the applications with the number of threads found during learning. Finally, we also compare our learning methodology with an exhaustive search. It has an average learning cost (i.e., the time spent by our search algorithm to find the best configuration) of only 3.1% to optimize the EDP of the entire benchmark set 1 .

关键词： Instruction sets Benchmark testing Predictive models Energy consumption Runtime parallel processing

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Research on Design of High-Performance Graph Computing System based on CPU Heterogeneous Architecture

Research on Design of High-Performance Graph Computing Syste...

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Electronic Information Technology and Smart Agriculture (ICEITSA), International conference on

作者： Yanjiang Li King's College London London UK

ISBN: (纸本)9781665413015

High performance computing is also known as supercomputing, mainly refers to more data in a short time to help the computer system to complete more computing tasks. Cloud computing, as a parallel distributed computing application concept proposed by the current technological innovation, will use the network to connect a large number of computing resources in practical application, thus forming a computing resource pool with stronger computing capacity and parallel processing capacity, so as to bring more superior computing services to users. From the perspective of overall development, cloud computing, as a new processing mode proposed to achieve high performance computing, is very novel in both application scope and promotion form, which plays a positive role in the future research of computer technology. Therefore, based on the understanding of high performance computing research significance and the central processing unit (CPU) implementation on the basis of the advantages of high performance computing, start from a computer unified device architecture (CUDA) for fast image processing system, in-depth studies are proving CPU heterogeneous architecture as the core of high performance computing system has a certain application advantages.

关键词： Technological innovation Cloud computing High performance computing Image processing Graphics processing units Weather forecasting Computer architecture

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