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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

共 34256 条记录，以下是4261-4270 订阅

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Experimental and Numerical Investigation on Bearing Behavior of TRC Slabs Under distributed or Concentrated Loads 5th

Experimental and Numerical Investigation on Bearing Behavior...

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5th international conference on Green Technology and Sustainable Development, GTSD 2020

作者： Truyen, Tran The Quan, Tu Sy University of Transport and Communications Hanoi Viet Nam

ISBN: (纸本)9783030623234

Textile-Reinforced Concrete (TRC) is a composite material combining of weaving grid and fine-grained cement concrete. The fiber grid made from carbon, aramid or glass is not corroded by the environment. Woven mesh has a much larger surface area than traditional reinforced bars, therefore weaving fiber grid reinforced concrete has a greater adhesion force, which is very convenient for thin structure requiring high level of architect such as roof, floor, shell, etc. In most cases, the weaving fiber grid is stretched either by direct traction or by bending. Accordingly, a problematic is the bearing behavior of the thin slabs under the effects of different kinds of loads and limit conditions. Based on the existing studies in literature, experimental models have been developed, which allow to evaluate the effect of the textile density on the bending capacity of the TRC slabs under the impact of distributed or concentrated loads. The failure mode of the TRC slabs is almost identical to the simulation results given by Abaqus software, which helps to make many recommendations for design work. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Finite element method

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Application Research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy

Application Research of Vertical Federated Learning Technolo...

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

作者： Yong Luo Zhi Lu Xiaofei Yin Songfeng Lu Yiting Weng 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 China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound.

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A General Offloading Approach for Near-DRAM Processing-In-Memory Architectures

A General Offloading Approach for Near-DRAM Processing-In-Me...

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international Symposium on parallel and distributed Processing (IPDPS)

作者： Dan Chen Hai Jin Long Zheng Yu Huang Pengcheng Yao Chuangyi Gui Qinggang Wang Haifeng Liu Haiheng He Xiaofei Liao Ran Zheng National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Clusters and Grid Computing Lab Huazhong University of Science and Technology China

Processing-in-memory (PIM) is promising to solve the well-known data movement challenge by performing in-situ computations near the data. Leveraging PIM features is pretty profitable to boost the energy efficiency of applications. Early studies mainly focus on improving the programmability for computation offloading on PIM architectures. They lack a comprehensive analysis of computation locality and hence fail to accelerate a wide variety of applications. In this paper, we present a general-purpose instruction-level offloading technique for near-DRAM PIM architectures, namely IOTPIM, to exploit PIM features comprehensively. IOTPIM is novel with two technical advances: 1) a new instruction offloading policy that fully considers the locality of the whole on-chip cache hierarchy, and 2) an offloading performance benefit prediction model that directly predicts offloading performance benefits of an instruction based on the input dataset characterizes, preserving low analysis overheads. The evaluation demonstrates that IOTPIM can be applied to accelerate a wide variety of applications, including graph processing, machine learning, and image processing. IOT-PIM outperforms the state-of-the-art PIM offloading techniques by 1.28×-1.51× while ensuring offloading accuracy as high as 91.89% on average.

关键词： Analytical models Technological innovation distributed processing Systematics Image processing Computer architecture Machine learning

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Enabling Pervasive Federated Learning using Vehicular Virtual Edge Servers

Enabling Pervasive Federated Learning using Vehicular Virtua...

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19th IEEE international conference on Pervasive computing and Communications (IEEE PerCom)

作者： Du, Anran Shen, Yicheng Tseng, Lewis Higuchi, Takamasa Ucar, Seyhan Altintas, Onur Boston Coll Boston MA 02215 USA Toyota Motor North Amer R&D InfoTech Labs Mountain View CA USA

ISBN: (纸本)9781665404242

Recent works have proposed various distributed federated learning (FL) systems for the edge computing paradigm. These FL algorithms can assist pervasive applications in various aspects, e.g., decision making, pattern recognition, and behavior prediction. Existing solutions do not efficiently support the training based on the real-time location-specific data, because fundamentally, the "data collection" problem is rarely studied in the context of FL systems. To address this problem, we present a novel system, VC-SGD (Vehicular Clouds-Stochastic Gradient Descent), which seamlessly integrates the emerging concept of vehicular clouds with an edge-based FL. We show that by using vehicular clouds as virtual edge servers, VC-SGD is able to effectively support FL algorithms that use real-time location-specific data. We develop a general simulator that uses SUMO to simulate vehicle mobility and MXNet to perform real training. We use our simulator to verify the efficacy of VC-SGD. The experimental results demonstrate that VC-SGD improves over existing solutions.

