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

作者： Wei Liao Lansheng Han Peng Chen Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

Lattice cryptography, as a recognized Cryptosystem that can resist quantum computation, has great potential for development. Lattice based signature scheme is currently a research focus. In this paper, the traceable ring signature scheme based on lattice cryptography in the Internet of Vehicles (IoV) environment is studied. Firstly, users’ public and private keys are generated on the lattice, which effectively reduces the key size and time cost. Secondly, the scheme also achieves traceability by adding additional information. The scheme satisfies strong unforgeability, traceability, and anonymity. In addition, the traceable ring signature scheme based on lattice cryptography is applied in the IoV environment to effectively protect user privacy. Model communication can not only protect user privacy, but also trace the signer when necessary, achieving a balance between security and privacy. The simulation results show that the average delay of IoV of our scheme is better than that of similar schemes. The research in this paper improves the security of data transmission in the IoV environment, enables it to adapt to the complex and changeable new network environment, and effectively guarantees the information security of vehicles and owners.

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Enhancing parallelization with OpenMP through Multi-Modal Transformer Learning

Enhancing Parallelization with OpenMP through Multi-Modal Tr...

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international conference on Computer Engineering and applications (ICCEA)

作者： Yuehua Chen Huaqiang Yuan Fengyao Hou Peng Hu Dongguan University of Technology Dongguan China Institute of High Energy Physics Chinese Academy of Sciences Beijing China Spallation Neutron Source Science Center Dongguan China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

The popularity of multicore processors and the rise of High Performance Computing as a Service (HPCaaS) have made parallel programming essential to fully utilize the performance of multicore systems. OpenMP, a widely adopted shared-memory parallel programming model, is favored for its ease of use. However, it is still challenging to assist and accelerate automation of its parallelization. Although existing automation tools such as Cetus and DiscoPoP to simplify the parallelization, there are still limitations when dealing with complex data dependencies and control flows. Inspired by the success of deep learning in the field of Natural Language processing (NLP), this study adopts a Transformer-based model to tackle the problems of automatic parallelization of OpenMP instructions. We propose a novel Transformer-based multimodal model, ParaMP, to improve the accuracy of OpenMP instruction classification. The ParaMP model not only takes into account the sequential features of the code text, but also incorporates the code structural features and enriches the input features of the model by representing the Abstract Syntax Trees (ASTs) corresponding to the codes in the form of binary trees. In addition, we built a BTCode dataset, which contains a large number of C/C++ code snippets and their corresponding simplified AST representations, to provide a basis for model training. Experimental evaluation shows that our model outperforms other existing automated tools and models in key performance metrics such as F1 score and recall. This study shows a significant improvement on the accuracy of OpenMP instruction classification by combining sequential and structural features of code text, which will provide a valuable insight into deep learning techniques to programming tasks.

关键词： Deep learning Training Codes Automation Accuracy parallel programming Multicore processing

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Selective Coflow Completion for Time-sensitive distributed applications with Poco 20

Selective Coflow Completion for Time-sensitive Distributed A...

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49th international conference on parallel processing (ICPP)

作者： Luo, Shouxi Fan, Pingzhi Xing, Huanlai Yu, Hongfang Southwest Jiaotong Univ Chengdu Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9781450388160

Recently, the abstraction of coflow is introduced to capture the collective data transmission patterns among modern distributed data-parallel application. During processing, coflows generally act as barriers;accordingly, time-sensitive applications prefer their coflows to complete within deadlines and deadline-aware coflow scheduling becomes very crucial. Regarding these data-parallel applications, we notice that many of them, including large-scale query system, distributed iterative training, and erasure codes enabled storage, are able to tolerate loss-bounded incomplete inputs by design. This tolerance indeed brings a flexible design space for the schedule of their coflows: when getting overloaded, the network can trade coflow completeness for timeliness, and balance the completenesses of different coflows on demand. Unfortunately, existing coflow schedulers neglect this tolerance, resulting in inflexible and inefficient bandwidth allocations. In this paper, we explore this fundamental trade-off and design Poco, a POlicy-based COflow scheduler, to achieve customizable selective coflow completions for these emerging time-sensitive distributed applications. Internally, Poco employs a suite of novel designs along with admission controls to make flexible, work-conserving, and performance-guaranteed rate allocation to online coflow requests very efficiently. Extensive trace-based simulations indicate that Poco is highly flexible and achieves optimal coflow schedules respecting the requirements specified by applications.

关键词： coflow deadline flow scheduling policy

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HCPipe: Hidden Computation Pipeline Model parallelism for Large-scale DCNN Model Training

HCPipe: Hidden Computation Pipeline Model Parallelism for La...

