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检索条件"任意字段=Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 4332 条记录，以下是1121-1130 订阅

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CE-Dedup: Cost-Effective Convolutional Neural Nets Training based on Image Deduplication

CE-Dedup: Cost-Effective Convolutional Neural Nets Training ...

引用

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

作者： Xuan Li Liqiong Chang Xue Liu School of Computer Science McGill University Montreal Quebec Canada

Attributed to the ever-increasing large image datasets, Convolutional Neural networks (CNNs) have become popular for vision-based tasks. It is generally admirable to have larger-sized datasets for higher network training accuracies. However, the impact of dataset quality has not to be involved. It is reasonable to assume the near-duplicate images exist in the datasets. For instance, the Street View House Numbers (SVHN) dataset having cropped house plate digits from 0 to 9 are likely to have repetitive digits from the same/similar house plates. Redundant images may take up a certain portion of the dataset without consciousness. While contributing little to no accuracy improvement for the CNNs training, these duplicated images unnecessarily pose extra resource and computation consumption. To this end, this paper proposes a framework to assess the impact of the near-duplicate images on CNN training performance, called CE-Dedup. Specifically, CE-Dedup associates a hashing-based image deduplication approach with downstream CNNs-based image classification tasks. CE-Dedup balances the tradeoff between a large deduplication ratio and a stable accuracy by adjusting the deduplication threshold. The effectiveness of CE-Dedup is validated through extensive experiments on well-known CNN benchmarks. On one hand, while maintaining the same validation accuracy, CE-Dedup can reduce the dataset size by 23%. On the other hand, when allowing a small validation accuracy drop (by 5%), CE-Dedup can trim the dataset size by 75%.

关键词： Training Benchmark testing Convolutional neural networks Task analysis Image classification

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Divisible Load Scheduling of Image processing Applications on the Heterogeneous Star network Using a New Genetic Algorithm 26

Divisible Load Scheduling of Image Processing Applications o...

引用

26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Aali, Sahar Nikbakht Shahhosseini, Hadi Shahriar Bagherzadeh, Nader Iran Univ Sci & Technol Tehran Iran Univ Calif Irvine Irvine CA USA

ISBN: (纸本)9781538649756

The divisible load scheduling of image processing applications on the heterogeneous star network is addressed in this paper. In our platform, processors and links have different speeds. Also the computation and communication overheads are considered. A new genetic algorithm for minimizing the processing time of low level image applications using divisible load theory is introduced. A closed form solution for the processing time and the image fractions that should be assigned to each processor are obtained. The optimum number of participating processors and the optimal sequence for load distribution with a new genetic algorithm are derived. The effect of different image and kernel sizes on processing time and speed up are investigated. Finally, to indicate the efficiency of our algorithm, several numerical experiments are presented.

关键词： Divisible load scheduling local operation image fractions load distibution sequence genetic algorithm

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Word Embedding-based Context-sensitive network Flow Payload Anomaly Detection

Word Embedding-based Context-sensitive Network Flow Payload ...

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International conference on Applied Machine Learning (ICAML)

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

ISBN: (纸本)9781665421263

Payload anomaly detection can discover malicious behaviors hidden in network packets. It is hard to handle payload due to its various possible characters and complex semantic context, and thus identifying abnormal payload is also a non-trivial task. Prior art only uses the n-gram language model to extract features, which directly leads to ultra-high-dimensional feature space and also fails to capture the context semantics fully. Accordingly, this paper proposes a word embedding-based context-sensitive network flow payload anomaly detection method (termed WECAD). First, WECAD obtains the initial feature representation of the payload through the word embedding-based method. Then, we propose a corpus pruning algorithm, which applies the cosine similarity clustering and frequency distribution to prune inconsequential characters. We only keep the essential characters to reduce the calculation space. Subsequently, we propose a context learning algorithm. It employs the co-occurrence matrix transformation technology and introduces the backward step size to consider the order relationship of essential characters. Comprehensive experiments on real-world intrusion detection datasets validate the effectiveness of our method.

关键词： Art Semantics Clustering algorithms Intrusion detection Machine learning Feature extraction Task analysis

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Resizing of Heterogeneous Platforms and the optimization of parallel applications 26

Resizing of Heterogeneous Platforms and the optimization of ...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Beji, Moussa Achour, Sami Fac Sci Tunis Dept Comp Sci Tunis Tunisia Higher Inst Appl Sci & Technol Sousse Dept Comp Sci Sousse Tunisia

ISBN: (纸本)9781538649756

With the birth of multi-cluster platforms, scheduling and finding the optimal number of resources (clusters, processors) to execute an application constitute very critical problems. In this paper, we address the need for scheduling techniques for parallel task applications on this kind of platforms and we propose a new strategy for scheduling sequential task graphs based on existing heuristics that have proved to be efficient on homogeneous environments. The contribution of this paper lies in determining the appropriate clusters which participate to compute a given application. Our solution is composed of three steps: Firstly, determining of the computing clusters, secondly, determining the optimal number of processors in each cluster, finally place the tasks on the appropriate processors. Simulation results, based on both randomly generated graphs and real configuration platforms, show that the proposed approach provides interesting trade-off between makespan and resource consumption.

