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Entropy-based gradient compression for distributed deep learning 21

Entropy-based gradient compression for distributed deep lear...

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21st IEEE international Conference on high performance computing and Communications, 17th IEEE international Conference on Smart City and 5th IEEE international Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Kuang, Di Chen, Mengqiang Xiao, Danyang Wu, Weigang School of Data and Computer Science Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781728120584

Nowadays, with the increasing amount of data and scale of network models, distributed deep learning, i.e., training a deep neural network using multiple workers distributed across different computing nodes, is becoming more and more popular. One of the major challenges of distributed deep learning lies in the frequent communication of gradients among workers, because it may cause severe bottlenecks, in terms of time latency and bandwidth. In this paper, we propose a novel approach named Entropy-based Gradient Compression (EGC) to reduce communication overhead. The major components of EGC are two algorithms: the entropy-based threshold selection algorithm and the automatic learning rate correction algorithm. To improve the accuracy, EGC also includes two commonly used algorithms: gradient residual and momentum correction. To evaluate the performance of EGC, we conduct experiments based on applications of image classification and language modeling using public datasets including Cifar10, Tiny ImageNet and Penn Treebank. The experiment results show that, compared with existing works, EGC can achieve a gradient compression ratio about 1000× while keeping the accuracy similar or even higher. © 2019 IEEE.

关键词： Entropy

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Accelerating All-Electron Ab initio Simulation of Raman Spectra for Biological Systems

Accelerating All-Electron Ab initio Simulation of Raman Spec...

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Supercomputing Conference

作者： Honghui Shang Fang Li Yunquan Zhang Ying Liu Libo Zhang Mingchuan Wu Yangjun Wu Di Wei Huimin Cui Xin Liu Fei Wang Yuxi Ye Yingxiang Gao Shuang Ni Xin Chen Dexun Chen SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China National Supercomputer Center in Wuxi Wuxi China Tsinghua University Beijing China Institute of Computing Technology Chinese Academy of Sciences UCAS Beijing China University of Science and Technology of China Hefei

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

Raman spectroscopy provides chemical and compositional information that can serve as a structural fingerprint for various materials. Therefore, simulations of Raman spectra, including both quantum perturbation analyses and ground-state calculations are of significant interest. However, highly accurate full quantum mechanical (QM) simulations of Raman spectra have previously been confined to small systems. For large systems such as biological materials, the computational cost of full QM simulations is extremely high, and their extension to such systems remains challenging. In the work described here, by employing robust new algorithms and advances in implementation for the many-core architectures, we are able to perform fast, accurate, and massively parallel full ab initio simulations of the Raman spectra of biological systems with excellent strong and weak scaling, thereby providing a starting point for applying QM approaches to structural studies of such systems.

关键词： Proteins Technological innovation Codes Sensitivity Biological system modeling Computational modeling Perturbation methods

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Topology Awareness for Smart 5G eMBB Network Slicing VNF Placement

Topology Awareness for Smart 5G eMBB Network Slicing VNF Pla...

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IEEE international symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Ignacio Sanchez-Navarro Jose M. Alcaraz Calero Qi Wang School of Computing Engineering and Physical Sciences University of the West of Scotland Paisley United Kingdom

ISBN: (数字)9781728173740

ISBN: (纸本)9781728173757

This paper presents an architecture to gather nontraditional metrics from 5G multi-tenant infrastructure using information about the network topology to take smart decisions on where to optimal placement VNFs that are used to provide services to network slices. The metrics considered are spatial metrics, where information about the shape and size of the network topology is taken into consideration. The architecture has been prototypical validated showing how optimal decisions are taken in an eMBB high-dense scenario, with topologies up to 65538 mobile users geographically concentrated on the same location. Our prototype is able to deal with the calculation of such spatial metrics over the 5G multi-tenant network with 65538 mobile users within 20 seconds, which make it viable at operational phase.

关键词： 5G mobile communication Measurement Network topology Topology computer architecture Resource management Shape

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Path-based routing and wavelength assignment for multiple multicasts in optical network-on-chip 21

Path-based routing and wavelength assignment for multiple mu...

