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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

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Practicability of embarrassingly parallel computations for enormous miniature workloads over massive underutilized IoT 5

Practicability of embarrassingly parallel computations for e...

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5th IEEE international Women in Engineering (WIE) conference on Electrical and Computer Engineering (IEEE WIECON-ECE)

作者： Tiruvayipati, Sujanavan Yellasiri, Ramadevi Maturi Venkata Subba Rao MVSR Engn Coll Dept Comp Sci & Engn Hyderabad India Chaitanya Bharathi Inst Technol Dept Comp Sci & Engn Hyderabad India

ISBN: (纸本)9781728144993

The Internet of Things (IoT) will grow seamlessly with advancements in data and communication technologies leading to the deploy meat of trillions of end devices. Its application starts with a simple home automation to a very large scale industrial automation system. The trend is leading towards huge data generation requiring high processing power. In the near future, computing resources might not be sufficient for handling dynamic humongous data production. As the technology advances, microcontrollers or System-on-Chips (SoCs) used for IoT end devices are becoming cheaper and more powerful. Hence, there is a requirement of effectively making use of huge number of underutilized IoT of the future by allocating additional microtasks in parallel which would solve the upcoming needs of the technological trend.

关键词： IoT parallel computing Multi-Threaded Microcontroller End-Device Processing Massive Data Management

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A social link based private storage cloud

A social link based private storage cloud

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international conference on parallel and distributed Systems (ICPADS)

作者： Michalis Konstantopoulos Nikos Chondros Mema Roussopoulos University of Athens Greece

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

In this paper, we present O 3 , a social link based private storage cloud for decentralized collaboration. O 3 allows users to share and collaborate on collections of files (shared folders) with others, in a decentralized manner, without the need for intermediaries (such as public cloud storage servers) to intervene. Thus, users can keep their working relationships (who they work with) and what they work on private from third parties. Using benchmarks and traces from real workloads, we experimentally evaluate O 3 and demonstrate that the system scales linearly when synchronizing increasing numbers of concurrent users, while performing on-par with the ext4 non-version-tracking filesystem.

关键词： Cloud computing Protocols Social networking (online) Web and internet services Collaboration Servers Multimedia communication

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A parallel uncertain frequent itemset mining algorithm with spark 20

A parallel uncertain frequent itemset mining algorithm with ...

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20th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2019

作者： Ding, Jiaman Li, Haibin Wang, Yuanyuan Jia, Lianyin You, Jinguo Yang, Yang Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China College of Engineering and Computer Science Australian National University Canberra Australia

ISBN: (纸本)9781728126166

Frequent Itemset Mining (FIM) from large-scale databases has emerged as an important problem in the data mining and knowledge discovery research community. However, FIM suffers from three important limitations with the rapidly expanding of big data in all domains. First, it assumes that all items have the same importance. Second, it ignores the fact that data collected in a real-life environment is often inaccurate. Third, it is also a data-intensive and computation-intensive process which makes the FIM algorithm very time-consuming over large datasets. To address these issues, we propose a parallel uncertain frequent itemset mining algorithm with spark (Pufim). Pufim firstly expresses item uncertainty by considering both the probability and weight, and calculates the maximum probability weight value of 1-items. Next, a distributed Pufim-tree structure is designed inspiring by FP-Tree for reducing the times of scanning the databases. Each node of Pufim-tree stores an item and its maximum probability weight value. Finally, experiments on publicly available UCI datasets demonstrate that Pufim achieves more prominent results than other related approaches across various metrics. In addition, the empirical study also shows Pufim has a good scalability. © 2019 IEEE.

关键词： Data mining

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Improving recommender systems accuracy in social networks using popularity 20

Improving recommender systems accuracy in social networks us...

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20th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2019

作者： Yazdi, Kasra Majbouri Yazdi, Adel Majbouri Khodayi, Saeed Hou, Jingyu Zhou, Wanlei Saedy, Saeid Rostami, Mehrdad School of Information Technology Deakin University Melbourne Australia Department of Computing Kharazmi University Tehran Iran Faculty of Computer and Electrical Engineering Azad University Qazvin Iran School of Software University of Technology Sydney Sydney Australia Faculty of Engineering Khavaran Higher Education Institute Mashad Iran Faculty of Computer Engineering University of Kurdistan Sanandaj Iran

ISBN: (纸本)9781728126166

With the rapid advancement of World Wide Web, people can share their knowledge and information via online tools such as sharing systems and ecommerce applications. Many approaches have been proposed to process and organize information. Recommender systems are good successful examples of such tools in providing personalized suggestions. The main purpose of a recommender system is to identify and introduce desired items of a user among many other options (e.g. music, movies, books, news and etc). The goal of our proposed method is to provide a recommender system based on information diffusion and popularity in social networks. By adding popularity, similarity and users' trusts a more efficient system is proposed. This approach makes an improvement in tackling the issues and defects of the previous methods such as prediction accuracy and coverage. The evaluation of the simulated proposed method on MovieLens and Epinions datasets shows that it provides more accurate recommendations in comparison to other approaches. © 2019 IEEE.

