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Formalizing and Verifying Decentralized Systems with Extended Concurrent Separation Logic 20th

Formalizing and Verifying Decentralized Systems with Extende...

引用

20th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Ding, Yepeng Sato, Hiroyuki Univ Tokyo Tokyo Japan

ISBN: (纸本)9783030602451;9783030602444

Decentralized techniques are becoming crucial and ubiquitous with the rapid advancement of distributed ledger technologies such as the blockchain. Numerous decentralized systems have been developed to address security and privacy issues with great dependability and reliability via these techniques. Meanwhile, formalization and verification of the decentralized systems is the key to ensuring correctness of the design and security properties of the implementation. In this paper, we propose a novel method of formalizing and verifying decentralized systems with a kind of extended concurrent separation logic. Our logic extends the standard concurrent separation logic with new features including communication encapsulation, environment perception, and node-level reasoning, which enhances modularity and expressiveness. Besides, we develop our logic with unitarity and compatibility to facilitate implementation. Furthermore, we demonstrate the effectiveness and versatility of our method by applying our logic to formalize and verify critical techniques in decentralized systems including the consensus mechanism and the smart contract.

关键词： Decentralized system Extended concurrent separation logic Formal methods Consensus Smart contract

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Grouping Synchronous to Eliminate Stragglers with Edge Computing in distributed Deep Learning

Grouping Synchronous to Eliminate Stragglers with Edge Compu...

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

作者： Zhiyi Gui Xiang Yang Hao Yang Wei Li Lei Zhang Qi Qi Jingyu Wang Haifeng Sun Jianxin Liao The State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Huawei Beijing China

With the development of artificial intelligence(AI) applications, a large number of data are generated from mobile or IoT devices at the edge of the network. Deep learning tasks are executed to obtain effective information in the user data. However, the edge nodes are heterogeneous and the network bandwidth is limited in this case, which will cause general distributed deep learning to be inefficient. In this paper, we propose Group Synchronous parallel (GSP), which uses a density-based algorithm to group edge nodes with similar training speeds together. In order to eliminate stragglers, group parameter servers are responsible for coordinating communication of nodes in the group with Stale Synchronous parallel and aggregating the gradients of these nodes. And a global parameter server is responsible for aggregating the gradients from the group parameter servers to update the global model. To save network bandwidth, we further propose Grouping Dynamic Sparsification (GDS). It adjusts the gradient sparsification rate of nodes dynamically based on GSP so as to differentiates the communication volume and makes the training speed of all nodes tend to be the same. We evaluate GSP and GDS’s performance on LeNet-5, ResNet, VGG, and Seq2Seq with Attention. The experimental results show that GSP speedups the training by 45% ~ 120% with 16 nodes. GDS on top of GSP can make up for some test accuracy loss, up to 0.82% for LeNet-5.

关键词： Training Deep learning Bridges Scalability Clustering methods Bandwidth Life estimation

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Evaluation and Optimization of Neutral Grounding methods for High-Proportion Cable Distribution Networks Considering Wind Power Access

Evaluation and Optimization of Neutral Grounding Methods for...

引用

image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Pengbo Liu Xiaodong Lu Jiaxin Li School of Electrical Engineering Shandong University Jinan China Dezhou Power Supply Company State Grid Dezhou China School of Electrical Engineering Northeast Electric Power University Jilin China School of Economics and Management Northeast Electric Power University Jilin China

ISBN: (纸本)9781728190181;9781728190198

Considering large capacitance current of the high proportion cable distribution network, coupled with the influence of wind turbine access on the selection of the distribution network grounding, single-phase is only generated in the traditional wind-powered high-proportion cable distribution network through ASC grounding Ground fault grounding method is evaluated. Faced with the problems of traditional grounding methods, an optimized grounding scheme was proposed in which the neutral point of the distribution network was connected in parallel with a small resistance via an ASC and wind turbine variable grounding method was used. Theoretical analysis and simulation results show that the improved neutral grounding method is superior to the traditional grounding method in limiting the transient overvoltage of single-phase grounding faults. It can improve the power supply reliability of the distribution network and the equipment safety. The fault line selection is easier without affecting the operation of the wind turbine. It can be applied in the distribution network with a high proportion of the cable line connected by the wind turbine.