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作者： Yang, Wen Chen, Yawen Huang, Zhiyi Zhang, Haibo Gu, Huaxi Department of Computer Science University of Otago Dunedin9016 New Zealand State Key Laboratory of ISN Xidian University Xi'an710071 China

ISBN: (纸本)9781728120584

Optical Network-on-Chip (ONoC) is emerging as a key on-chip communication architecture for multiprocessor systems. While multicast communication commonly exists in various parallel applications for Chip Multi-Processors (CMPs), most existing studies for multicast in an ONoC only consider the optimization of one multicast. This limits the practical applications because real systems often have to handle multiple multicasts requested from various applications. In this paper, we investigate the wavelength assignment strategies for multiple multicasts based on path-based routing in an ONoC, with the objective of reducing the number of wavelengths required. We first propose an intra-multicast wavelength assignment strategy including two different wavelength assignment methods, Multicast with Single Wavelength (MSW) and Multicast with Multiple Wavelengths (MMW), which are two configurations by assigning a single wavelength or multiple wavelengths to the same multicast, respectively. Then, two modified conflict graphs are proposed to evaluate the wavelength requirements of MSW and MMW. By analyzing the distribution of different multicasts, we further design a Layer-based Wavelength Assignment algorithm for Multiple Multicasts (LWAMM) to implement the inter-multicast wavelength assignment. The key idea is to allocate non-overlapping paths to the same layer (a detailed explanation will be given in Section IV) that is logically duplicated from the given ONoC, and assign the same wavelength to the paths in the same layer. Since the number of wavelengths is equal to the number of layers, it can be reduced by allocating all paths to as few layers as possible. Finally, simulation results verify our theoretical results, and show that LWAMM can reduce the average number of wavelengths used by 11.31%, 15.1%, and 17.7%, respectively, when the multicast ratios are 30%, 50%, and 90%, compared to other path-based schemes. © 2019 IEEE.

关键词： Network-on-chip

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Parallel DNA computing Model of Point-Doubling in Conic Curves Cryptosystem over Finite Field GF(2n) 21

Parallel DNA Computing Model of Point-Doubling in Conic Curv...

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作者： Li, Yongnan Xiao, Limin School of Criminal Investigation and Counter Terrorism People's Public Security University of China Beijing100038 China School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781728120584

DNA cryptography becomes a burgeoning new area of study along with the fast-developing of DNA computing and modern cryptography. Point-doubling, point-addition and point-multiplication are three fundamental point-operations to construct encryption protocols in some cryptosystem over mathematical curves such as elliptic curves and conic curves. This paper proposes a DNA computing model to calculate point-doubling in conic curves cryptosystem over finite field GF(2n). By decomposing and rearranging the computing steps of point-doubling, the assembly process could be fulfilled by using 8 different types of computation tiles performing different functions with 1097 encoding ways. This model could also figure out point-multiplication if its coefficient is 2k. The assembly time complexity is (2k+1)n-k-1, and the space complexity is (kn)2+k(n2)-(k2)n. © 2019 IEEE.

关键词： Cryptography

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Communication-aware and energy saving virtual machine allocation algorithm in data center 21

Communication-aware and energy saving virtual machine alloca...

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作者： Luo, Juan Fan, Xin Yin, Luxiu College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728120584

Data centers can provide users with reliable and on-demand cloud services. Efficient virtual machine allocation strategies are important for improving data center performance and increasing allocation efficiency. However, the existing allocation algorithms have limited consideration for the communication delay between virtual machines caused by the data center topology. Therefore, this study formalizes the network communication problem of virtual machines in data centers by studying these centers network topology. Accordingly, this paper proposes an energy saving allocation algorithm based on communication-aware, called ESBCA, which optimizes the data center traffic from the two stages of virtual machine migration and placement. In the migration phase, virtual machines with high traffic volumes are preferred to be migrated. In the placement stage, different targets are set and the destination physical machine is systematically determined. Experimental results show that the proposed algorithm evidently reduces the data traffic, improves the efficiency of the data center migration and decreases the energy consumption of data centers. © 2019 IEEE.

关键词： Virtual machine

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Image super-resolution model based on bypass convolution and feature weighting 21

Image super-resolution model based on bypass convolution and...

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作者： Pan, Zhenzhong Zhang, Yu Guo, Xing School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781728120584

Image super-resolution(SR) reconstruction aims at reconstructing corresponding high-resolution images from low-resolution images. Methods based on deep CNN have achieved distinguished performance recently. Among these methods, those based on the single-layer features cannot get top reconstruction results since shallow features are ignored, and those based on multi-layer features need to discriminate good and poor features. To address this issue, we propose a new model, BySEDenseSR (Bypass Convolution Squeeze Excitation Dense). Specifically, We use a squeeze excitation block that learns to assign appropriate weights based on the quality of features. This provides an effective way to suppress the effects of poor features. Furthermore, using bypass convolution, BySEDenseSR reduces the forwarding noise, which improves the stability of middle-layer features. Experiments show that compared to LapSRN, BySEDenseSR improves its peak signal-to-noise ratio (PSNR) by 0.16dB, 0.08dB and 0.03dB respectively on Set5, Set14 and BSD100, at a magnification of 4×. © 2019 IEEE.