关键词： Federated Learning Machine Learning Edge computing Vehicular Clouds Simulation

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Computational Aspects of Solving grid Equations in Heterogeneous computing Systems 16th

Computational Aspects of Solving Grid Equations in Heterogen...

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16th international conference on parallel computing Technologies, PaCT 2021

作者： Sukhinov, Alexander Litvinov, Vladimir Chistyakov, Alexander Nikitina, Alla Gracheva, Natalia Rudenko, Nelli Don State Technical University Rostov-on-Don Russia Azov-Black Sea Engineering Institute of Don State Agrarian University Zernograd Russia Southern Federal University Rostov-on-Don Russia

ISBN: (纸本)9783030863586

The prediction of environmental disasters, both technogenic and natural, is currently based on advances in mathematical modeling. The high cost and costly maintenance of computing clusters actualizes the research in the field of heterogeneous computing. One of the directions of them is to maximize the use of all available hardware resources, including the central processor and the video adapters (GPU). The purpose of the research is to develop an algorithm and a software module that implements it for solving a system of linear algebraic equations (SLAE) by the modified alternating-triangular iterative method (MATM) (self-adjoint and non-self-adjoint cases) for the hydrodynamics problem of shallow water using NVIDIA CUDA technology. The conducted experiment with the flow distribution along the Ox and Oz axes of the computational grid at a fixed value of the grid nodes along the Oy axis allowed reducing the implementation time of one step of the MATM on the GPU. A regression equation was obtained at the experimental data processing in the Statistica program, on the basis of which it was found that the implementation time of one step of the MATM on the GPU is affected only by the number of threads along the axis Oz. The optimal two-dimensional configuration of threads in a computing unit executed on a single thread multiprocessor is determined, in which the calculation time on the GPU for one step of the MATM is minimal. © 2021, Springer Nature Switzerland AG.

关键词： Iterative methods

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Designing a 3D parallel Memory-Aware Lattice Boltzmann Algorithm on Manycore Systems 27th

Designing a 3D Parallel Memory-Aware Lattice Boltzmann Algor...

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27th international European conference on parallel and distributed computing (Euro-Par)

作者： Fu, Yuankun Song, Fengguang Purdue Univ Indianapolis IN USA Indiana Univ Purdue Univ Indianapolis IN 46202 USA

ISBN: (纸本)9783030856656;9783030856649

Lattice Boltzmann method (LBM) is a promising approach to solving Computational Fluid Dynamics (CFD) problems, however, its nature of memory-boundness limits nearly all LBM algorithms' performance on modern computer architectures. This paper introduces novel sequential and parallel 3D memory-aware LBM algorithms to optimize its memory access performance. The introduced new algorithms combine the features of single-copy distribution, single sweep, swap algorithm, prism traversal, and merging two temporal time steps. We also design a parallel methodology to guarantee thread safety and reduce synchronizations in the parallel LBM algorithm. At last, we evaluate their performances on three high-end manycore systems and demonstrate that our new 3D memory-aware LBM algorithms outperform the state-of-the-art Palabos software (which realizes the Fuse Swap Prism LBM solver) by up to 89%.

关键词： Lattice Boltzmann method Memory-aware algorithms parallel numerical methods Manycore systems

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Stability Margin Analysis of Multi-inverter grid-connected System Considering Asynchronous Control Period Under High Penetration 12

Stability Margin Analysis of Multi-inverter Grid-connected S...

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12th IEEE Energy Conversion Congress and Exposition-Asia (ECCE-Asia)

作者： Yu, Changzhou Xu, Haizhen Liu, Chun Chen, Chen Wang, Qinglong Zhang, Xing Hefei Univ Sch Adv Mfg Engn Hefei Peoples R China Hefei Univ Technol Sch Elect Engn & Automat Hefei Peoples R China

ISBN: (纸本)9781728163444

With the growth of renewable energy, grid-connected inverter as the interface has gradually increased its application. However, with the large-scale connection of inverters, resonance and stability problems have also been brought to distributed systems. Aiming at this issue, in this paper, a multi-parallel equivalent small signal model based on the structure of the multi-inverter grid-connected system is established firstly, and the expression of the output current of each inverter is obtained. According to the phase characteristics of the inverter's output at different frequency bands, the output current expressions for synchronized and interleaved control period are further obtained. On this basis, influences of penetration, control gain and delay on harmonic or resonance are analyzed. Finally, a platform of four 30kW inverters is setup, and "resonance stability margin" method is proposed to verify the harmonic resonance characteristics of the inverter output current under high penetration.

关键词： stability margin multi-inverter high penetration

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Deep Reinforcement Learning based Elasticity-compatible Heterogeneous Resource Management for Time-critical computing 20

Deep Reinforcement Learning based Elasticity-compatible Hete...