关键词： Resistance Grounding Power cables Computer network reliability Distribution networks Wind power generation Wind turbines

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Memory Access Optimization of High-Order CFD Stencil Computations on GPU 1

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21st International conference on parallel and distributed Computing, Applications, and Technologies, PDCAT 2020

作者： Wang, Shengxiang Li, Zhuoqian Che, Yonggang Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9783030692445

ISBN: (纸本)9783030692438

Stencils computations are a class of computations commonly found in scientific and engineering applications. They have relatively lower arithmetic intensity. Therefore, their performance is greatly affected by memory access. This paper studies the issue of memory access optimization for the key stencil computations of a high-order CFD program on the NVidia GPU. Two methods are used to optimize the performance. First, we use registers to cache the data used by the stencil computations in the kernel. We use the CUDA warp shuffle functions to exchange data between neighboring grid points, and adjust the thread computation granularity to increase the data reuse. Second, we use the shared memory to buffer the grid data used by the stencil computations in the kernel, and utilize loop tiling to reduce redundant accesses to the global memory. Performance evaluation is done on an NVidia Tesla K80 GPU. The results show that compared to the original implementation that only uses the global memory, the optimized implementation that utilizes the registers achieves a maximum speedup of 2.59 and 2.79 relatively for 15M and 60M grids, and the optimized implementation that utilizes the shared memory achieves a maximum speedup of 3.51 and 3.36 relatively for 15M and 60M grids. © 2021, Springer Nature Switzerland AG.

关键词： Graphics processing unit

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High-Resolution Range Profile Recognition Method of Vehicle Targets Based on Accelerated T-SNE with Multi-polarization Fusion

High-Resolution Range Profile Recognition Method of Vehicle ...

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IEEE International conference on Signal and image processing (ICSIP)

作者： Hao Wu Dahai Dai Penghui Ji Bo Pang Xuesong Wang State Key Laboratory of Complex Electromagnetic Environment Effects and Electronics and Information System National University of Defense Technology Changsha China College of Electronic Science National University of Defense Technology Changsha China

ISBN: (纸本)9781665446006

Polarization plays an important role in analyzing radar targets. Full polarization information has a better ability to inverse fine structures of the target. However, previous studies of high-resolution range profile (HRRP) mainly neglected effects of polarization. Therefore, a novel radar target recognition algorithm based on accelerated t-distributed Stochastic Neighbor Embedding(t-SNE) and multi-polarization weighted fusion has been proposed. Firstly, three single polarization channels, odd and even-bounce mechanism HRRP, and full polarization HRRP are described. The accelerated t-SNE method based on Barnes-Hut approximation improves the dimensionality reduction speed of processing HRRPs while maintaining the initial information of each HRRP. Also, it is capable of visualizing different targets. With the weighted fusion of posterior probabilities of multiple support vector machines (SVMs), we are able to realize recognition of targets. Experimental results of vehicle targets based on electromagnetic calculations show that recognition performance of this algorithm is superior to other methods, and it shows strong robustness under the condition of large azimuth angle.

关键词： Support vector machines Dimensionality reduction image recognition image resolution Target recognition Fuses Radar

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Standard Deviation Based Adaptive Gradient Compression For distributed Deep Learning 20

Standard Deviation Based Adaptive Gradient Compression For D...

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20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Chen, Mengqiang Yan, Zijie Ren, Jiangtao Wu, Weigang Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9781728160955

distributed deep learning has been proposed to train large scale neural network models with huge amounts of datasets using multiple workers. Since workers need to frequently communicate with each other to exchange gradients for updating parameters, communication overhead is always a major challenge in distributed deep learning. To cope with this challenge, gradient compression has been used to reduce the amount of data to be exchanged. However, existing compression methods, including both gradient quantization and gradient sparsification, either hurt model performance significantly or suffer from inefficient compression. In this paper, we propose a novel approach, called Standard Deviation based Adaptive Gradient Compression (SDAGC), which can simultaneously achieve model training with low communication overhead and high model performance in synchronous training. SDAGC uses the standard deviation of gradients in each layer of the neural network to dynamically calculate a suitable threshold according to the training process. Moreover, several associated methods, including residual gradient accumulation, local gradient clipping, adaptive learning rate revision and momentum compensation, are integrated to guarantee the convergence of the model. We verify the performance of SDAGC on various machine learning tasks: image classification, language modeling and speech recognition. The experiment results show that, compared with other existing works, SDAGC can achieve a gradient compression ratio from 433x to 2021x with similar or even better accuracy.

关键词： distributed deep learning gradient compression standard deviation neural networks parallel processing

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An Unstructured Array Camera System with Scalable Capability

An Unstructured Array Camera System with Scalable Capability

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International conference on Computational Intelligence and Security

作者： Shuo Li Lianshan Yan Tao Yao LuDong University Yantai China Yantai Research Institute of New Generation Information Technology Southwest Jiaotong University Yantai China School of Information Science and Technology Southwest Jiaotong University Chengdu China

ISBN: (纸本)9781665494908

Unstructured array camera systems break through the optical limitations of a single camera and blaze a new trail to high-performance imaging by combining cameras. However, existing solutions use an array of cameras with fixed focal lengths and the same hardware model, lacking flexibility and practicality. The previous works rely on known focal length values, and the image processing methods are too complex to generate ultra-high-resolution videos in real-time. Inspired by image matching and deep learning methods, we proposed an unstructured array camera system with scalable capability that can freely change the focal length of each camera. The proposed cross-scale image matching method based on feature matching can quickly and accurately search the positions of local-view cameras. The image warping algorithm based on deep neural networks can efficiently composite ultra-high-resolution videos in real-time. Furthermore, the color alignment based on the LAB format is used for addressing the problem of different brightness and color between cameras. Experimental results show that our proposed system can use any camera or phone to composite ultra-high-resolution videos in real-time with flexibility, robustness, and efficiency.