关键词： Recommender systems

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FlowCon: Elastic Flow Configuration for Containerized Deep Learning Applications 19

FlowCon: Elastic Flow Configuration for Containerized Deep L...

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48th international conference on parallel Processing (ICPP)

作者： Zheng, Wenjia Tynes, Michael Gorelick, Henry Mao, Ying Cheng, Long Hou, Yantian Fordham Univ Bronx NY 10458 USA Univ Coll Dublin Dublin Ireland Boise State Univ Boise ID 83725 USA

ISBN: (纸本)9781450362955

An increasing number of companies are using data analytics to improve their products, services, and business processes. However, learning knowledge effectively from massive data sets always involves nontrivial computational resources. Most businesses thus choose to migrate their hardware needs to a remote cluster computing service (e.g., AWS) or to an in-house cluster facility which is often run at its resource capacity. In such scenarios, where jobs compete for available resources utilizing resources effectively to achieve high-performance data analytics becomes desirable. Although cluster resource management is a fruitful research area having made many advances (e.g., YARN, Kubernetes), few projects have investigated how further optimizations can be made specifically for training multiple machine learning (ML) / deep learning (DL) models. In this work, we introduce FlowCon, a system which is able to monitor loss functions of ML/DL jobs at runtime, and thus to make decisions on resource configuration elastically. We present a detailed design and implementation of FlowCon, and conduct intensive experiments over various DL models. Our experimental results show that FlowCon can strongly improve DL job completion time and resource utilization efficiency, compared to existing approaches. Specifically, FlowCon can reduce the completion time by up to 42.06% for a specific job without sacrificing the overall makespan, in the presence of various DL job workloads.

关键词： cloud computing deep learning containerized application resource management high performance analytics

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Fully distributed Bayesian Optimization with Stochastic Policies 28

Fully Distributed Bayesian Optimization with Stochastic Poli...

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28th international Joint conference on Artificial Intelligence

作者： Garcia-Barcos, Javier Martinez-Cantin, Ruben Univ Zaragoza Inst Invest Ingn Aragon Zaragoza Spain Ctr Univ Def Zaragoza Spain

ISBN: (纸本)9780999241141

Bayesian optimization has become a popular method for high-throughput computing, like the design of computer experiments or hyperparameter tuning of expensive models, where sample efficiency is mandatory. In these applications, distributed and scalable architectures are a necessity. However, Bayesian optimization is mostly sequential. Even parallel variants require certain computations between samples, limiting the parallelization bandwidth. Thompson sampling has been previously applied for distributed Bayesian optimization. But, when compared with other acquisition functions in the sequential setting, Thompson sampling is known to perform suboptimally. In this paper, we present a new method for fully distributed Bayesian optimization, which can be combined with any acquisition function. Our approach considers Bayesian optimization as a partially observable Markov decision process. In this context, stochastic policies, such as the Boltzmann policy, have some interesting properties which can also be studied for Bayesian optimization. Furthermore, the Boltzmann policy trivially allows a distributed Bayesian optimization implementation with high level of parallelism and scalability. We present results in several benchmarks and applications that show the performance of our method.

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parallel Approach for Sub-graph Isomorphism on Multicore System Using OpenMP 2nd

Parallel Approach for Sub-graph Isomorphism on Multicore Sys...