关键词： multi-cluster paltforms parallel task graph makespan scheduling

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ARTICONF: Towards a Smart Social Media Ecosystem in a Blockchain Federated Environment 25th

ARTICONF: Towards a Smart Social Media Ecosystem in a Blockc...

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25th International conference on parallel and distributed Computing (Euro-Par)

作者： Prodan, Radu Saurabh, Nishant Zhao, Zhiming Orton-Johnson, Kate Chakravorty, Antorweep Karadimce, Aleksandar Ulisses, Alexandre Univ Klagenfurt Inst Informat Technol Klagenfurt Austria Univ Amsterdam Informat Inst Amsterdam Netherlands Univ Edinburgh Sch Social & Polit Sci Edinburgh Midlothian Scotland Univ Stavanger Dept Elect Engn & Comp Sci Stavanger Norway Univ Informat Sci & Technol Ohrid North Macedonia MOG Technol Maia Portugal

ISBN: (纸本)9783030483401;9783030483395

The ARTICONF project funded by the European Horizon 2020 program addresses issues of trust, time-criticality and democratisation for a new generation of federated infrastructure, to fulfil the privacy, robustness, and autonomy related promises critical in proprietary social media platforms. It aims to: (1) simplify the creation of open and agile social media ecosystem with trusted participation using a two stage permissioned blockchain;(2) automatically detect interest groups and communities using graph anonymization techniques for decentralised and tokenized decision-making and reasoning;(3) elastically autoscale time-critical social media applications through an adaptive orchestrated Cloud edge-based infrastructure meeting application runtime requirements;and (4) enhance monetary inclusion in collaborative models through cognition and knowledge supply chains. We summarize the initial envisaged architecture of the ARTICONF ecosystem, the industrial pilot use cases for validating it, and the planned innovations compared to related other European research projects.

关键词： Decentralized social media Privacy Trust Blockchain Semantic network Auto-scaling Cloud and edge computing

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The 16,384-node parallelism of 3D-CNN Training on An Arm CPU based Supercomputer

The 16,384-node Parallelism of 3D-CNN Training on An Arm CPU...

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International conference on High Performance Computing

作者： Akihiro Tabuchi Koichi Shirahata Masafumi Yamazaki Akihiko Kasagi Takumi Honda Kouji Kurihara Kentaro Kawakami Tsuguchika Tabaru Naoto Fukumoto Akiyoshi Kuroda Takaaki Fukai Kento Sato Fujitsu Limited Kawasaki Kanagawa Japan RIKEN Center for Computational Science Kobe Hyogo Japan

ISBN: (纸本)9781665410175

As the computational cost and datasets available for deep neural network training continue to increase, there is a significant demand for fast distributed training on supercomputers. However, porting and tuning applications for new advanced supercomputers requires tremendous amount of development efforts. Therefore, we present software tuning best practice for a 3D-CNN model training on a new Arm CPU based supercomputer, Fugaku. We (i) tune computation in DL by a JIT translator for aarch64, (ii) optimize collective communication such as Allreduce for 6D mesh/torus network topology, (iii) tune I/O by data staging with compression and data loader with caching, and (iv) parallelize training in data and model parallelism. We apply the proposed methods to a CosmoFlow 3D-CNN model, and achieve the training in 30 minutes using 16,384 nodes consisting of 4096 data- and 4 model-parallelism. This is the fastest result of any CPU-based systems in MLPerf HPC v0.7 in the world.

关键词： Training Computational modeling Neural networks parallel processing Supercomputers Software Computational efficiency

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Power Quality Enhancement in AC Microgrids Using Integrated Genetic Algorithm in ANN

Power Quality Enhancement in AC Microgrids Using Integrated ...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： M. Vijay Karthik Priyanka Chaudhary Noida International University Greater Noida India

