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

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Cost-Effective Dynamic Alliance Pricing Mechanism Based on distributed Edge-Intelligence 21

Cost-Effective Dynamic Alliance Pricing Mechanism Based on D...

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21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Cao, Zhihan Zheng, Xi Guo, Jianxiong Jia, Weijia Xu, Yang Wang, Tian Beijing Normal University Zhuhai China Fudan University Shanghai China Macquarie University Australia Hunan University Changsha China

ISBN: (纸本)9798350329223

In Internet of Thing (ToT) system based on edge intelligence, pay-for-use demand has become a consensus, and the pricing of ToT services has attracted the attention of academia and industry. The pricing method based on non-cooperative game allows edge service providers to compete fairly, effectively preventing edge nodes from malicious bidding. However, in this scenario, a task can only be assigned to one edge vendor. Because only one winner can profit each time, it is easy to create a large number of edge service providers who cannot profit for a long time due to insufficient incentives. To this end, a dynamic alliance pricing mechanism based on distributed edge-intelligence is proposed. Edge service providers can freely choose to form an edge dynamic alliance, which not only retains the independence of edge nodes, but also makes full use of the advantages of mutual cooperation between nodes. Tn order to prevent abnormal behaviors of edge nodes, we set up necessary restrictive rules. At the same time, we designed a privacy-enhanced joint pricing prediction algorithm to screen the alliance's candidate solutions to improve pricing efficiency and edge benefit. The experimental results show that, compared with the traditional game method, the performance of the mechanism we proposed improves the utilization rate of edge resources by 32.76%-61.37%. Meanwhile, the prediction accuracy was improved by 16.47%38.86%, and the average prediction time was reduced by 42.81 %-65.57%. © 2023 IEEE.

关键词： Profitability

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Efficient Large Scale Reverse-time Migration Imaging Computation based on distributed Spark Cluster with GPUs 21

Efficient Large Scale Reverse-time Migration Imaging Computa...

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作者： Wangzhang, Suhui Yang, Ruizhang Gu, Rong Li, Bo Liu, Dingjin Wang, Zhaokang Nanjing University State Key Laboratory for Novel Software Technology China Sinopec Geophysical Research Institute China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China

ISBN: (纸本)9798350329223

The reverse-time migration (RTM) imaging algorithm, which is used for complex underground structure analysis, is known as one of the current mainstream method of high-precision seismic imaging. Nowadays, as the fast development of seismic data acquisition technology, there exists large amount of seismic data. Thus, the requirement for the efficient computation of the reverse-time migration (RTM) imaging algorithms over large-scale data have been increased greatly. To address this problem, in this paper, we propose a distributed reverse-time migration imaging algorithm based on the widely-used Spark platform first. Based on that, we design and implement a RTM computation solution based on Spark and GPU with several optimizations, including pre-shuffle&caching and load balancing. We also analyze the fault tolerance capability of the proposed distributed algorithm. We evaluate the proposed methods on the typical three-dimensional wavefild benchmark dataset. Experimental results show that the performance of the Spark based reverse-time migration imaging algorithm can scale up near linearly with computing nodes. In addition, the GPU+Spark based version has significant performance improvement on execution efficiency over the Spark-based algorithm. Moreover, the proposed optimization strategies also take effect. In addition, the method proposed in this paper has been applied in the real-world environment of one of the top oil exploration companies world wide, and has achieved significant improvement under the premise of the algorithm correctness guarantee. © 2023 IEEE.

关键词： Graphics processing unit

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Hierarchical computing Network Collaboration Architecture for Industrial Internet of Things 28

Hierarchical Computing Network Collaboration Architecture fo...

