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检索条件"任意字段=8th International Conference on Parallel and Distributed Computing, Applications and Technologies"

共 1802 条记录，以下是391-400 订阅

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Supporting efficient execution of many-task applications with everest 8

Supporting efficient execution of many-task applications wit...

引用

Selected Papers of the 8th international conference "distributed computing and Grid-technologies in Science and Education", GRID 2018

作者： Sukhoroslov, O.V. Centre for Distributed Computing Institute for Information Transmission Problems Bolshoy Karetny Per. 19 Build. 1 Moscow127051 Russia

distributed computing systems are widely used for execution of loosely coupled many-task applications. there are two important classes of such applications. Bag-of-tasks applications, e.g., parameter sweeps or Monte Carlo simulations, represent a set of independent tasks. Workflows, which are used for automation of complex computational and data processing pipelines, consist of multiple tasks with control or data dependencies. the paper discusses the common problems related to the efficient execution of such applications on distributed computing resources and the relevant solutions implemented within the Everest platform. © 2018 Oleg V. Sukhoroslov.

关键词： Monte Carlo methods

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Transaction Finality through Ledger Checkpoints

Transaction Finality through Ledger Checkpoints

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Ratul Antik Das Md. Muhaimin Shah Pahalovi Muhammad Nur Yanhaona Research and Development Kona Software Lab Dhaka Bangladesh

ISBN: (纸本)9781728125848

the reversal of transactions due to blockchain ledger reorganization has become a major hindrance for public blockchain technologies' adoption in real-world business and financial applications. Since a typical real-world product, service, or agreement cannot be reversed; associated transactions in the blockchain ledger must also be final. this paper describes and analyzes the transaction finality solution for our proof of work (PoW) mining based blockchain network, the Kona Blockchain Platform. the solution works even if 49% of the network's total mining power is compromised due to malicious attacks. Although designed for our specific platform, the ideas from the solution can be easily adapted to achieve transaction finality in existing public blockchain networks. this paper also discusses how this can be done. To the best of our knowledge, ours is the first solution for deterministic transaction finality for blockchain networks incentivized exclusively by PoW mining.

关键词： Computer networks Peer-to-peer computing distributed information systems Fault tolerance Protocols distributed information systems Peer to peer computing computer network Fault tolerance protocols Checkpoints mining Solutions self-reversal

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PPS: parallel Pincer Search for Mining Frequent Itemsets Based on Spark 8th

PPS: Parallel Pincer Search for Mining Frequent Itemsets Bas...

引用

8th international conference on Soft computing and Pattern Recognition (SoCPaR) / 8th international conference on Computational Aspects of Social Networks (CASoN)

作者： Sethi, Krishan Kumar Dharavath, Ramesh Nyakotey, Samuel Indian Inst Technol ISM Dept Comp Sci & Engn Dhanbad 826004 Jharkhand India

ISBN: (纸本)9783319606187;9783319606170

Association rule mining is one of prominent techniques to discover the relation between data items of a transactional data. the process of mining has been simplified by considering only the frequent itemsets. Pincer search is one of the frequent itemset mining method which combines top-down and bottom-up search techniques to get the benefits of both. Top-down approach in Pincer search reduces the number of candidates in pass of iterations and saves a lot of computing resources. In this work, we present a parallel Pincer Search (PPS) which is based on distributed implementation on Spark framework. We have converted the search algorithm according to the Spark framework to make it run in parallel. Spark provides a lot of features for the iterative algorithm such as in-memory execution, efficient data structure, better fault tolerant method, etc. We implemented the PPS on a Spark cluster with multiple datasets and analysed the performance.

关键词： Pincer search Frequent itemset mining Apriori algorithm Maximal itemset Spark

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Tasking in Accelerators: Performance Evaluation

Tasking in Accelerators: Performance Evaluation

引用

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

作者： Leonel Toledo Antonio J. Peña Sandra Catalán Pedro Valero-Lara Barcelona Supercomputing Center (BSC) Barcelona Spain Barcelona Supercomputing Center Barcelona Spain

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

In this work, we analyze the implications and results of implementing dynamic parallelism, concurrent kernels and CUDA Graphs to solve task-oriented problems. As a benchmark we propose three different methods for solving DGEMM operation on tiled-matrices; which might be the most popular benchmark for performance analysis. For the algorithms that we study, we present significant differences in terms of data dependencies, synchronization and granularity. the main contribution of this work is determining which of the previous approaches work better for having multiple task running concurrently in a single GPU, as well as stating the main limitations and benefits of every technique. Using dynamic parallelism and CUDA Streams we were able to achieve up to 30% speedups and for CUDA Graph API up to 25x acceleration outperforming state of the art results.

