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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1001-1010 订阅

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ROCK-CNN: a distributed RockPro64-based Convolutional Neural network Cluster for IoT. Verification and Performance Analysis 26

ROCK-CNN: a Distributed RockPro64-based Convolutional Neural...

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26th conference of Open Innovations Association FRUCT

作者： Khaydarova, Rezeda Fishchenko, Vladislav Mouromtsev, Dmitriy Shmatkov, Vladislav Lapaev, Maksim ITMO Univ St Petersburg Russia TPP Lab LTD St Petersburg Russia

ISBN: (纸本)9789526924427

The paper is dedicated to optimization of machine learning and neural networks applications by replacing common servers with Single Board Computer (SBC) clusters to minimize mounting and service expenses, simplify node mounting process and organize parallel computing in IoT applications. Authors focus on former experience of using distributed computing, mainly, light-weight and cost-optimized SBCs to classify use cases, then, choose an appropriate hardware platform enabling sufficient data processing and easy hot-replacement of nodes. This task requires organizing an efficient software architecture to make use of advantages of SBCs. A comparison for various SBSs is presented. Authors suggest their formerly-designed architecture with changes allowing using it for neural network applications. Authors pay attention to thorough parameter examination based on numerous tests. Parameter timelines are presented. The paper describes a number of test-cases to validate the efficiency of suggested architecture based on common use-cases. Performance analysis and cluster scalability potential estimation are conducted as well to estimate an efficient number of nodes required for future tasks.

关键词： Mountings

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Evaluating the performance and improving the usability of parallel and distributed Word Embeddings tools

Evaluating the performance and improving the usability of pa...

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euromicro conference on parallel, distributed and network-based processing

作者： Matheus L. da Silva Vinícius Meyer Dionatrã F. Kirchoff F. S. Joaquim Neto Renata Vieira A. F. César De Rose Polytechnic School Pontifical Catholic University of Rio Grande do Sul (PUCRS) Porto Alegre RS Brazil

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

The representation of words by means of vectors, also called Word Embeddings (WE), has been receiving great attention from the Natural Language processing (NLP) field. WE models are able to express syntactic and semantic similarities, as well as relationships and contexts of words within a given corpus. Although the most popular implementations of WE algorithms present low scalability, there are new approaches that apply High-Performance Computing (HPC) techniques. This is an opportunity for an analysis of the main differences among the existing implementations, based on performance and scalability metrics. In this paper, we present a study which addresses resource utilization and performance aspects of known WE algorithms found in the literature. To improve scalability and usability we propose a wrapper library for local and remote execution environments that contains a set of optimizations such as the pWord2vec, pWord2vec MPI, Wang2vec and the original Word2vec algorithm. Utilizing these optimizations it is possible to achieve an average performance gain of 15x for multicores and 105x for multinodes compared to the original version. There is also a big reduction in the memory footprint compared to the most popular python versions.

关键词： Optimization Scalability Libraries Training Usability Natural language processing Computer architecture

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SFTA: Spiking Neural networks Vulnerable to Spiking Feature Transferable Attack

SFTA: Spiking Neural Networks Vulnerable to Spiking Feature ...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xuanwei Lin Chen Dong Ximeng Liu College of Computer and Data Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing (Fuzhou University) Key Lab of Information Security of Network Systems (Fuzhou University)

Many recent works have shown that spiking neural networks (SNNs) are vulnerable to well-crafted adversarial attacks in the white-box setting, which may lead to misclassification of the trained model. However, the information about the model is in a black-box setting and is usually unknown to the attacker, e.g., the parameters and structure of the model. So it is more challenging to counter the attack in the black-box setting, and there is still little research on this aspect in SNNs. Therefore, this work describes a transfer-based black box adversarial attack against spike feature space representations, providing insights for robust spike features in SNNs models. The attack is explicitly designed to be transferable, and more robust feature gradients are obtained by accumulating the feature gradients of the intermediate layers. Then we can successfully attack the black-box model by suppressing robust positive features and enhancing negative features. Extensive experimental evaluation demonstrates the proposed method’s effectiveness and superior performance on static and neuromorphic datasets. i.e., The attack success is 89.72% and 90% on the static MNIST and CIFAR10 datasets, and 85.97% and 69.09% on the neuromorphic N-MNIST and CIFARO-DVS datasets.