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IEEE 28th international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Luo, Zihui Zheng, Xiaolong Wang, Bo Meng, Qifeng Cu, Helei Guo, Xiaobing Liu, Liang Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Lenovo Res Beijing Peoples R China

ISBN: (纸本)9781665473156

The Industrial Internet of Things (IIoT) is deemed a promising direction to drive a new industrial revolution. However, due to the isolation of the existing OT network and IT network, the requirements of low latency, low jitter, and high reliability for transmission and processing of industrial time-sensitive tasks data traffic in IIoT scenarios with strong dynamic and complex topology face a series of non-trivial challenges. In this paper, we propose a hierarchical computing network collaboration architecture for IIoT based on edge/fog computing. Our architecture is built upon the Time-Sensitive Networking (TSN) to flexibly support different requirements of large-scale industrial production applications by constructing the hierarchical computing network collaboration domain, combined with an improved Cyclic Queuing and Forwarding (CQF) scheduling shaper mechanism. We tackle the critical problems of architecture design by presenting three essential components. Moreover, we build and implement our simulation testbed based on Omnet++, and evaluate our design.

关键词： IIoT computing Network Collaboration Edge computing Fog computing Time-Sensitive Networking

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Communication Analysis for Multidimensional parallel Training of Large-scale DNN Models 25

Communication Analysis for Multidimensional Parallel Trainin...

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25th IEEE international conferences on High Performance computing and Communications, 9th international conference on Data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Lai, Zhiquan Hao, Yanqi Li, Shengwei Li, Dongsheng College of Computer National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing Changsha China

ISBN: (纸本)9798350330014

Multidimensional parallel training has been widely applied to train large-scale deep learning models like GPT-3. The efficiency of parameter communication among training devices/processes is often the performance bottleneck of large model training. Analysis of parameter communication mode and traffic has important reference significance for the research of interconnection network design and computing task scheduling to improve the training performance. In this paper, we analyze the parametric communication modes in typical 3D parallel training (data parallelism, pipeline parallelism, and tensor parallelism), and model the traffic in different communication modes. Finally, taking GPT-3 as an example, we present the communication in its 3D parallel training. © 2023 IEEE.

关键词： Deep neural networks

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Cross-subject Drowsiness Recognition Model Based on the Multi-scale parallel Convolution of EEG Channels 21

Cross-subject Drowsiness Recognition Model Based on the Mult...

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作者： Li, Songquan Zhu, Rongbo Yuan, Jun Wan, Shaohua Huazhong Agricultural University College of Informatics Wuhan430070 China Huazhong Agricultural University Shenzhen Institute of Nutrition and Health Wuhan430070 China Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen Branch Guangdong Laboratory for Lingnan Modern Agriculture Genome Analysis Laboratory Ministry of Agriculture Shenzhen518000 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518000 China

ISBN: (纸本)9798350329223

Cross-subject electroencephalogram (EEG) drowsiness recognition is currently one of the most efficient methods. However, the traditional cross-subject approaches overlook the correlation between channel sub-features and effectively utilize the potential connection between frequency features and drowsiness, making it difficult to extract shared features from differentiated subjects. This paper proposes a novel deep learning model called CPCNN to address the challenging of cross-subject drowsiness recognition without calibration and improve accuracy. CPCNN obtains delta frequency band features related to drowsiness from sub-features in the channel dimension, effectively combining the advantages of delta frequency bands in EEG with the richness of channel features. To capture the common features of drowsiness, a channel slicing module is proposed, which divides the spatial and channel features extracted by the deep separable convolution module into multiple sub-features along the channel dimension. In addition, to further improve cross-subject recognition performance, a multi-scale parallel group convolution is designed to capture four key frequency sub-feature groups within the delta frequency band. The experimental results show that CPCNN boosts the recognition performance, significantly outperforms all strong baselines. On balanced and unbalanced public datasets, the average accuracy of CPCNN is 81.49% and 78.26%, respectively, surpassing the excellent baseline method by 3.14% and 0.56%. © 2023 IEEE.

关键词： Electroencephalography

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Energy-Efficient Scheduling of Moldable Streaming Computations for the Edge-Cloud Continuum 9

Energy-Efficient Scheduling of Moldable Streaming Computatio...