关键词： Convolution

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Federated edge computing for disaster management in remote smart oil fields 21

Federated edge computing for disaster management in remote s...

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作者： Hussain, Razin Amini, Mohsen Kovalenko, Anna Feng, Yin Semiari, Omid Laboratory School of Computing and Informatics United States Department of Petroleum Engineering University of Louisiana at Lafayette LafayetteLA United States Department of Electrical and Computer Engineering Georgia Southern University StatesboroGA United States

ISBN: (纸本)9781728120584

Oil & Gas (O&G) industry is extending the extraction operation to remote offshore sites. Cost-effective, efficient, and nature-friendly oil extraction is a challenging issue in these remote sites, due to the disaster-prone nature of oil extraction process and hurdles in accessing these sites. To overcome these difficulties, smart oil fields use numerous sensors (e.g., pipeline pressure, gas leakage, temperature sensors) and can generate more than a terabyte of data per day. The data are transferred to cloud datacenters via high-latency and unstable satellite communication, which is not suitable for latency-intolerant (urgent) disaster-related tasks. Edge computing can be deployed in oil rigs to process the latency-intolerant tasks, however, processing capacity of an edge system falls short at the time of a disaster - when several coordinated activities must be processed within a short time. To address this shortage, we propose robust smart oil fields operating based on a federation of edge computing systems, provisioned from nearby/mobile micro datacenters. Our solution achieves robustness by capturing uncertainties exist both in communication and computation of the federated environment and allocating urgent tasks so that the likelihood of their on-time completion is maximized. Evaluation results reflect significant performance improvement (up to 27%) of the proposed solution when compared to conventional solutions for smart oil fields. © 2019 IEEE.

关键词： Extraction

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Optimization of a network retrograde analysis system implemented on HDFS 21

Optimization of a network retrograde analysis system impleme...

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作者： Yuan, Xiaofang Zhang, Wubin College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728120584

The Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. In addition, it provides a distributed file system (HDFS) that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster. However, the problem of small-file storage in Big data is restricted by the ability of HDFS to work effectively, as it is originally designed to deal with large files. In order to construct a network retrograde analysis system (RAS), we propose a method to optimize the performance of HDFS for small file storage. In this system, a large number of network packet (pcap) files are stored in HDFS, each 40 to 50 MB in size. To reduce the number of files, pcap files are merged into larger files, meanwhile indexes for each larger file are also generated. A distributed caching index strategy is introduced to improve the read speed of pcap files in HDFS. The experimental results show that our system can improve the access efficiencies of small files in HDFS, while reducing the NameNode memory consumption by about 90 compared to the original HDFS, and 70% to Hadoop Archive program (HAR), respectively. The access time per MB file is reduced remarkly, by about 50% lower than the original HDFS, and 70% than that of HAR. © 2019 IEEE.

关键词： Digital storage

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Memory-efficient GPU-based exact and parallel triangle counting in large graphs 21

Memory-efficient GPU-based exact and parallel triangle count...

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作者： Ouyang, Zhiyou Wu, Shanni Zhao, Tongtong Yue, Dong Zhang, Tengfei Institute of Advanced Technology School of Automation Nanjing University of Posts and Telecommunications Nanjing210003 China School of Computer Nanjing University of Posts and Telecommunications Nanjing China Institute of Advanced Technology School of Automation Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications Nanjing China School of Automation Jiangsu Engineering Laboratory of Big Data Analysis and Control for Active Distribution Network Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781728120584

Triangle counting is one of the basic research topics in many practical problems, such as clustering coefficients, transitivity ratio and traffic network complexity, etc., all of which can be converted into triangle counting issues. In this paper, a memory efficient GPU-based parallel triangle counting algorithm is proposed for large undirected graphs, which based on Polak's parallel forward triangle counting algorithm and improved the algorithm flow, edges sorting and triangle counting kernel function to increase the memory usage efficiency and run time performance. Algorithm comparison with the description of proposed algorithm steps and the implementation details is also introduced in the paper. performance evaluations show that the proposed algorithm achieves 5 to 10 times speedup over the existing parallel forward algorithm, moreover, the GPU memory usage is reduced to a maximum of 50% compared to the existing algorithm. © 2019 IEEE.

关键词： Graphics processing unit

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