关键词： Deep learning image color analysis image matching parallel processing Cameras Real-time systems Hardware

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Solving Quadratic Assignment Problem Using Crow Search Algorithm in Accelerated Systems 2nd

Solving Quadratic Assignment Problem Using Crow Search Algor...

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2nd International conference on Machine Learning, image processing, Network Security and Data Sciences, MIND 2020

作者： Kumar, Manoj Mitra, Pinaki Department of Computer Science and Engineering IIT Guwahati Guwahati India

ISBN: (纸本)9789811563171

The Quadratic Assignment problem (QAP) is one of the most studied optimization problem. Although many direct and heuristic methods are used to give the solution of QAP for small size instances in reasonable time but it takes huge time for large size instances. So, solving QAP in massively parallel architecture like Graphics processing unit (GPU) by applying a noble metaheuristics Crow Search Algorithm (CSA) can further optimize the solution and their execution time. So, in this paper we analyse the QAP in accelerated systems by using CSA metaheuristics and CSA performs approximately 10 times faster on GPU as compared to CPU. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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FRD: Few-shot rumor detection for the novel coronavirus pneumonia epidemic

FRD: Few-shot rumor detection for the novel coronavirus pneu...

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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： Danroujing Chen Xiaoliang Chen Xuyang Wang Weicheng Wang Xiaoyu Dong School of Computer and Software Engineering Xihua University Chengdu China

With the full arrival of the digital era, fueled by both information technology and business marketing, rumors are produced and spread endlessly on social networks. During the recent novel coronavirus pneumonia epidemic, online rumors have continued to flourish. Most existing studies on traditional rumor detection rely on a large number of features in practical applications. However, the current severe epidemic scenarios have limited rumor information features, and it remains a challenging problem to detect epidemic rumors with high accuracy using only limited information. As a result, we propose a novel Few-shot Rumor Detection model (FRD) for the novel coronavirus pneumonia, which is combined with meta-learning to be able to accurately identify rumors as soon as possible in crises. Specifically, we started by using the BERT+BiLSTM combination for rumor text feature extraction and representation to generate the historical rumor sample-wise vector and epidemic rumor sample-wise vector; secondly, the prototypical network was introduced to summarize the historical rumor data, and the feature vectors of samples belonging to the same category were averaged to obtain the prototype representation of historical rumor category; finally, we utilize the modified cosine similarity measure function to calculate the distance between the class-wise vector of historical rumor text and the sample-wise vector of epidemic rumor, and complete the rumor detection according to the nearest neighbor method. Our experimental results on English datasets show that the FRD rumor detection model proposed in this paper is superior to other baseline algorithms in terms of accuracy, precision, recall and macro F1 value. From the comparison of experimental results, the FRD model can effectively improve conventional rumor detection methods, and better realize the early detection of sudden epidemic rumors.

关键词： COVID-19 Epidemics Social networking (online) Pulmonary diseases Computational modeling Prototypes Nearest neighbor methods

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Accelerating Finite Impulse Response Filtering Using Tensor Cores

Accelerating Finite Impulse Response Filtering Using Tensor ...

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Asia-Pacific Signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Takumi Kondo Yoshihiro Maeda Norishige Fukushima Nagoya Institute of Technology Japan Tokyo University of Science Japan

ISBN: (纸本)9781665441629

This paper studies how to accelerate a single channel 2D image convolution using NVIDIA's Tensor Core. Tensor Core is a dedicated arithmetic unit for speeding up matrix products and was proposed to speed up the convolution in machine learning. From the Volta architecture of NVIDIA's GPU, Tensor Core is utilized for various applications. The Tensor Core is not limited to machine learning but various applications. This paper focus acceleration of image convolution of a single-channel filter of finite impulse response (FIR) filtering. Usually, Tensor Core is used for multi-channel convolution; thus, direct usage of Tensor Core cannot maximize the performance of the usual FIR filtering. Therefore, we propose three programming patterns for parallel processing of FIR filtering: kernel-loop unrolling, kernel-image-loop unrolling, and diagonal kernel-image-loop unrolling. Our experiments compare the proposed loop unrolling methods for linear time-invariant (LTI) and variant filters (LTV): Gaussian filtering and bilateral filtering. The results show that Tensor Core can accelerate the convolution for LTI filters but cannot accelerate linear LTV filters. Also, we compare the two GPU architectures of Turing and Ampere, and we can accelerate the LTI filter using TCs in both architectures.

关键词： Linear systems Maximum likelihood detection Tensors Finite impulse response filters Filtering Convolution Graphics processing units

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