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international conference on Computer Engineering and applications (ICCEA)

作者： Bin Liu Yongyao Ma Zhonghao Zhang Zeyu Ji Guoqiang Hu Yijie Chen College of Information Engineering Northwest A&F University Yangling China Network and Educational Technology Center Northwest A&F University Yangling China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

The complexity of Deep Convolutional Neural Networks (DCNNs) with big datasets significantly prolongs the time of model training. Pipeline Model parallelism (PMP) is a commonly used method in distributed training for shortening model time. However, traditional PMP methods struggle to maximize hidden computational, resulting in significant idle time. This paper introduces an innovative method, HCPipe, which employs a hidden computation method to overlap computation times further, effectively reducing idle time in pipeline training. The effectiveness of this method is firstly theoretically demonstrated. Subsequently, a model partitioning method based on dynamic programming is designed, aimed at rapidly dividing the model. To evaluate the performance of HCPipe, using the ImageNet dataset extensive experiments were conducted with ResNet and VGG models on a 4-GPU server. The experimental results indicate that the proposed HCPipe achieves significant performance improvements. Compared to GPipe, DAPPLE, and GEMS, the average throughput for the VGG series is increased by 13.3%, 4.6%, and 128%, respectively, while the average throughput for the ResNet series is increased by 27.8%, 14.4%, and 157%, respectively.

关键词： Training Computational modeling Pipelines Graphics processing units parallel processing Throughput Dynamic programming

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Web stream processing with RSP4J 21

Web stream processing with RSP4J

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15th ACM international conference on distributed and Event-Based Systems, DEBS 2021

作者： Tommasini, Riccardo Bonte, Pieter University of Tartu Data System Group Estonia Ghent University Imec IDLab Belgium

ISBN: (纸本)9781450385558

Social Media Analysis, Internet of Things, and Fake News detection have unveiled the relevance of real-time analytics on the Web. As a consequence, the Web infrastructure is evolving to enable continuous and reactive data access. Since data streams available on the Web originate from a variety of sources, they are highly heterogeneous. Indeed, addressing data variety and velocity simultaneously is inevitable. Stream Reasoning is the research field that studies how to combine data integration techniques with stream processing technologies. In particular, solutions for RDF Stream processing (RSP) combine stream processing notions with data integration standards. This tutorial paper presents RSP4J, a innovative API that aims at fostering the adoption of RSP by simplifying the usage, benchmarking, and fast prototyping of Web Stream processing applications. © 2021 Owner/Author.

关键词： Data integration

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Static Modeling of A Soft Robotic Manipulator Using Neural Networks and Computer Vision techniques

Static Modeling of A Soft Robotic Manipulator Using Neural N...

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RSI/ISM international conference on Robotics and Mechatronics (ICROM)

作者： Ali Shojaeezadeh Pouya Firuzy Rad Hamed Ghafarirad Mechanical Engineering Department Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

This research explores the static modeling of a planar soft robotic arm composed of two parallel silicon-based actuators using neural networks. Soft robots, characterized by their pliable structure and pneumatic actuation, present challenges in traditional control and modeling due to their nonlinear behavior and material flexibility. Conventional methods often fail to accurately predict their motion, especially under varying internal pressures and structural imperfections. In contrast, neural network-based approaches offer robust solutions by effectively capturing these complexities through data-driven techniques. Utilizing a Levenberg-Marquardt algorithm for training, this study develops a neural network model that predicts the 2D spatial position of the robotic arm's end-effector based on input pressure variables. Experimental data, obtained via image processing techniques for precise position tracking, informs the model's training and validation phases. Results demonstrate high accuracy in predicting end-effector positions, validated by low mean squared errors and high coefficients of determination. This approach not only advances static modeling capabilities for soft robotic systems but also lays groundwork for future dynamic modeling and real-time control applications.

关键词： Training Actuators Computational modeling Neural networks Soft robotics Predictive models Pneumatic systems End effectors Real-time systems Long short term memory

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Bigflow: A General Optimization Layer for distributed Computing Frameworks

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Journal of Computer Science & Technology 2020年第2期35卷 453-467页

作者： Yun-Cong Zhang Xiao-Yang Wang Cong Wang Yao Xu Jian-Wei Zhang Xiao-Dong Lin Guang-Yu Sun Gong-Lin Zheng Shan-Hui Yin Xian-Jin Ye Li Li Zhan Song Dong-Dong Miao Baidu Inc. Beijing 100193China Center for Energy-Efficient Computing and Applications Peking UniversityBeijing 100871China

As data volumes grow rapidly, distributed computations are widely employed in data-centers to provide cheap and efficient methods to process large-scale parallel datasets. Various computation models have been proposed to improve the abstraction of distributed datasets and hide the details of parallelism. However, most of them follow the single-layer partitioning method, which limits developers to express a multi-level partitioning operation succinctly. To overcome the problem, we present the NDD (Nested distributed Dataset) data model. It is a more compact and expressive extension of Spark RDD (Resilient distributed Dataset), in order to remove the burden on developers to manually write the logic for multi-level partitioning cases. Base on the NDD model, we develop an open-source framework called Bigflow, which serves as an optimization layer over computation engines from most widely used processing frameworks. With the help of Bigflow, some advanced optimization techniques, which may only be applied by experienced programmers manually, are enabled automatically in a distributed data processing job. Currently, Bigflow is processing about 3 PB data volumes daily in the data-centers of Baidu. According to customer experience, it can significantly save code length and improve performance over the intuitive programming style.