ISBN: (纸本)9781665435253

This article presents the detailed analysis of power quality improvement in microgrids by using the multiple schemes. The microgrid contains the various renewable energy sources, and they are interconnected with the power electronic converters. These converters are faced the many issues with respect to the reactive inrush currents and harmonic voltages. These currents also resulted in voltage drop across the line impedances. The voltage harmonics and fluctuations can generate the malfunctioning, overheating and equipment tripping issues. The microgrid stability mainly depends upon the optimization method and compensation of various micro grid units. This article mainly focused on development of smart grid system by combining the two individual microgrids using electronically coupled distributed energy resources (EC-DER) inverter and smart impedance (SI) converter. Thus, the optimal power flow was achieved based on the each individual microgrid available power. The two “parallel optimization” methods are used to optimize various power quality challenges such as operation mode transfer transients, harmonic load sharing and parallel control of current and voltage quality. Thus, artificial neural network (ANN) approach is used for improving EC-DER performance and genetic algorithm (GA) is used for improving SI performance. The stability of the system is achieved by operating the ANN-GA in parallel processing manner. Thus, two microgrids develops the individual powers and optimal power flow is generated in smart grid. The simulation results shows that the proposed ANN-GA controller results the reduced total harmonic distortion (THD) and fault setting time in microgrids as compared to the conventional model predictive control (MPC) approach.

关键词： Power quality Microgrids Artificial neural networks Power system stability parallel processing Harmonic analysis Stability analysis

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A Secure distributed Framework for Agglomerative Hierarchical Clustering Construction 26

A Secure Distributed Framework for Agglomerative Hierarchica...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Hamidi, Mona Sheikhalishahi, Mina Martinelli, Fabio Univ Siena Dipartimento Ingn Informaz & Sci Matemat Siena Italy CNR Ist Informat & Telemat Pisa Italy

ISBN: (纸本)9781538649756

This paper presents a general framework for constructing any agglomerative hierarchical clustering algorithm over partitioned data. It is assumed that data is distributed between two (or more) parties horizontally, such that for mutual benefits the participated parties are willing to identify the clusters' structure on their data as a whole, but for privacy restrictions, they avoid to share the original datasets. To this end, in this study, we propose general algorithms based on secure scalar product and secure hamming distance computation to securely compute the desired criteria for shaping the clusters' scheme. The proposed approach covers all possible secure agglomerative hierarchical clustering construction when data is distributed between two (or more) parties, including both numerical and categorical data.

关键词： Privacy Hierarchical Clustering Secure Two-Party Computation Collaborative Clustering

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Local and Global Shared Memory for Task based HPC Applications on Heterogeneous Platforms 26

Local and Global Shared Memory for Task Based HPC Applicatio...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Liu, Chao Leeser, Miriam Northeastern Univ Dept ECE Boston MA 02115 USA

ISBN: (纸本)9781538649756

With the prevalence of multicore and manycore processors, developing parallel applications to benefit from massively parallel resources is important. In this work, we introduce a hybrid shared memory mechanism based on a high-level task design. We implemented task scoped global shared data based on the one-sided communication feature of MPI-3 and enable users to implement and create multi-threaded tasks that can execute either on a single node or on multiple nodes. Task threads of distributed nodes can share data sets through global shared data objects using one-sided remote memory access. We ported and developed a set of benchmark applications and tested on a cluster platform. The high-level task design and hybrid shared memory help users develop and maintain parallel programs easily, and the results show that the global shared data can deliver good RMA performance;the multi-threaded task implementations perform up to 20% faster than ordinary OpenMP programs and have better scaling performance than MPI programs on multiple nodes.

关键词： Task analysis Graphics processing units Electronics packaging parallel processing Programming Instruction sets Runtime

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Implementation of Bayesian inference in distributed neural networks 26

Implementation of Bayesian inference in distributed neural n...

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26th euromicro International conference on parallel, distributed, and network-based processing (PDP)

作者： Yu, Zhaofei Hang, Tiejun Liu, Jian K. Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Graz Univ Technol Inst Theoret Comp Sci Graz Austria

ISBN: (纸本)9781538649756

Numerous neuroscience experiments have suggested that the cognitive process of human brain is realized as probability reasoning and further modeled as Bayesian inference. It is still unclear how Bayesian inference could be implemented by neural underpinnings in the brain. Here we present a novel Bayesian inference algorithm based on importance sampling. By distributed sampling through a deep tree structure with simple and stackable basic motifs for any given neural circuit, one can perform local inference while guaranteeing the accuracy of global inference. We show that these task-independent motifs can be used in parallel for fast inference without iteration and scale-limitation. Furthermore, experimental simulations with a small-scale neural network demonstrate that our distributed sampling-based algorithm, consisting with our theoretical analysis, can approximate Bayesian inference. Taken all together, we provide a proof-of-principle to use distributed neural networks to implement Bayesian inference, which gives a road-map for large-scale Bayesian network implementation based on spiking neural networks with computer hardwares, including neuromorphic chips.

关键词： Bayesian inference distributed neural network importance sampling neural implementation

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