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

共 24655 条记录，以下是601-610 订阅

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Improved Streaming computing Method Based on IoT Components of Virtual Power Plant 4

Improved Streaming Computing Method Based on IoT Components ...

引用

4th international conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Jiao, Xinyuan Feng, Dechao Wang, Mengqiang Cai, Zhencong State Grid Information & Telecommunication Group Co. Ltd Beijing China

ISBN: (纸本)9798350343618

The virtual power plant is an important part of the new power system. Its IoT components need to collect and process energy equipment measurement data in real time, which inevitably uses stream computing. This paper improves the original streaming computing method. By sorting the data according to the reference relationship, it avoids the centralized storage of a large amount of data, reduces the concurrent access to the data, and realizes that each step is parallel;by splitting the data, the data processing in the branch stream is simplified;by encapsulating computing units, the free distribution of computing on distributed computing nodes is realized, avoiding the bottleneck limitation of a single node, and realizing lateral expansion. This ensures stable system operation and efficient use of computing resources. © 2023 IEEE.

关键词： Data acquisition

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Designing an Independent Study to Create HPC Learning Experiences for Undergraduates 29

Designing an Independent Study to Create HPC Learning Experi...

引用

29th IEEE international conference on High Performance computing, Data and Analytics (HiPC)

作者： Vargas-Perez, Sandino Kalamazoo Coll Dept Comp Sci Kalamazoo MI USA

ISBN: (纸本)9798350333886

This paper aims to present a multi-tiered approach to designing learning experiences in HPC for undergraduate students that significantly reinforce comprehension of CS topics while working with new concepts in parallel and distributed computing. The paper will detail the experience of students working in the design, construction, and testing of a computing cluster including budgeting, hardware purchase and setup, software installation and configuration, interconnection networks, communication, benchmarking, and running parallel code using MPI and OpenMP. The case study of building a relatively low-cost, small-scale computing cluster that can be used as a template for CS senior projects or independent studies, also yielded an opportunity to involve students in the creation of teaching tools for parallel computing at many levels of the CS curriculum.

关键词： parallel computing HPC Undergraduate Education PDC Teaching Tools OpenMP MPI Cluster

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Q-Learning-Based Task Allocation for High-Speed distributed computing in Heterogeneous Clusters 39

Q-Learning-Based Task Allocation for High-Speed Distributed ...

引用

39th international conference on Information Networking, ICOIN 2025

作者： Yang, Seung Geon Kwon, Girim Lim, Seung-Chan Hankyong National University School of ICT Robotics and Mechanical Engineering Anseong Korea Republic of University of Seoul School of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798331506940

This paper proposes a Q-learning-based task allocation approach for wireless coded distributed computing systems with heterogeneous worker nodes. Task allocation in such systems is challenging due to the heterogeneity in computation and communication capabilities, leading to non-identically and independently distributed processing times across nodes. By modeling the task allocation problem as a Markov decision process and applying Q-learning, the master node learns to allocate tasks effectively, adapting to node heterogeneity and minimizing the average processing time. This approach highlights the potential of reinforcement learning to optimize distributed computing in heterogeneous environments. © 2025 IEEE.

关键词： parallel processing systems

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perun: Benchmarking Energy Consumption of High-Performance computing Applications 29th

perun: Benchmarking Energy Consumption of High-Performance C...

引用

29th international conference on parallel and distributed computing (Euro-Par)

作者： Muriedas, Juan Pedro Gutierrez Hermosillo Fluegel, Katharina Debus, Charlotte Obermaier, Holger Streit, Achim Goetz, Markus Karlsruhe Inst Technol KIT Steinbuch Ctr Comp SCC Hermann Von Helmholtz Pl 1 D-76344 Eggenstein Leopoldshafen Germany Helmholtz AI Karlsruhe Germany

ISBN: (纸本)9783031396977;9783031396984

Looking closely at the Top500 list of high-performance computers (HPC) in the world, it becomes clear that computing power is not the only number that has been growing in the last three decades. The amount of power required to operate such massive computing machines has been steadily increasing, earning HPC users a higher than usual carbon footprint. While the problem is well known in academia, the exact energy requirements of hardware, software and how to optimize it are hard to quantify. To tackle this issue, we need tools to understand the software and its relationship with power consumption in today's high performance computers. With that in mind, we present perun, a Python package and command line interface to measure energy consumption based on hardware performance counters and selected physical measurement sensors. This enables accurate energy measurements on various scales of computing, from a single laptop to an MPI-distributed HPC application. We include an analysis of the discrepancies between these sensor readings and hardware performance counters, with particular focus on the power draw of the usually overlooked non-compute components such as memory. One of our major insights is their significant share of the total energy consumption. We have equally analyzed the runtime and energy overhead perun generates when monitoring common HPC applications, and found it to be minimal. Finally, an analysis on the accuracy of different measuring methodologies when applied at large scales is presented.

