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Design of Power Data Encryption Speed Improvement Scheme Based on SM4

Design of Power Data Encryption Speed Improvement Scheme Bas...

引用

Power systems and Electrical Technology (PSET), IEEE international Conference on

作者： Yuqing Luo Jingxiang Feng Ruijue Feng School of advanced manufacturing Guangdong Polytechnic of Environmental Protection Engineering Foshan China School of Automotive Engineering Foshan Polytechnic Foshan China School of Electrical Engineering Guangzhou City University of Technology Guangzhou China

Electric power data contains rich private information, and encryption technology is often used to protect its information security. Due to its huge data volume, considering the lightweight and high-efficiency encryption algorithm, based on the encryption algorithm SM4 with independent intellectual property rights in my country, combined with the big data technology Hadoop, a parallel encryption speed-up scheme based on the national encryption algorithm SM4 is designed to realize the data encryption speed. Encryption and decryption protection and acceleration of computation. Finally, an experimental platform based on Hadoop is built, and the data is encrypted in blocks with the MapReduce computing framework, and the feasibility test of the scheme is carried out. The experimental results show that the algorithm scheme can improve the encryption speed of unstructured data files. The optimal number of Maps in the established cluster is 8, and the speedup ratio between serial encryption and parallel encryption is more than 2. Compared with serial encryption, it can save 50% of computing overhead time.

关键词： distributed databases Information security Clustering algorithms Intellectual property Network security Big Data Encryption

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D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning

D-Cliques: Compensating for Data Heterogeneity with Topology...

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Reliable distributed systems

作者： Aurélien Bellet Anne-Marie Kermarrec Erick Lavoie Inria Lille France EPFL Switzerland

ISBN: (数字)9781665497534

ISBN: (纸本)9781665497541

The convergence speed of machine learning models trained with Federated Learning is significantly affected by heterogeneous data partitions, even more so in a fully decentralized setting without a central server. In this paper, we show that the impact of label distribution skew, an important type of data heterogeneity, can be significantly reduced by carefully designing the underlying communication topology. We present D-Cliques, a novel topology that reduces gradient bias by grouping nodes in sparsely interconnected cliques such that the label distribution in a clique is representative of the global label distribution. We also show how to adapt the updates of decentralized SGD to obtain unbiased gradients and implement an effective momentum with D-Cliques. Our extensive empirical evaluation on MNIST and CIFAR10 validates our design and demonstrates that our approach achieves similar convergence speed as a fully-connected topology, while providing a significant reduction in the number of edges and messages. In a 1000-node topology, D-Cliques require 98% less edges and 96% less total messages, with further possible gains using a small-world topology across cliques.

关键词： Network topology Federated learning Scalability Computational modeling distributed databases Data models Topology

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Deinsum: Practically I/O Optimal Multi-Linear Algebra

Deinsum: Practically I/O Optimal Multi-Linear Algebra

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Supercomputing Conference

作者： Alexandros Nikolaos Ziogas Grzegorz Kwasniewski Tal Ben-Nun Timo Schneider Torsten Hoefler Department of Computer Science ETH Zurich Zurich Switzerland NextSilicon Tel Aviv Israel

ISBN: (纸本)9781665454452

Multilinear algebra kernel performance on modern massively-parallel systems is determined mainly by data movement. However, deriving data movement-optimal distributed schedules for programs with many high-dimensional inputs is a notoriously hard problem. State-of-the-art libraries rely on heuristics and often fall back to suboptimal tensor folding and BLAS calls. We present Deinsum, an automated framework for distributed multilinear algebra computations expressed in Einstein notation, based on rigorous mathematical tools to address this problem. Our framework automatically derives data movement-optimal tiling and generates corresponding distributed schedules, further optimizing the performance of local computations by increasing their arithmetic intensity. To show the benefits of our approach, we test it on two important tensor kernel classes: Matricized Tensor Times Khatri-Rao Products and Tensor Times Matrix chains. We show performance results and scaling on the Piz Daint supercomputer, with up to 19x speedup over state-of-the-art solutions on 512 nodes.

关键词： Schedules Tensors Algebra High performance computing distributed databases Supercomputers Libraries

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A distributed Implementation of Signature Matching Algorithms for IoT Intrusion Detection 5th

A Distributed Implementation of Signature Matching Algorithm...