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49th international conference on parallel Processing (ICPP)

作者： Liu, Zixia Wang, Liqiang Quan, Gang Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Florida Int Univ Elect & Comp Engn Dept Miami FL USA

ISBN: (纸本)9781450388160

Rapidly generated data and the amount magnitude of data analytical jobs pose great pressure to the underlying computing facilities. A distributed multi-cluster computing environment such as a hybrid cloud consequently raises its necessity due to its advantages in adapting geographically distributed and potentially cloud-based computing resources. Different clusters forming such an environment could be heterogeneous and may be resource-elastic as well. From analytical perspective, in accordance with increasing needs on streaming applications and timely analytical demands, many data analytical jobs nowadays are time-critical in terms of their temporal urgency. And the overall workload of the computing environment can be hybrid to contain both time-critical and general applications. These all call for an efficient resource management approach capable to apprehend both computing environment and application features. However, the added up complexity and high dynamics of the system greatly hinder the performance of traditional rule-based approaches. In this work, we propose to utilize deep reinforcement learning for developing elasticity-compatible resource management for a heterogeneous distributed computing environment, aiming for less occurrences of missing temporal deadline while maintaining low average execution time ratio. Along with reinforcement learning we design a deep model employing Long Short-Term Memory (LSTM) structure and partial model sharing for multi-target learning mechanism. The experimental results show that the proposed approach could greatly outperform baselines and serve as a robust resource management for variant workloads.

关键词： deep reinforcement learning time-critical heterogeneous elasticity multi-cluster distributed computing

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Analyzing the distributed training of deep-learning models via data locality 29

Analyzing the distributed training of deep-learning models v...

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29th Euromicro international conference on parallel, distributed and Network-Based Processing, PDP 2021

作者： Alonso-Monsalve, Saúl Calderón, Alejandro García-Carballeira, Félix Rivadeneira, José Cern Geneva Switzerland Universidad Carlos Iii de Madrid Leganés Madrid Spain Universidad Carlos Iii de Madrid Computer Science and Engineering Department Leganés Madrid Spain

ISBN: (纸本)9781665414555

In the last few years, deep-learning models are becoming crucial for numerous scientific and industrial applications. Due to the growth and complexity of deep neural networks, researchers have been investigating techniques to train those networks more efficiently. Many efforts have been made to optimize deep-learning models by parallelizing or distributing their training computation across multiple devices. Current state-of-the-art techniques, such as Horovod, have shown to maximize the performance of both the training computation and the inter-node communication of models for different deep-learning frameworks. However, some applications cannot take advantage of the above techniques due to an I/O bottleneck caused by the input data, thus limiting the scalability of the trainings. In this paper, we study an approach based on data locality - that has not been fully studied yet - for those neural networks that cannot benefit from scaling their computation due to a significant bottleneck in the data I/O. © 2021 IEEE.

关键词： Deep neural networks

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A Provenance-Aware Approach to Big Data Workflow Management in Heterogeneous Cloud Environments

A Provenance-Aware Approach to Big Data Workflow Management ...

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IEEE international conference on Cloud computing and Big Data Analysis (ICCCBDA)

作者： Andre Kashliev Kaitlyn Tracy Department of Computer Science Eastern Michigan University Ypsilanti USA

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

Big data workflows have emerged as a powerful paradigm that enables researchers and practitioners to run complex multi-step computational processes in the cloud to gain insight into their large datasets. To create a workflow, a user logs on to a specialized software, called Big Data Workflow Management System, or simply BDW system, to select and connect together various components, or tasks, into a workflow. The workflow is then mapped onto a set of distributed compute resources, such as Virtual Machines (VMs), and storage resources, such as S3 buckets and EBS volumes. It is then executed, with different branches and tasks of the workflow running in parallel on different nodes. During execution, the BDW system captures provenance, which is the history of data derivation that describes data processing steps that yielded each output result. Workflow management, including workflow composition and schedule refinement, is a challenging problem. This problem is further exacerbated by the growing number and heterogeneity of workflow tasks and cloud resources, as well as by the growing size and complexity of workflow structures. Few efforts were made to leverage provenance for facilitating workflow composition and schedule refinement. To address these issues, we 1) produce a comprehensive conceptual model for big data workflow provenance that captures the complexity and heterogeneity of cloud-based workflow execution, 2) propose a scalable Cassandra database schema for provenance-aware workflow composition and schedule refinement, 3) outline a four-step provenance-based schedule refinement process for balancing workflow execution time and cost, and 4) present a scalable and highly available microservices-based reference architecture for big data workflow management in the cloud. Our proposed loosely coupled architecture ensures superior scalability, as well as operational and technological independence of each module within the BDW system.

关键词： Cloud computing Schedules Scalability Computer architecture Big Data Virtual machining Software

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