关键词： Energy Benchmarking High-performance computing Artificial Intelligence distributed Memory System

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Spiking Neural Network for Microseismic Events Detection Using distributed Acoustic Sensing Data 6th

Spiking Neural Network for Microseismic Events Detection Usi...

引用

6th international conference on Soft computing and Data Mining (SCDM)

作者： Bin Shahabudin, Mohd Safuwan Krishnan, Nor Farisha Binti Muhamad Mausor, Farahida Hanim Binti Univ Teknol PETRONAS Seri Iskandar 32610 Perak Malaysia

ISBN: (纸本)9783031669644;9783031669651

Microseismic detection events are critical in monitoring subsurface activities, including hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage and reservoir characterization, to guarantee safe and efficient energy extraction. This presents significant challenges as it generates large amounts of data. Despite the recent machine learning techniques being increasingly integrated into fiber-optic distributed acoustic sensor (DAS) systems to enhance their intelligent recognition capabilities, there is still a need to solve this. In some research, it has been observed that computational speed is time-consuming and overfitting, thus necessitating a more extensive investigation of this analysis. This study proposes a novel approach using DAS data to enhance microseismic event detection's precision, overfitting issue, and interpretability. This approach utilizes a specifically designed neural network architecture. The deep learning approach is highly effective for the real-time management of the substantial amounts of data recorded by DAS equipment. Three phases of research methodology are proposed. The contribution of this research is that spiking neural network architecture for microseismic detection will bring advancements in microseismic monitoring.

关键词： Deep Learning Signal Processing Microseismic Event Detection Spiking Neural Network distributed Acoustic Sensing

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Optimizing the parallelism of Communication and Computation in distributed Training Platform 23rd

Optimizing the Parallelism of Communication and Computation ...

引用

23rd international conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Hou, Xiang Yuan, Yuan Ma, Sheng Xu, Rui Wang, Bo Li, Tiejun Jiang, Wei Wu, Lizhou Zhang, Jianmin Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9789819708338;9789819708345

With the development of deep learning, DNN models have become more complex. Large-scale model parameters enhance the level of AI by improving the accuracy of DNN models. However, they also present more severe challenges to the hardware training platform for training a large model needs a lot of computing and memory resources, which can easily exceed the capacity of an accelerator. In addition, with the increasing demand for the accuracy of DNN models in academia and industry, the number of training iterations is also skyrocketing. In these backgrounds, more accelerators are integrated on a hierarchical platform to conduct distributed training. In distributed training platforms, the computation of the DNN model and the communication of the intermediate parameters are handled by different hardware modules, so their degree of parallelism profoundly affects the training speed. In this work, based on the widely used hierarchical Torus-Ring training platform and the Ring All-Reduce collective communication algorithm, we improve the speed of distributed training by optimizing the parallelism of communication and computation. Specifically, based on the analysis of the distributed training process, we schedule the computation and communication so that they execute simultaneously as much as possible. Finally, for data parallelism and model parallelism, we reduce the communication exposure time and the computation exposure time, respectively. Compared with the previous work, the training speed (including 5 training iterations) of the Resnet50 model and the Transformer model is increased by 23.77%-25.64% and 11.66%-12.83%.

关键词： distributed training simulator Collective communication operation Communication exposure time Computation exposure time

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Fostering Sustainable Development through distributed Intelligence Applications

Fostering Sustainable Development through Distributed Intell...

引用

2024 OPJU international Technology conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Durga, C.S.L. Vijaya Agrawal, Shruti Anandhi, R.J. Singh, Navdeep Kalra, Ravi Adnan, Myasar Mundher Institute of Aeronautical Engineering Hyderabad Dundigal India Ies College of Technology Department of Computer Science & Engineering Ma. P Bhopal India New Horizon College of Engineering Department of Information Science Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India The Islamic University Najaf Iraq

ISBN: (纸本)9798350373783

SDODI, which stands for Sustainable Development Optimization using distributed Intelligence, advances sustainable development. SDODI is the first to examine environmental, social, and economic challenges together using EcoOpt, InclusNet, and ResilienceHub. Environmental optimization program EcoOpt increases the total effect while ensuring resource efficiency and environmentally friendly behavior. InclusNet uses math to include everyone and make communities more active, varied, and accessible. The ResilienceHub algorithm prioritizes financial stability, flexibility, and economic power. Sustainable initiatives are typically divided by traditional approaches. SDODI utilizes information to better allocate resources, engage the community, and strengthen the economy. SDODI is unique because it adapts to changing situations, giving it a comprehensive sustainable growth solution. SDODI is new because it supports and improves sustainability ideals. © 2024 IEEE.