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5th international Conference on ICT for Sustainable Development, ICT4SD 2020

作者： Domb, Menachem Ashkelon Academy College Ashkelon Israel

ISBN: (纸本)9789811582882

Common software systems manage large databases and huge incoming and outgoing data. A major security risk is the intrusion of viruses via devices connected to the system network. Each virus has a unique signature. Early detection of these signatures is the core of most anti-virus software packages. Each anti-virus uses a multi-string-matching algorithm to detect the existence of a virus signature in the incoming data or a given data file. String matching uses pre-defined signature lists, which are growing fast and may contain a few million entries. Studies show that anti-viruses may have an impact on the data transmission performance and propose ways to cope with it either by enhancing the processing power, improvements to the matching algorithms and apply selective detection with low-risk probability. The introduction of the Internet of things (IoT) exposes the Internet to major security risks as numerous devices are expected to be connected to the Internet. IoT devices have poor processing resources, and it is not able to run himself proper anti-virus software and so, allowing malicious virus attacks without any way to prevent it. Some solutions propose incorporating in the IoT environment a powerful security gateway processor to handle the anti-virus aspect. This solution is against the concept of IoT as an independent entity able to cope with such challenges. This paper presents a hierarchical distributed and parallel processing framework that accelerates multi-string matching for IoT devices by utilizing the accumulated excess processing power of the local IoT devices network, eliminating the need for high capacity robust computers. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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Non-Asymptotic Performance Analysis of Size-Based Routing Policies 28

Non-Asymptotic Performance Analysis of Size-Based Routing Po...

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28th IEEE international symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication systems (IEEE MASCOTS)

作者： Bachmat, Eitan Doncel, Josu Ben Gurion Univ Negev Dept Comp Sci IL-84105 Beer Sheva Israel Univ Basque Country UPV EHU Dept Math Leioa 48940 Spain

ISBN: (纸本)9781728192383

We investigate the performance of two size-based routing policies: the Size Interval Task Assignment (SITA) and Task Assignment based on Guessing Size (TAGS). We consider a system with two servers and Bounded Pareto distributed job sizes with tail parameter 1 where the difference between the size of the largest and the smallest job is finite. We show that the ratio between the mean waiting time of TAGS over the mean waiting time of SITA is unbounded when the largest job size is large and the arrival rate times the largest job size is less than one. We provide numerical experiments that show that our theoretical findings extend to Bounded Pareto distributed job sizes with tail parameter different to 1.

关键词： Size-based Routing parallel Servers Heavy-tailed distributions

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Analyzing Search Techniques for Autotuning Image-based GPU Kernels: The Impact of Sample Sizes

Analyzing Search Techniques for Autotuning Image-based GPU K...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Jacob O Tø rring Anne C. Elster Department of Computer Science Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Modern computing systems are increasingly more complex, with their multicore CPUs and GPUs accelerators changing yearly, if not more often. It thus has become very challenging to write programs that efficiently use the associated complex memory systems and take advantage of the available parallelism. Autotuning addresses this by optimizing parameterized code to the targeted hardware by searching for the optimal set of parameters. Empirical autotuning has therefore gained interest during the past decades. While new autotuning algorithms are regularly presented and published, we will show why comparing these autotuning algorithms is a deceptively difficult task. In this paper, we describe our empirical study of state-of-the-art search techniques for autotuning by comparing them on a range of sample sizes, benchmarks and architectures. We optimize 6 tunable parameters with a search-space size of over 2 million. The algorithms studied include Random Search (RS), Random Forest Regression (RF), Genetic Algorithms (GA), Bayesian Optimization with Gaussian Processes (BO GP) and Bayesian Optimization with Tree-Parzen Estimators (BO TPE). Our results on the ImageCL benchmark suite suggest that the ideal autotuning algorithm heavily depends on the sample size. In our study, BO GP and BO TPE outperform the other algorithms in most scenarios with sample sizes from 25 to 100. However, GA usually outperforms the others for sample sizes 200 and beyond. We generally see the most speedup to be gained over RS in the lower range of sample sizes (25–100). However, the algorithms more consistently outperform RS for higher sample sizes (200–400). Hence, no single state-of-the-art algorithm outperforms the rest for all sample sizes. Some suggestions for future work are also included.

关键词： Radio frequency Multicore processing Memory management Benchmark testing parallel processing Bayes methods Task analysis

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Toward Ultra-large Scale Neural Spike Sorting with distributed Sorting Channels and Unsupervised Training

Toward Ultra-large Scale Neural Spike Sorting with Distribut...

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IEEE international symposium on Circuits and systems

作者： Junhong Sun Tianhao Li Tongtong Guo Yongfu Li Changyun Fu Yan Liu Deren of Micro-Nno Eecronics Shnghi Jio Tong Universiy ChinDeren of Micro-Nno Eecronics Shnghi Jio Tong Universiy ChinDeren of Micro-Nno Eecronics Shnghi Jio Tong Universiy ChinDeren of Micro-Nno Eecronics Shnghi Jio Tong Universiy ChinDeren of Micro-Nno Eecronics Shnghi Jio Tong Universiy ChinDeren of Micro-Nno Eecronics Shnghi Jio Tong Universiy Chin

ISBN: (数字)9781665484855

ISBN: (纸本)9781665484862

Brain machine interface systems will require recording thousands of neural channels in parallel to acquire large scale neuronal activity. High bandwidth action potential signal will overload the data communication bandwidth, and on-site spike sorting can extract essential information, however, requires extensive computational resources to achieve high classification accuracy. This demands for high resources consuming, especially in large-scale real-time sorting systems. In this work, a customized unsupervised training engine incorporated with distributed and optimized sorting channels is presented in order to reduce the hardware complexity without compromising the accuracy of spike sorting. A mixed-domain feature set is extracted in each channel, followed by feature based sorting. Each channel will constantly monitor the sorting accuracy and will request training engine intervention when in need. The proposed system is implemented in a 180 nm CMOS process, consuming only 0.33 W/channel with a clock of 25 kHz and power supply of 1.8 V, and in-channel sorting occupies 0.0023 mm~2, with training engines occupying 1.956 mm~2, which can be shared by all the channels.