关键词： Graphics processing units Task analysis parallel processing Kernel Performance evaluation Synchronization Programming

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Concurrent Failure Recovery for Product Matrix Regenerating Code

Concurrent Failure Recovery for Product Matrix Regenerating ...

引用

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

作者： Jingyao Zhang Chief Architect of Here Data Technology Shenzhen China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

Regenerating codes can minimize the network bandwidth required to recover the lost data in case of node failure in distributed storage systems. Product Matrix (PM) code is an important kind of Minimum Storage Regenerating (MSR) code that can maximize the storage efficiency, meanwhile minimizing the repair bandwidth. the original Product Matrix (PM) code only addressed single node failure. In this work, we will propose an algorithm of recovering multiple failed nodes concurrently for PM code. the explicit construction of the Repair Matrix that is applicable to any reasonable combinations of coding parameters will be presented, and the lost data can be obtained by simply multiplying the helper data with the repair matrix, thus is very easy for implementation. Based on the proposed strategy, the needed bandwidth for two major repairing policies: centralized and distributed recovery will be given formally. Moreover, the impact of Repairing Degree (the number of surviving nodes from which the assistant data are downloaded) on the bandwidth cost will be studied, which can help make optimal decisions in practical storage systems.

关键词： Maintenance engineering Bandwidth distributed databases Encoding Symmetric matrices Matrices Redundancy

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GPU parallel Acceleration of Frequency Domain Algorithm for Calculating Radar Target Echoes

GPU Parallel Acceleration of Frequency Domain Algorithm for ...

引用

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

作者： Zhao Naixuan Zhang Ge Wang Zijian Kuang Ling University of Electronic Science and Technology of China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126173

After the radar detects the target by transmitting signal, the target returns echo signal, which contains the position and velocity of the target. To meet the demand of high-performance echo computing for real-time radar simulation, this paper proposes a frequency domain algorithm for calculating echoes based on GPU. the experiments are conducted to compare the execution time on CPU and GPU. Moreover, we do a lot of tests to contrast the traditional GPU acceleration time domain algorithm and the algorithm proposed in this paper. the results show that, compared with the traditional CPU-based simulator, the GPU-based simulator calculates the echo faster. And with the increase of the number of sampling points, speedup ratio rises. Compared with the existing GPU acceleration time domain algorithm, the GPU-based frequency domain algorithm for calculating echo improves the real-time performance, and with the increase of the number of the targets in the scene, the acceleration ratio promotes obviously. Furthermore, the phase offset due to the time delay is performed in the frequency domain directly, which improve the ranging accuracy.

关键词： Graphics processing units Frequency-domain analysis Radar Time-domain analysis Delay effects Radar antennas Acceleration

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A Hybrid parallel Implementation for the Maximum Flow Problem 26

A Hybrid Parallel Implementation for the Maximum Flow Proble...

引用

26th Euromicro international conference on parallel, distributed, and Network-Based Processing (PDP)

作者： Stefanes, Marco A. Alvino, Luiz F. Univ Fed Mato Grosso do Sul Comp Coll Campo Grande Brazil Univ Fed Mato Grosso do Sul Campo Grande Brazil

ISBN: (纸本)9781538649756

the maximum flow problem is a classical combinatorial problem with many applications. In this work a hybrid parallel algorithm using both multi-core and many-core technologies for computing the maximum flow in a network is presented. the proposed implementation is applicable in OpenMP/CUDA-enabled computing environment. To improve the performance two strategies were implemented: an adaptive approach where the algorithm alternate GPU/CPU processing according to the number of active nodes and implementations of the global relabeling and gap relabeling heuristics on multi-core approach. When compared against the best sequential implementation, the speedups range from 2.36 to 5.38 in several kinds of graph. Results show that the proposed algorithm is faster than previous parallel implementations on CPU/GPUs for all kinds of tested graphs.

关键词： Maximum flow Push-relabel method Hybrid parallel programming Multi/many-core architectures

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FPGA-Based parallel Multi-Core GZIP Compressor in HDFS

FPGA-Based Parallel Multi-Core GZIP Compressor in HDFS

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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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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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