关键词： Sustainable development goals

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Research Summary of Load Balancing and Interoperability in distributed IoT 13

Research Summary of Load Balancing and Interoperability in D...

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13th international conference on advances in computing, Control, and Telecommunication Technologies, ACT 2022

作者： Aradhya, Suvarna N. Priya, Rashmi JSS Academy of Technical Education Department of ECE Uttar Pradesh Noida India GD Goenka University Department of MCA Haryana Gurgaon India

ISBN: (纸本)9781713857938

With the evolutionary era of IoT and its implied changes in area of computing, distributed systems have resurfaced as Cloud computing, Grid computing, Utility computing, Cluster computing, Edge computing and Fog computing. Load balancing is very vital in distributed systems for optimum usage of resources: computing power, storage, bandwidth, latency while maintaining the required levels of security and fault tolerance. The research on this topic is very extensive with varying algorithms, architectures, programming models, communication protocols. This research survey paper makes a comparison of the research works, research gaps and related future scope for improvement through bridging the gap. © Grenze Scientific Society, 2022.

关键词： Resource allocation

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A Hybrid Resampling Technique with Adaptive Intervals Used in the parallel/distributed Particle Filters 7

A Hybrid Resampling Technique with Adaptive Intervals Used i...

引用

7th international conference on Computer Science and Artificial Intelligence, CSAI 2023

作者： Zhang, Xudong Gu, Feng Zhong, Wei Ai, Chunyu Department of Mathematics and Computer Science University of South Carolina Upstate SpartanburgSC 29303 United States Department of Computer Science The College of Staten Island Staten IslandNY10314 United States

ISBN: (纸本)9798400708688

parallel/distributed particle filters estimate the states of dynamic systems by using Bayesian interference and stochastic sampling techniques with multiple processing units (PUs). The sampling procedure and the resampling procedure alternatively execute to estimate the states in particle filters. There are two basic types of resampling techniques used in parallel/distributed particle filters. They are centralized resampling and decentralized resampling. The high communication between PUs in centralized resampling lowers the speedup factor in parallel computing but improves the estimation accuracy. The decentralized resampling can avoid the communication and improve the performance. Some types of hybrid resampling techniques mainly execute the decentralized resampling and only invoke the centralized resampling with constant intervals to achieve ideal performance without losing the estimation accuracy. However, the constant intervals cannot guarantee that the centralized resamplings are invoked timely. In this study, we proposed a hybrid resampling technique with adaptive intervals between centralized resamplings to overcome that issue. The experimental results indicate that the proposed hybrid resampling technique is able to improve the performance and the estimation accuracy. © 2023 ACM.

关键词： Stochastic systems

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SPM-GCN: An adaptive reordering algorithm for sparse LU factorization via GCN 29

SPM-GCN: An adaptive reordering algorithm for sparse LU fact...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Tian, Min Liu, Huazeng Wang, Junjie Liu, Qi Du, Wei Zhang, Zanjun China Shandong Fundamental Research Center for Computer Science Jinan Key Laboratory of High Performance Computing Shandong Provincial Key Laboratory of Computer Networks China Xidian University Shaanxi Key Laboratory of Large Scale Electromagnetic Computing Xi'an710071 China

ISBN: (纸本)9798350330717

Sparse LU factorization is a critical kernel in scientific computing and engineering applications. A better nonzero pattern of sparse matrixes can accelerate LU factorization by reordering. Traditionally it's difficult to predict which non-zero pattern is optimal for a sparse matrix. In this paper, we proposed a graph convolutional neural network (GCN) for adaptively selecting the optimal reordering algorithm from five candidate reordering algorithms in the Matrix preprocessing step, referred to as SPM-GCN. After using our SPM-GCN, the average numerical factorization time outperformed the other five algorithms, and the average numerical factorization time was reduced by 17.3% compared with the default method of swSuperLU. © 2023 IEEE.

关键词： Graph Convolutional Network reordering algorithm sparse linear system solver Sparse LU factorization

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