关键词： Neural spike sorting unsupervised training feature extraction

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Exploiting 3D Memory for Accelerated In-Network Processing of Hash Joins in distributed databases 17th

Exploiting 3D Memory for Accelerated In-Network Processing o...

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17th international symposium on Applied Reconfigurable Computing, ARC 2021

作者： Wirth, Johannes Hofmann, Jaco A. Thostrup, Lasse Koch, Andreas Binnig, Carsten Embedded Systems and Applications Group TU Darmstadt Hochschulstr. 10 Darmstadt64289 Germany Data Management Lab TU Darmstadt Hochschulstr. 10 Darmstadt64289 Germany

ISBN: (纸本)9783030790240

The computing potential of programmable switches with multi-Tbit/s throughput is of increasing interest to the research community and industry alike. Such systems have already been employed in a wide spectrum of applications, including statistics gathering, in-network consensus protocols, or application data caching. Despite their high throughput, most architectures for programmable switches have practical limitations, e.g., with regard to stateful operations. FPGAs, on the other hand, can be used to flexibly realize switch architectures for far more complex processing operations. Recently, FPGAs have become available that feature 3D-memory, such as HBM stacks, that is tightly integrated with their logic element fabrics. In this paper, we examine the impact of exploiting such HBM to accelerate an inter-server join operation at the switch-level between the servers of a distributed database system. As the hash-join algorithm used for high performance needs to maintain a large state, it would overtax the capabilities of conventional software-programmable switches. The paper shows that across eight 10G Ethernet ports, the single HBM-FPGA in our prototype can not only keep up with the demands of over 60 Gbit/s of network throughput, but it also beats distributed-join implementations that do not exploit in-network processing. © 2021, Springer Nature Switzerland AG.

关键词： Field programmable gate arrays (FPGA)

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parallel State Estimation under Flexible Connections in Small Celestial Body Landings

Parallel State Estimation under Flexible Connections in Smal...

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IAF Space Communications and Navigation symposium 2021 at the 72nd international Astronautical Congress, IAC 2021

作者： Ge, Dantong Cui, Pingyuan Lu, Xiaoxuan Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781713843023

In this paper, the problem of multi-node state estimation during small celestial body flexible landing is investigated. Considering the restrained effect imposed by the flexible connections between the nodes, an equivalent restrained state is defined. The actual state of the node can be regarded as the combination of its free state and the equivalent restrained state. The equivalent restrained state is then treated as the extended state of the node. A parallel state estimation algorithm is designed, which accomplishes a simultaneous estimation of both the actual state and the equivalent restrained state. In view of the bidirectional restrained effect of the flexible connections, a correction method that fuses equivalent restrained state estimates is further proposed. By exchanging state estimates and measurements among the sensors, consensus of the adjacent nodes’ estimation results with respect to the equivalent restrained state can be guaranteed. The proposed method is validated and compared with the standard distributed EKF algorithm through numerical simulations. It shows that, for cooperative navigation systems with time-variant internal constraints, the precision of state estimation can be effectively improved by implementing the proposed estimation method. Copyright © 2021 by the international Astronautical Federation (IAF). All rights reserved.

关键词： State estimation

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Research on Data Security Protection System Based on SM Algorithm

Research on Data Security Protection System Based on SM Algo...

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Information Science, parallel and distributed systems (ISPDS), international Conference on

作者： Wei Xu Hao Liang Yunhan Ge The Third Research Institute of the Ministry of Public Security Shanghai China

ISBN: (纸本)9781665417396

As the rapid development of information technology and networks, there have been several new challenges to data security. For security needs in the process of data transmission and storage, the data security protection mechanism based on SM algorithm is studied. In addition, data cryptographic security protection system model composed of cryptographic infrastructure, cryptographic service nodes and cryptographic modules is proposed. As the core of the mechanism, SM algorithm not only brings about efficient data encryption and decryption, but ensures the security, integrity and non-repudiation of data transmission and storage. Secure and controllable key management is implemented by this model, which provides easy-to-expandable cryptographic services, and brings efficient cryptographic capabilities applicable for multiple scenarios.

关键词： Information science distributed databases Data models Encryption Data communication Internet of Things Information technology

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