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检索条件"任意字段=9th International Conference on Parallel and Distributed Computing, Applications and Technologies"

共 1772 条记录，以下是411-420 订阅

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FPGA-Based parallel Multi-Core GZIP Compressor in HDFS

FPGA-Based Parallel Multi-Core GZIP Compressor in HDFS

引用

IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Haoxin Luo Ye Cai Qiuming Luo Rui Mao College of Computer Science & Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

With the development of Big Data, data storage has been exposed to more challenges. Data compression which can save both storage and network bandwidth, is a very important technology to deal with the challenges. In this paper, we present an end-to-end, complete, high-throughput parallel multi-core GZIP compressor in FPGA for HDFS. the GZIP compressor is designed by the scalable architecture, which supports to increase throughput by expanding multiple compression cores based on systolic array architecture. We implemented and evaluated the hardware compressor in Alpha Data Adm-Pcie-KU3 FPGA board, utilizing RIFFA for data transfers over PCI Express. According to the evaluation results, up to 16-cores compressor can be implemented and the peak compression throughput exceeds 1.1 GB/s. It is 70X speedup compared with the software compression solution. When we load the hardware compressor into HDFS, the performance of HDFS is twice as much as that without loading the compressor.

关键词： throughput Hardware Field programmable gate arrays Software Dictionaries Arrays

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A parallel Adaptive Circumference Method with OpenMP

A Parallel Adaptive Circumference Method with OpenMP

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12th KIPS international conference on Ubiquitous Information technologies and applications (CUTE) / 9th international conference on Computer Science and its applications (CSA)

作者： Bai, Hongtao Fang, Jinwei Zhang, Fengxu Sun, Xiaomeng Jilin Univ Ctr Comp Fundamental Educ Changchun 130012 Jilin Peoples R China Jilin Univ Coll Earth Survey Sci & Technol Changchun 130026 Jilin Peoples R China

ISBN: (纸本)9789811076053;9789811076046

the implementation of identifying regional and local gravity fields computing algorithm by adaptive circumference method is achieved in this paper, aiming to the drawback of traditional circumference method in a chosen suitable radius among varies objects. We get suitable radius automatically by the minimum variance of computing point relative to the points on circle in series of set radius, and use the average of regional field value instead of the average of anomaly gravity when calculated point is as the center and to get closer to actual fields in iteration procession. Further, a parallel algorithm based on OpenMP employed multi-core CPU has been presented for high efficiency. Otherwise, the relationship between the number of cores and parallel efficiency is analyzed. the results of theoretical model and actual data show not only our adaptive circumference method can clearly separate abnormal boundary and get accurate abnormity, but also the algorithm is adaptive and has high computational efficiency.

关键词： Gravity abnormity Regional gravity field Local gravity field Adaptive circumference method OpenMP

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Quantifying the Impact of Design Strategies for Big Data Cyber Security Analytics: An Empirical Investigation

Quantifying the Impact of Design Strategies for Big Data Cyb...

引用

IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Faheem Ullah M. Ali Babar CREST ??? Centre for Research on Engineering of Software Technologies The University of Adelaide Australia Cyber Security Adelaide The University of Adelaide Australia

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

Big Data Cyber Security Analytics (BDCA) systems use big data technologies (e.g., Hadoop and Spark) for collecting, storing, and analyzing a large volume of security event data to detect cyber-attacks. the state-of-the-art uses various design strategies (e.g., feature selection and alert ranking) to help BDCA systems to achieve the desired levels of accuracy and response time. However, the use of these strategies in the state-of-the-art is not consistent, which exposes a lack of consensus on "when to use (and not to use) these design strategies?" In this paper, we follow a systematic experimentation framework to quantify the impact of four design strategies on the accuracy and response time with respect to three contextual factors i.e., security data, machine learning model employed in the system, and the execution mode of the system. For the aimed quantification, we performed experiments on a Hadoop-based BDCA system using four security datasets, five machine learning models, and three execution modes. Our findings lead us to formulate a set of design guidelines that will help researchers and practitioners to decide when to use (and not to use) the design strategies.

关键词： Time factors Feature extraction distributed databases Training Big Data Computer security

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From Insight to Impact: Building a Sustainable Edge computing Platform for Smart Homes 24

From Insight to Impact: Building a Sustainable Edge Computin...

