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检索条件"任意字段=19th International Conference on Algorithms andArchitectures for Parallel Processing, ICA3PP 2019"

共 18 条记录，以下是11-20 订阅

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RTEF-pp: A Robust Trust Evaluation Framework with Privacy Protection for Cloud Services Providers 19th

RTEF-PP: A Robust Trust Evaluation Framework with Privacy Pr...

引用

19th international conference on algorithms and Architectures for parallel processing (ica3pp)

作者： Zhong, Hong Zou, JianZhong Cui, Jie Xu, Yan Anhui Univ Sch Comp Sci & Technol Hefei 230039 Peoples R China Anhui Engn Lab IoT Secur Technol Hefei 230039 Peoples R China

ISBN: (纸本)9783030389918;9783030389901

the trust problem of Cloud Services Providers (CSPs) has become one of the most challenging issues for cloud computing. To build trust between Cloud Clients (CCs) and CSPs, a large number of trust evaluation frameworks have been proposed. Most of these trust evaluation frameworks collect and process evidence data such as the feedback and the preferences from CCs. However, evidence data may reveal the CCs' privacy. So far there are very few trust frameworks study on the privacy protection of CCs. In addition, when the number of malicious CCs' feedback increases, the accuracy of existing frameworks is greatly reduced. this paper proposes a robust trust evaluation framework RTEF-pp, which uses differential privacy to protect CCs' privacy. Furthermore, RTEF-pp uses the Euclidean distances between the monitored QoS values and CCs' feedback to detect malicious CCs' feedback ratings, and is not affected by the number of malicious CCs' feedback rating. Experimental results show that RTEF-pp is reliable and will not be affected by the number of malicious CCs' feedback rating.

关键词： Cloud computing Trust evaluation Robust Differential privacy

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Selective Velocity Distributed Indexing for Continuously Moving Objects Model 19th

Selective Velocity Distributed Indexing for Continuously Mov...

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19th international conference on algorithms and Architectures for parallel processing (ica3pp)

作者： Bareche, Imene Xia, Ying Chongqing Univ Posts & Telecommun Sch Comp Sci & Technol Chongqing Peoples R China

ISBN: (纸本)9783030389611;9783030389604

the widespread of GPS embedded devices has lead to a ubiquitous location dependent services, based on the generated real-time location data. this introduced the notion of continuous querying, and with the aid of advanced indexing techniques several complex query types could be supported. However the efficient querying and manipulation of such highly dynamic data is not trivial, processing factors of crucial importance should be carefully thought out such as accuracy and scalability. In this study we focus on Continuous KNN (CKNN) queries processing, one of the most well-know spatio-temporal queries over large scale of continuously moving objects. In this paper we provide an overview of CKNN queries and related challenges, as well as an outline of proposed works in the literature and their limitations, before getting to our contribution proposal. We propose a novel indexing approach model for CKNN querying, namely VS-TIMO. the proposed structure is based on a selective velocity partitioning method, since we have different objects with varying speeds. Our structure base unit is a comprised of a non overlapping R-tree and a two dimensions grid. In order to enhance performances, we design a compact multi-layer index structure on a distributed setting, and propose a CKNN search algorithm for accurate results using a candidate cells identification process. We provide a comprehensive vision of our indexing model and the adopted querying technique.

关键词： Continuous KNN querying Moving objects indexing Distributed spatio-temporal indices Velocity partitioning based index parallel processing

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FastDRC: Fast and Scalable Genome Compression Based on Distributed and parallel processing 19th

FastDRC: Fast and Scalable Genome Compression Based on Distr...

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19th international conference on algorithms and Architectures for parallel processing (ica3pp)

作者： Ji, Yimu Fang, Houzhi Yao, Haichang He, Jing Chen, Shuai Li, Kui Liu, Shangdong Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China Nanjing Inst Ind Technol Sch Comp & Software Nanjing 210023 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia Nanjing Univ Posts & Telecommun Inst High Performance Comp & Big Data Nanjing 210003 Peoples R China Nanjing Ctr HPC China Nanjing 210003 Peoples R China Jiangsu HPC & Intelligent Proc Engineer Res Ctr Nanjing 210003 Peoples R China

ISBN: (纸本)9783030389611;9783030389604

With the advent of next-generation sequencing technology, sequencing costs have fallen sharply compared to the previous sequencing technologies. Genomic big data has become the significant big data application. In the face of growing genomic data, its storage and migration face enormous challenges. therefore, researchers have proposed a variety of genome compression algorithms, but these algorithms cannot meet the processing requirements for large amount of biological data and high processing speed. this manuscript proposes a parallel and distributed referential genome compression algorithm-Fast Distributed Referential Compression (FastDRC). this algorithm compresses a large number of genomic sequences in parallel under the Apache Hadoop distributed computing framework. Experiments show that the compression efficiency of the FastDRC is greatly improved when it compresses large quantities of genomic data. Moreover, FastDRC leads to the only distributed computing method known to us in the field of genome compression. the source code for FastDRC can be obtained from this link: https://***/GhostCCCathenry/FastDRC.

关键词： Genome compression Distributed processing Apache Hadoop FASTA

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algorithms and Architectures for parallel processing 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Sheng Wen Albert Zomaya Laurence T. Yang

the two-volume set LNCS 11944-11945 constitutes the proceedings of the 19th international conference on algorithms and Architectures for parallel processing, ica3pp 2019, held in Melbourne, Australia, in December 2019.