关键词： Correlation Neuromorphics Neural networks Closed box Ubiquitous computing Optimization Glass box

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Understanding the Resource Demand Differences of Deep Neural network Training 19th

Understanding the Resource Demand Differences of Deep Neural...

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19th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Du, Jiangsu Zhu, Xin Hu, Nan Du, Yunfei Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9783030389611;9783030389604

More deep neural networks (DNN) are deployed in the real world, while the heavy computing demand becomes an obstacle. In this paper, we analyze the resource demand differences of DNN training and help understand its performance characteristic. In detail, we study both shared-memory and message-passing behavior in distributed DNN training from layer-level and model-level perspectives. From layer-level perspective, we evaluate and compare basic layers' resource demand. From model-level perspective, we measure parallel training of representative models then explain the causes of performance differences based on their structures. Experimental results reveal that different models vary in resource demand and even a model can have very different resource demand with different input sizes. Further, we give out some observations and recommendations on performance improvement of on-chip training and parallel training.

关键词： Deep neural network training Performance Resource demand differences

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Optimal GPU-CPU Offloading Strategies for Deep Neural network Training 26th

Optimal GPU-CPU Offloading Strategies for Deep Neural Networ...

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26th International conference on parallel and distributed Computing (Euro-Par)

作者： Beaumont, Olivier Eyraud-Dubois, Lionel Shilova, Alena Inria Bordeaux Sud Ouest Bordeaux France Univ Bordeaux Bordeaux France

ISBN: (纸本)9783030576752;9783030576745

Training Deep Neural networks is known to be an expensive operation, both in terms of computational cost and memory load. Indeed, during training, all intermediate layer outputs (called activations) computed during the forward phase must be stored until the corresponding gradient has been computed in the backward phase. These memory requirements sometimes prevent to consider larger batch sizes and deeper networks, so that they can limit both convergence speed and accuracy. Recent works have proposed to offload some of the computed forward activations from the device memory to the main memory and requires to determine which activations should be offloaded and when these transfers should take place. We prove that this problem is NP-complete in the strong sense, and propose two heuristics based on relaxations of the problem. We then conduct a thorough experimental evaluation of standard deep neural networks.

关键词： Memory management Deep Neural network Dynamic programming Scheduling

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An Energy-balanced Routing by Considering Neighbor Node’s Energy based on Small World WSN

An Energy-balanced Routing by Considering Neighbor Node’s E...

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

作者： Linlin Liu Minghua Wang Yan Wang The Hunan Province Key Laboratory for Ultra-Fast Micro/Nano Technology and Advanced Laser Manufacture School of Electrical Engineering University of South China Hengyang China

Wireless sensor network (WSN) is widely used in military, environmental monitoring, modern agriculture and other scenarios. Therefore, how to ensure the energy balance, improve the utilization rate of nodes, and prolong the network lifetime is the focus of the whole research. To solve this problem, we propose an energy-balanced routing by considering neighbor nodes energy based on small world WSN (EN-SWSN). Small world network has smaller average path length and larger clustering coefficient. A new link is created between the node and Sink according to the connection probability and the energy level of the neighbor node in WSN for data transmission based on small world network. The introduction of small world network into WSN can reduce the communication cost of the whole network, improve the data processing efficiency and prolong the network lifetime. Simulation results explain that this algorithm has incomparable advantages in energy balance and network lifetime extension compared with the other two algorithms.

关键词： Wireless sensor networks Energy consumption Simulation Clustering algorithms Routing Data processing Energy states

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Learning Heterogeneous DAG Tasks Scheduling Policies With Efficient Neural network Evolution

Learning Heterogeneous DAG Tasks Scheduling Policies With Ef...

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

作者： Weiyu Tan Tian Ling Zhenyu Zeng Ying Tao Yuxia Cheng School of Computer Science Hangzhou Dianzi University Hangzhou China

Neural networks based on NeuroEvolution of Augmenting Topology (NEAT) attracted much attention owing to multiple novel features. It is expected to develop optimal net structure adaptively through evolution and applied to solve specific problems. However, the difficulty of indicating problem status and determining evolution direction along with long execution time makes NEAT hard to show its superiority in many applications. This paper proposed a new DAG scheduling algorithm NeuroSched based on NEAT and parallel Reinforcement Learning. In Markov Decision Processes we use evolved network as a bridge between State of DAG and Action of strategy, and guide network’s evolution direction through Fitness which is calculated by Reward, both of which are under parallel RL environment. Experimental results show that our algorithm is superior to contrasted algorithms in multiple indicators like Makespan and SLR on different scales of DAG, and the parallel process greatly reduces execution time compared with previous evolutionary algorithms.