关键词： distributed computing programming model optimization technique

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Fast recovery of correlated failures in distributed stream processing engines 21

Fast recovery of correlated failures in distributed stream p...

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15th ACM international conference on distributed and Event-Based Systems, DEBS 2021

作者： Su, Li Zhou, Yongluan Alibaba Group China University of Copenhagen Denmark

ISBN: (纸本)9781450385558

In a large-scale cluster, correlated failures usually involve a number of nodes failing simultaneously. Although correlated failures occur infrequently, they have significant effect on systems' availability, especially for streaming applications that require real-time analysis, as repairing the failed nodes or acquiring additional ones would take a significant amount of time. Most state-of-the-art distributed stream processing systems (DSPSs) focus on recovering individual failures and do not consider the optimization for recovering correlated failure. In this work, we propose an incremental and query-centric recovery paradigm where the recovery of failed operator partitions would be carefully scheduled based on the current availability of resources, such that the outputs of queries can be recovered as early as possible. By analyzing the existing recovery techniques, we identify the challenges and propose a fault-tolerance framework that can support incremental recovery with minimum overhead during the system's normal execution. We also formulate the new problem of recovery scheduling under correlated failures and design algorithms to optimize the recovery latency with a performance guarantee. A comprehensive set of experiments are conducted to study the validity of our proposal. © 2021 ACM.

关键词： Fault tolerance

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Revenue Forecasting Using distributed Machine Learning In Online Advertising Systems

Revenue Forecasting Using Distributed Machine Learning In On...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE), international conference on

作者： Murugan P Esakkiammal S N. Kanagavalli P. Krishna Priya Jason Elroy Martis P. Kavitha Department of BBA Vels Institute of Science Technology and Advanced Studie (VISTAS) Pallavaram Chennai Tamilnadu India Department of Library Central University of Tamil Nadu Neelakudi Thiruvarur Tamilnadu India Department of CSE Rajalakshmi Institute of Technology Kuthambakkam Chennai Tamilnadu India KL Business School KL Education Foundation Vaddeswaram Guntur Andrha Pradesh India Department of Information Science and Engineering NMAM Institute of Technology Nitte Deemed to be University Karkala Karnataka India Department of Computer Science and Engineering R.M.K. Engineering College Tamilnadu India

This research delves into the utilization of distributed machine learning methods for revenue forecasting in the realm of online advertising systems. As the domain of digital advertising continues to expand rapidly, the accurate prediction of revenue becomes indispensable for optimizing resource allocation and making well-informed decisions. Conventional approaches frequently encounter difficulties in managing the sheer volume and intricacy of advertising data. In contrast, distributed machine learning harnesses the capabilities of parallel processing and distributed computing to efficiently process and analyze large datasets. This investigation explores diverse distributed machine learning algorithms, including gradient boosting, random forests, and neural networks, within the context of revenue forecasting. The research assesses their performance, scalability, and precision in comparison to traditional techniques. Through empirical experiments and case studies, we showcase the potential of distributed machine learning to elevate the accuracy of revenue forecasting in online advertising systems, ultimately contributing to more informed business strategies and enhanced revenue generation.

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distributed and parallel Sparse Computing for Very Large Graph Neural Networks

Distributed and Parallel Sparse Computing for Very Large Gra...

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IEEE international conference on Big Data

作者： Quentin R. Petit Chong Li Nahid Emad Huawei Paris Research Center & Université Paris-Saclay Boulogne-Billancourt France Distributed and Parallel Software Lab Huawei Paris Research Center Boulogne-Billancourt France Maison de la Simulation & LI-PaRAD Université Versailles Saint-Quentin Saclay France

ISBN: (纸本)9781665480468

Deep learning (DL) requires high-performance processing on big data. Graph Neural Networks, a challenging topic in DL using linear algebra methods, need algorithmic solutions to efficiently assign and process graph data on modern distributed and parallel machines, which are considered with mixed arithmetic and various types of tensor/matrix accelerators. Determining compression techniques for the graph’s sparse data structures is one of the key *** first objective is to design and implement a reusable parallel numerical library to resolve large neural network graphs. Our design strategy is drawn on a component-based approach and targets maximum code reuse in various parallel contexts while allowing for performance optimization. The solution could be later integrated into a DL framework like MindSpore.

关键词： Deep learning distributed databases Linear algebra Big Data parallel machines Graph neural networks Libraries

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