引用

24th IEEE international conference on parallel and distributed Systems (ICPADS)

作者： Chang, Xiaomin Li, Wei Xia, Chunqiu Ma, Jin Cao, Junwei Khan, Samee U. Zomaya, Albert Y. Univ Sydney Sch Informat Technol Ctr Distributed & High Performance Comp Sydney NSW Australia Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Tsinghua Univ Res Inst Informat Technol Beijing Peoples R China North Dakota State Univ Dept Elect & Comp Engn Fargo ND USA

ISBN: (纸本)9781538673089

there is a growing trend for engaging edge computing to help smart homes to improve the living comfort of residents. However, the rapidly rising interest in such deployments does not normally integrate with energy management schemes, which is a central issue that smart homes have to face. In this paper, we propose a unified energy management framework for enabling a sustainable edge computing paradigm while meeting the needs of home energy management and smart home applications. this framework aims to enable the full use of renewable energy while reducing electricity bills for households. A prototype system was implemented by using low-cost and easy-to-get hardware. the experiment results demonstrated that renewable energy is fully capable of supporting the reliable running of edge computing devices and electricity bills could be cut by up to 86% when our proposed framework was employed.

关键词： Internet of things Edge computing Smart Homes Renewable Energy Sustainable computing Scheduling Energy Management

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Performance Study of distributed Big Data Analysis in YARN Cluster 9

Performance Study of Distributed Big Data Analysis in YARN C...

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9th international conference on Information and Communication Technology Convergence (ICTC)

作者： Ahn, HooYoung Kim, Hyunjae You, WoongShik ETRI KSB Convergence Res Dept 218 Gajeong Ro Daejeon 34129 South Korea

ISBN: (纸本)9781538650417

In the 4-th Industrial Revolution era, various intelligent solutions and services have been emerging recently. To provide high quality service in those intelligent applications, the big data should be collected without any loss and comprehensively analyzed. Especially, when using machine and deep learning techniques, the big data processing delays should be minimized in order to guarantee the freshness of models. In this paper, we evaluate the performance of Apache Spark which is one of the most popular big data processing and analysis frameworks. Beyond the performance analysis of Spark in distributed cluster environment, we evaluate the performance of TensorFlowOnSpark which is the promising distributed deep learning framework designed to handle big data efficiently. From the experimental results, we can conclude that Spark on YARN is a solid underlying framework that guarantees the performance and scalability of distributed machine and deep learning by efficiently processing its data and algorithms in a parallel and distributed manner.

关键词： distributed Machine Learning distributed Deep Learning Big Data parallel computing Spark YARN TensorFlow Benchmark

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Hadoop Based parallel Deduplication Method for Web Documents

Hadoop Based Parallel Deduplication Method for Web Documents

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12th KIPS international conference on Ubiquitous Information technologies and applications (CUTE) / 9th international conference on Computer Science and its applications (CSA)

作者： Song, Junjie Liu, Jin Zheng, Yuhui Shanghai Martime Univ Coll Informat Shanghai Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China

ISBN: (纸本)9789811076053;9789811076046

this paper proposes a method of deleting duplicate web pages through tf-idf and splay tree. According to the keywords which are extracted by TextRank, those pages which may be duplicate copies will be sent to a group. then these pages will be judged by the method above. We use three Map-Reduce tasks to ensure the method of calculating tf-idf and deleting duplicate web pages. the experiment result shows that the algorithm can remove duplicate web pages efficiently and accurately.

关键词： Webpage deduplication Vertical search engine

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the Library for Hadoop Deflate Compression Based on FPGA Accelerator with Load Balance

The Library for Hadoop Deflate Compression Based on FPGA Acc...

引用

IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Haixin Du Jiankui Zhang Shihao Sha Cai Ye Qiuming Luo College of Computer Science and Software Engineering Shenzhen University NHPCC/Guangdong Key Laboratory of popular HPC and Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

Hadoop application will produce lots of intermediate results in the map/reduce process that requires disk I/O and network transmission. By compressing the large-scale data of intermediate result, it will greatly improve disk access efficiently and reduce program run time. Hardware-accelerated solutions have become more desirable. this paper design a multi-FPGA compression accelerator on the Hadoop platform, and the system performance analysis compared with a software-only solution that mainly uses CPU to processing. the testing programs are zpipe, TestDFSIO and Terasort. In contrast with the software-only solution. the max speedup of zpipe is 6.55X (single FPGA) and 10.24X (dual FPGA), the max speedup of TestDFSIO is 6.28X (single FPGA) and 6.28X (dual FPGA), and the max speedup of Terasort application is up to 3.25X(single FPGA) and 3.35X(dual FPGA).