关键词： Training Scheduling algorithms network topology Neural networks Reinforcement learning Evolutionary computation Markov processes

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Pervasive Container Computing–––a Fine-Grained Loosely Coupled Cloud Cluster

Pervasive Container Computing–––a Fine-Grained Loosely Co...

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IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Xinhong Hei Dan Wu YiFan Liu Wenjiang Ji Yichuan Wang Lei Zhu School of computer science and engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China

With the emergence of CaaS, a cloud service model that can quickly build a production environment, this model has been widely used in the industry. However, the degree of loose coupling of various containers in the cluster and the fine-grained level of function division are often difficult to achieve people’s expectations. In addition, it is difficult to achieve the desired effect in terms of cloud cluster security. Therefore, this paper takes container-based cloud computing clusters as the main research object, and proposes a fine-grained, loosely coupled cloud cluster model based on containers, is called Pervasive-container computing. The experimental results show that Pervasive-container computing not only meets the fine-grained loose coupling characteristics required by microservices, but also adopts the strategy of “created with time” and “destroy after use”, which provides guarantees in terms of endogenous security. In addition, the proposed scheduling algorithm based on Pervasive-container calculation reduces the time consumption of file transfer by 20% compared to the time consumption without the algorithm.

关键词： Couplings Industries Cloud computing Scheduling algorithms Computational modeling Microservice architectures Production

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Efficient On-Chip Multicast Routing based on Dynamic Partition Merging

Efficient On-Chip Multicast Routing based on Dynamic Partiti...

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euromicro conference on parallel, distributed and network-based processing

作者： Binayak Tiwari Mei Yang Yingtao Jiang Xiaohang Wang Department of Electrical and Computer Engineering University of Nevada Las Vegas Las Vegas USA School of Software Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

networks-on-chips (NoCs) have become the mainstream communication infrastructure for chip multiprocessors (CMPs) and many-core systems. The commonly used parallel applications and emerging machine learning-based applications involve a significant amount of collective communication patterns. In CMP applications, multicast is widely used in multithreaded programs and protocols for barrier/clock synchronization and cache coherence. Multicast routing plays an important role on the system performance of a CMP. Existing partition-based multicast routing algorithms all use static destination set partition strategy which lacks the global view of path optimization. In this paper, we propose an efficient Dynamic Partition Merging (DPM)-based multicast routing algorithm. The proposed algorithm divides the multicast destination set into partitions dynamically by comparing the routing cost of different partition merging options and selecting the merged partitions with lower cost. The simulation results of synthetic traffic and PARSEC benchmark applications confirm that the proposed algorithm outperforms the existing path-based routing algorithms. The proposed algorithm is able to improve up to 23% in average packet latency and 14% in power consumption against the existing multipath routing algorithm when tested in PARSEC benchmark workloads.

关键词： Routing Partitioning algorithms Heuristic algorithms Merging Unicast System recovery Protocols

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Hybrid power flow calculation of AC-DC flexible interconnected system in distribution network

Hybrid power flow calculation of AC-DC flexible interconnect...

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Sustainable Power and Energy conference (iSPEC)

作者： Yulin Zhang Jiancui Zhang Guojia Liu Nari Group Corporation Nanjing China State Grid Sichuan Electric Power Aba Power Supply Company Chengdu China

ISBN: (纸本)9781665414401

based on the AC/DC flexible interconnected system of distribution network, it is necessary to analyze the impact of AC side on DC side and DC side on AC side, and consider the impact of load fluctuation or photovoltaic access, so as to comprehensively analyze the power flow distribution of AC system and DC system. In this paper, based on the graph database to establish the distribution network diagram power flow calculation related equipment model, using the basic principle of graph parallel processing model, based on Newton Raphson method, the alternating iteration method is used to realize the design of AC and DC diagram power flow calculation algorithm. based on the distributed graph computing cluster system, the online AC/DC hybrid power flow calculation program is developed, and the online real-time operation of AC/DC hybrid power flow calculation is realized. The function of the software is reliable and simple, which can fully meet the needs of the project, so as to solve the practical problem of power flow analysis and calculation in the actual project..

关键词： Computational modeling Heuristic algorithms Distribution networks Interconnected systems parallel processing Hybrid power systems Real-time systems

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