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9th IEEE international conference on Fog and Mobile Edge computing (FMEC)

作者： Khosravi, Sajad Kessler, Christoph Litzinger, Sebastian Keller, Joerg Linkoping Univ Linkoping Sweden Fernuniv Hagen Germany

ISBN: (纸本)9798350366495;9798350366488

We consider the problem of cost-effectively mapping a swarm of soft real-time stream processing applications with moldable-parallel tasks to multicore resources in the device-edge-cloud continuum, consisting of mobile devices, edge resources and cloud resources. We leverage flexibility from different parallelization degrees and frequency levels (DVFS) for the tasks, keeping application throughput constraints and communication bandwidth limitations while minimizing overall cost (including device/edge resource energy and cloud resource renting). We present two offline algorithmic solutions with a global view of the environment: an integer linear program (ILP) extending the crown scheduling approach for multi-layer distributed systems and a greedy heuristic algorithm. Our experimental evaluation for several real-world and synthetic scenarios shows that the time required for solving the scheduling problem to cost-optimality by the ILP is feasible for nontrivial scenarios. The heuristic achieves about 12% worse cost efficiency on average, yet operates much faster (by 1-2 orders of magnitude), allowing to scale up the problem size more than the ILP approach.

关键词： distributed stream processing Mapping Scheduling Moldable tasks Edge-Cloud continuum Energy efficiency DVFS

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distributed Robust Optimization Method for AC/MTDC Hybrid Power Systems with DC Network Cognizant

Distributed Robust Optimization Method for AC/MTDC Hybrid Po...

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international conference on Smart Energy Systems and Technologies (SEST) - Driving the advances for Future Electrification

作者： Li, Haixiao Lekic, Aleksandra Delft Univ Technol Fac Elect Engn Math & Comp Sci Delft Netherlands Chongqing Univ Technol Sch Elect & Elect Engn Chongqing Peoples R China

ISBN: (纸本)9798350386509;9798350386493

AC/ multi-terminal DC (MTDC) hybrid power systems have emerged as a solution for the large-scale and long-distance accommodation of power produced by renewable energy systems (RESs). To ensure the optimal operation of such hybrid power systems, this paper addresses three key issues: system operational flexibility, centralized communication limitations, and RES uncertainties. Accordingly, a specific AC/DC optimal power flow (OPF) model and a distributed robust optimization method are proposed. Firstly, we apply a set of linear approximation and convex relaxation techniques to formulate the mixed-integer convex AC/DC OPF model. This model incorporates the DC network-cognizant constraint, enabling DC topology reconfiguration. Next, generalized Benders decomposition (GBD) is employed to provide distributed optimization. Enhanced approaches are incorporated into GBD to achieve parallel computation and asynchronous updating. Additionally, the extreme scenario method (ESM) is embedded into the constructed AC/DC OPF model to provide robust decisions to hedge against RES uncertainties. ESM is further extended to align the GBD procedure. Numerical results are finally presented to validate the effectiveness of our proposed optimization method.

关键词： Multi-terminal DC grid DC network cognizance distributed robust optimization generalized benders decomposition extreme scenario method

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Enabling High-Throughput parallel I/O in Particle-in-Cell Monte Carlo Simulations with openPMD and Darshan I/O Monitoring

Enabling High-Throughput Parallel I/O in Particle-in-Cell Mo...

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2024 international conference on Cluster computing

作者： Williams, Jeremy J. Medeiros, Daniel Costea, Stefan Tskhakaya, David Poeschel, Franz Widera, Rene Huebl, Axel Klasky, Scott Podhorszki, Norbert Kos, Leon Podolnik, Ales Hromadka, Jakub Narwal, Tapish Steiniger, Klaus Bussmann, Michael Laurel, Erwin Markidis, Stefano KTH Royal Inst Technol Stockholm Sweden Univ Ljubljana LeCAD Ljubljana Slovenia CAS Inst Plasma Phys Prague Czech Republic Helmholtz Zentrum Dresden Rossendorf Dresden Germany Lawrence Berkeley Natl Lab Berkeley CA USA Oak Ridge Natl Lab Oak Ridge TN USA Max Planck Comp & Data Facil Garching Germany