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2nd international conference on Smart Innovations in Communications and Computational Sciences ICSICCS-2018

作者： Somkunwar, Rachna Vaze, Vinod M. Shri Jagdishprasad Jhabarmal Tibrewala University JhunjhunuRajasthan India

ISBN: (纸本)9789811324130

Sub-graph can be used to recognize functional and non-functional characteristics in various graph applications. The sub-graph isomorphism is the problem of detection of input graph inside the target graph. However, if the size of graph grows exponentially, the only available solution to this problem is to use parallel or distributed system. This paper presents parallel approach for sub-graph isomorphism on multi-core system using OpenMP. OpenMP and MPI are application programming interfaces used for multi-core system. OpenMP is used for shared memory architecture. In this work, we parallelize the algorithm to improve the performance of the system using different ways: Grouping of similar nodes, reducing the size of groups and finding the path of nodes. The experimental results show that the proposed approach brings the advantage of high-performance parallel hardware system than single CPU-based results. This approach is highly efficient for the large graphs and also for different variety of graphs. This paper extends the work of COPG algorithm by adding the parallelization method. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Application programming interfaces (API)

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distributed computing method for Synthetic Aperture Radar Compressed Sensing Imaging based on MapReduce

Distributed Computing method for Synthetic Aperture Radar Co...

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IEEE international conference on Electronic Information and Communication Technology (ICEICT)

作者： Can Zheng Kefei Liao Shan Ouyan Changshu Li State and Local Joint Engineering Research Center for Satellite Navigation and Location Service Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

When using the compressed sensing method in Synthetic Aperture Radar(SAR) imaging, there are two major problems: long calculation time and insufficient scalability of t calculation ability. In order to solve the above problems, this paper proposes a distributed imaging method for SAR compressed sensing imaging based on MapReduce. First, the sparse data is labeled, then the range and azimuth image are reconstructed by two MapReduce calculation processes. With parallel computing advantages, the acceleration of SAR compressed sensing imaging is realized.

关键词： Imaging Radar imaging Apertures Sensors Acceleration Compressed sensing Synthetic aperture radar

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MULTI-LEVEL LOAD BALANCING FOR parallel PARTICLE SIMULATIONS 6

MULTI-LEVEL LOAD BALANCING FOR PARALLEL PARTICLE SIMULATIONS

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6th international conference on Particle-Based Methods (PARTICLES) - Fundamentals and Applications

作者： Sutmann, Godehard Forschungszentrum Julich JSC Julich Supercomp Ctr JSC Inst Adv Simulat IAS D-52425 Julich Germany Ruhr Univ Bochum ICAMS D-44801 Bochum Germany

ISBN: (纸本)9788412110111

Ideas from multi-level relaxation methods are combined with load balancing techniques to achieve a convergence acceleration for a homogeneous work load distribution over a given set of processors when the underlying work function is inhomogeneously distributed in space. The algorithm is based on an orthogonal recursive bisection approach which is evaluated via a hierarchically refined coarse integration. The method only requires a minimal information transfer across processors during the tree traversal steps. It is described of how to partition the system of processors to geometrical space, when global information is needed for the spatial tesselation.

关键词： parallel computing Particle Methods Load Balancing Multi-Level Methods Multigrid

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T-MPP: A Novel Topic-Driven Meta-path-Based Approach for Co-authorship Prediction in Large-Scale Content-Based Heterogeneous Bibliographic Network in distributed computing Framework by Spark

T-MPP: A Novel Topic-Driven Meta-path-Based Approach for Co-...

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1st international conference on Intelligent computing and Optimization (ICO)

作者： Phuc Do Phu Pham Trung Phan Thuc Nguyen UIT VNU HCM Ho Chi Minh City Vietnam

ISBN: (纸本)9783030009793;9783030009786

Recently, heterogeneous network mining has gained tremendous attention from researcher due to its wide applications. Link prediction is one of the most important task in information network mining. From the past, most of the networked data mining approaches are mainly applied for homogenous network which is considered as single-typed objects and links. Moreover, there are remained challenges related to thoroughly evaluating the content of linked objects which are considered as important in predicting the potential relationships between objects. Like a common problem of predicting co-authorship in bibliographic network such as: DBLP, DBIS, etc. There is no doubt that an author who is interesting in "data mining" field tend to cooperate with the other authors who contribute on this field only. Hence, predicting co-authorships between authors work on "data mining" with others who work on "hardware" is dull as well. Moreover, in the context of large-scaled network, traditional standalone computing mechanism also is not affordable due to low-performance in time-consuming. To overcome these challenges, n this paper, we propose an approach of topic-driven meta-path-based prediction in heterogeneous network, called T-MPP which is implemented on distributed computing environment of Spark. The T-MPP not only enables to discover potential relationships in given bibliographic network but also supports to capture the topic similarity between authors. We present experiments on a real-world DBLP network. The outputs show that our proposed T-MPP model can generate more accurate prediction results as compared to previous approaches.

关键词： Information network HIN Link prediction Meta-path-based link prediction Topic-driven link prediction Large-scaled HIN distributed graph-based computing Spark

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