ISBN: (数字)9783030389918

ISBN: (纸本)9783030389901

关键词： Computational Mathematics and Numerical Analysis Algorithm Analysis and Problem Complexity Computer Systems Organization and Communication Networks Operating Systems

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algorithms and Architectures for parallel processing 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Sheng Wen Albert Zomaya Laurence T. Yang

ISBN: (数字)9783030389611

ISBN: (纸本)9783030389604

关键词： Computational Mathematics and Numerical Analysis Algorithm Analysis and Problem Complexity Computer Systems Organization and Communication Networks Operating Systems

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A Communication-Avoiding Algorithm for Molecular Dynamics Simulation 19th

A Communication-Avoiding Algorithm for Molecular Dynamics Si...

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19th international conference on algorithms and Architectures for parallel processing (ica3pp)

作者： Wang, Bei Chen, Yifeng Hou, Chaofeng Peking Univ EECS Beijing 100871 Peoples R China Chinese Acad Sci Inst Proc Engn Beijing Peoples R China

ISBN: (纸本)9783030389918;9783030389901

Molecular dynamics and many similar time-dependent computing tasks are defined as simple state updates over multiple time steps. In recent years, modern supercomputing clusters have enjoyed fast-growing compute capability and moderate-growing memory bandwidth, but their improvement of network bandwidth/latency is limited. In this paper, we propose a new communication-avoiding algorithmic model based on asynchronous communications which, unlike BSP, records and handles multiple iterative states together. the basic idea is to let computation run in small regular time steps while communications over longer dynamic time steps. Computation keeps checking inaccuracies so that the intervals between communications are small in volatile scenarios but longer when dynamics is smooth. this helps reduce the number of data exchanges via network communication and hence improve the overall performance when communication is the bottleneck. We test MD simulation of condensed covalent materials on the Sunway TaihuLight. For best time-to-solution, the general-purpose supercomputer Sunway TaihuLight performs 11.8K steps/s for a system with 2.1 million silicon atoms and 5.1 K steps/s for 50.4 million silicon atoms. this time-to-solution performance is close to those of state-of-art hardware solution. A software solution using general-purpose supercomputers makes the technology more accessible to the general scientific users.

关键词： BSP parallel-in-time Molecular dynamics

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Rise the Momentum: A Method for Reducing the Training Error on Multiple GPUs 19th

Rise the Momentum: A Method for Reducing the Training Error ...

引用

19th international conference on algorithms and Architectures for parallel processing (ica3pp)

作者： Tang, Yu Yin, Lujia Zhang, Zhaoning Li, Dongsheng Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha Peoples R China

ISBN: (纸本)9783030389611;9783030389604

Deep neural network training is a common issue that is receiving increasing attention in recent years and basically performed on Stochastic Gradient Descent or its variants. Distributed training increases training speed significantly but causes precision loss at the mean time. Increasing batchsize can improve training parallelism in distributed training. However, if the batchsize is too large, it will bring difficulty to training process and introduce more training error. In this paper, we consider controlling the total batchsize and lowering batchsize on each GPU by increasing the number of GPUs in distributed training. We train Resnet50 [4] on CIFAR-10 dataset by different optimizers, such as SGD, Adam and NAG. the experimental results show that large batchsize speeds up convergence to some degree. However, if the batchsize of per GPU is too small, training process fails to converge. Large number of GPUs, which means a small batchsize on each GPU declines the training performance in distributed training. We tried several ways to reduce the training error on multiple GPUs. According to our results, increasing momentum is a well-behaved method in distributed training to improve training performance on condition of multiple GPUs of constant large batchsize.

关键词： Multiple GPUs Batchsize Distributed training Momentum

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HDF5-Based I/O Optimization for Extragalactic HI Data Pipeline of FAST 19th

HDF5-Based I/O Optimization for Extragalactic HI Data Pipeli...

引用

19th international conference on algorithms and Architectures for parallel processing (ica3pp)

作者： Ji, Yiming Yu, Ce Xiao, Jian Tang, Shanjiang Wang, Hao Zhang, Bo Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Chinese Acad Sci CAS Key Lab FAST NAOC Beijing Peoples R China

ISBN: (纸本)9783030389611;9783030389604

the Five-hundred-meter Aperture Spherical Radio Telescope (FAST), which is the largest single-dish radio telescope in the world, has been producing a very large data volume with high speed. So it requires a high performance data pipeline to covert the huge raw observed data to science data product. However, the existing solutions of pipelines widely used in radio data processing cannot tackle this situation efficiently. the paper proposes a pipeline architecture for FAST based on HDF5 format and several I/O optimization strategies. First, we design the workflow engine driving the various tasks efficiently in the pipeline;second, we design a common radio data storage specification on the top of HDF5 format, and also developed a fast converter to map the original FITS format to the new HDF5 format;third, we apply several concrete strategies to optimize the I/O operations, including chunks storage, parallel reading/writing, on-demand dump, and stream process etc. In the experiment of processing 700 GB of FAST data, the results show that HDF5 based data structure without other optimizations was 1.7 times faster than original FITS format. If chunk storage and parallel I/O optimization are applied, the overall performance can reach 4.5 times as the original one. Moreover, due to the good expansibility and flexibility, our solution of FAST pipeline can be adapted to other radio telescopes.

关键词： FAST FITS HDF5 Pipeline in parallel High performance I/O

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