ISBN: (纸本)9798350383461;9798350383454

Large-scale HPC simulations of plasma dynamics in fusion devices require efficient parallel I/O to avoid slowing down the simulation and to enable the post-processing of critical information. Such complex simulations lacking parallel I/O capabilities may encounter performance bottlenecks, hindering their effectiveness in data-intensive computing tasks. In this work, we focus on introducing and enhancing the efficiency of parallel I/O operations in Particle-in-Cell Monte Carlo simulations. We first evaluate the scalability of BIT1, a massivelyparallel electrostatic PIC MC code, determining its initial write throughput capabilities and performance bottlenecks using an HPC I/O performance monitoring tool, Darshan. We design and develop an adaptor to the openPMD I/O interface that allows us to stream PIC particle and field information to I/O using the BP4 backend, aggressively optimized for I/O efficiency, including the highly efficient ADIOS2 interface. Next, we explore advanced optimization techniques such as data compression, aggregation, and Lustre file striping, achieving write throughput improvements while enhancing data storage efficiency. Finally, we analyze the enhanced high-throughput parallel I/O and storage capabilities achieved through the integration of openPMD with rapid metadata extraction in BP4 format. Our study demonstrates that the integration of openPMD and advanced I/O optimizations significantly enhances BIT1's I/O performance and storage capabilities, successfully introducing high throughput parallel I/O and surpassing the capabilities of traditional file I/O.

关键词： openPMD Darshan ADIOS2 parallel I/O Efficient Data Processing distributed Storage Large-Scale PIC Simulations

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Improving The Efficiency of Split Learning Based on Multi-user and parallel Training 3

Improving The Efficiency of Split Learning Based on Multi-us...

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3rd international conference on Artificial Intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Li, Yinhui Lu, Xiaofeng School of Cyberspace Security Beijing University of Post and Telecommunications Beijing100876 China

ISBN: (纸本)9781510671485

Federated learning has rapidly become a research hotspot in the field of distributed deep learning because raw data does not need to be shared. However, it is still characterized by problems in terms of privacy security and computing power. Split learning is a new distributed deep learning technology. By dividing the complete deep learning model, the client and server can participate in the training together to overcome some problems related to computing power and privacy and security. In this paper, the concept of split learning is introduced. This article introduces the use of multi-user and parallel training methods to improve the learning efficiency of split learning. © 2023 SPIE.

关键词： computing power

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Fast Deterministic Massively parallel Ruling Sets Algorithms 25

Fast Deterministic Massively Parallel Ruling Sets Algorithms

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26th international conference on distributed computing and Networking, ICDCN 2025

作者： Ji, Hongyan Kothapalli, Kishore Pemmaraju, Sriram V Singh, Ajitanshu Department of Computer Science The University of Iowa Iowa CityIA United States Center for Security Theory and Algorithmic Research IIIT Hyderabad Telangana Hyderabad India

ISBN: (纸本)9798400710629

In this paper, we present a deterministic Õ(log1/3)-round algorithm for the 2-ruling set problem in the sublinear Massively parallel Computation (MPC) model. This improves upon the fastest known deterministic 2-ruling set algorithm for this model, which is the Õ(√log;n)-round algorithm by Giliberti and Parsaeian (PODC 2024). Our result is obtained by derandomizing the "sample-and-gather"approach of Kothapalli, Pai, and Pemmaraju (FSTTCS 2020). The "sample-and-gather"approach involves making random sampling decisions, not just for the current iteration, but a batch of future iterations. Thus, derandomizing this approach requires the "fixing"of randomness for a batch of future iterations. We further extend our results to show that a β-ruling set for β ≥ 2 can be obtained in Õ(log1/2β - 1 Δ) deterministic rounds in the sublinear MPC model. Additionally, we present a deterministic β-ruling set algorithms for sparse graphs (i.e., bounded arboricity graphs) where β ≥ 2, which runs in Õ(log1/2β - 1 λ) rounds for arboricity-λ graphs in the sublinear MPC model. © 2025 Copyright held by the owner/author(s).

关键词： Consensus algorithm

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