关键词： Handheld computers conferences Computer science Software engineering distributed databases Indexes

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Succinct Representations in Collaborative Filtering: A Case Study using Wavelet Tree on 1,000 Cores

Succinct Representations in Collaborative Filtering: A Case ...

引用

IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Xiangjun Peng Qingfeng Wang Xu Sun Chunye Gong Yaohua Wang User-Centric Computing Group University of Nottingham Ningbo China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

User-Item (U-I) matrix has been used as the dominant data infrastructure of Collaborative Filtering (CF). To reduce space consumption in runtime and storage, caused by data sparsity and growing need to accommodate side information in CF design, one needs to go beyond the U-I Matrix. In this paper, we took a case study of Succinct Representations in Collaborative Filtering, rather than using a U-I Matrix. Our key insight is to introduce Succinct Data Structures as a new infrastructure of CF. Towards this, we implemented a User-based K-Nearest-Neighbor CF prototype via Wavelet Tree, by first designing a Accessible Compressed Documents (ACD) to compress U-I data in Wavelet Tree, which is efficient in both storage and runtime. then, we showed that ACD can be applied to develop an efficient intersection algorithm without decompression, by taking advantage of ACD's characteristics. We evaluated our design on 1,000 cores of Tianhe-II supercomputer, with one of the largest public data set ml-20m. the results showed that our prototype could achieve 3.7 minutes on average to deliver the results.

关键词： Vegetation Collaboration Filtering Data structures Runtime Indexes Time complexity

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parallel data reduction method for complex technical objects and processes 9

Parallel data reduction method for complex technical objects...

引用

9th IEEE international conference on Dependable Systems, Services and technologies, DESSERT 2018

作者： Oliinyk, Andrii Leoshchenko, Serhii Lovkin, Valerii Subbotin, Sergey Zaiko, Tetiana Zaporizhzhia National Technical University Dept. of Software Tools 69063 Ukraine

ISBN: (纸本)9781538659038

this paper presents a method that implements parallel reduction of data based on the stochastic calculations. Significant attention is paid to the relevance of the developed method and the description of its features. For assessing the effectiveness of the method experimental comparison was carried out: the results are also presented in this paper. © 2018 IEEE.

关键词： Stochastic systems

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A Holistic Stream Partitioning Algorithm for distributed Stream Processing Systems

A Holistic Stream Partitioning Algorithm for Distributed Str...

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IEEE international conference on parallel and distributed computing, applications and technologies (PDCAT)

作者： Kejian Li Gang Liu Minhua Lu Guangdong Province Key Laboratory of Popular High Performance Computers Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of biomedical engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

the performances of modern distributed stream processing systems are critically affected by the distribution of the load across workers. Skewed data streams in real world are very common and pose a great challenge to these systems, especially for stateful applications. Key splitting, which allows a single key to be routed to multiple workers, is a great idea to achieve good balance of load in the cluster. However, it comes with the cost of increased memory consumption and computation overhead as well as network communication. In this paper, we present a new definition of metric to model the cost of key splitting for intra-operator parallelism in stream processing systems and provide a novel perspective to reduce replication factor while keeping both overall load imbalance and processing latency low. Similar to previous work, our approach treats the head and the tail of the distribution differently in order to reduce memory requirements. For the head, it uses our proposed notion of regional load imbalance to decide dynamically whether to make one more worker responsible for the heavy hitter or not. For the tail, it simply uses hash partitioning to keep the size of the routing table for the head as small as possible. Extensive experimental evaluation demonstrates that our approach provides superior performance compared to the state-of-the-art partitioning algorithms in terms of load imbalance, replication factor and latency over different levels of skewed stream distributions.

关键词： Partitioning algorithms Memory management Measurement parallel processing Microsoft Windows Load modeling throughput

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