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检索条件"任意字段=Distributed Memory Computing Conference"

共 8902 条记录，以下是11-20 订阅

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Distributing Context-Aware Shared memory Data Structures: A Case Study on Singly-Linked Lists 25

Distributing Context-Aware Shared Memory Data Structures: A ...

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

作者： Ravishankar, Raaghav Kulkarni, Sandeep Peri, Sathya Sharma, Gokarna Michigan State University East LansingMI United States Indian Institute of Technology Hyderabad Hyderabad India Kent State University KentWA United States

ISBN: (纸本)9798400710629

In this paper, we study the partitioning of a context-aware shared memory data structure so that it can be implemented as a distributed data structure running on multiple machines. By context-aware data structures, we mean that the result of an operation not only depends upon the value of the shared data but also upon the previous operations performed by the same client. While there is substantial work on designing distributed data structures, designing distributed context-aware data structures has not received much attention. As a case study of the context-aware data structure, we transform a shared memory context-aware lock-free singly-linked list into a distributed lock-free context-aware singly-linked list. We present two protocols that preserve these properties of client-visible operations of the linked list. In both protocols, the client-visible operations remain lock-free. Also, our transformation approach does not utilize any hardware primitives (except a compare-and-swap operation on a single word). We note that our transformation is generic and can be used for other lock-free context-aware data structures that can be constructed from singly-linked lists. © 2025 Copyright held by the owner/author(s).

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Optimal Dispersion in Triangular Grids: Achieving Efficiency Without Prior Knowledge 21st

Optimal Dispersion in Triangular Grids: Achieving Efficiency...

引用

21st International conference on distributed computing and Intelligent Technology

作者： Pandit, Himani Pandit, Supantha Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar Gujarat India

ISBN: (纸本)9783031814037;9783031814044

In the dispersion problem, a group of k <= n mobile robots, initially placed on the vertices of an anonymous graph G with n vertices, must redistribute themselves so that each vertex hosts no more than one robot. We address this challenge on an anonymous triangular grid graph, where each vertex can connect to up to six adjacent vertices. We propose a distributed deterministic algorithm that achieves dispersion on an unoriented triangular grid graph in O(root n) time, where n is the number of vertices. Each robot requires O(log n) bits of memory. The time complexity of our algorithm and the memory usage per robot are optimal. This work builds on previous studies by Kshemkalyani et al. [WALCOM 2020 [17]] and Banerjee et al. [ALGOWIN 2024 [3]]. Importantly, our algorithm terminates without requiring prior knowledge of n and resolves a question posed by Banerjee et al. [ALGOWIN 2024 [3]].

关键词： distributed algorithms Mobile robots Dispersion Triangular grid Deterministic algorithms

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Performance Optimization and Comparative Analysis of distributed Big Data Processing Framework

Performance Optimization and Comparative Analysis of Distrib...

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2024 International conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Xie, Xinyan Chongqing Three Gorges Vocational College 404155 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

The research and application of big data processing and analysis have been quite mature, but more and more fields have put forward demands for real-time analysis and rapid response of fast and massive distributed big data. distributed big data framework performance needs to be optimized. To this end, the article studies and analyzes the platform architecture and data computing model of typical batch processing technology Hadoop, memory computing technology Spark, and stream computing technology Storm, and summarizes the similarities and differences of these three big data processing technologies. Then, on the basis of studying the big data platform and traditional cardinality estimation algorithm, a HyperLogLog algorithm application model based on the streaming platform is proposed, and the cardinality calculation is performed in the Storm processing engine to achieve the performance of the distributed big data processing framework. optimization. The results show that the HyperLogLog cardinality estimation algorithm of the Storm stream computing platform can be used to optimize the performance of the distributed big data processing framework. © 2025 SPIE.

关键词： Data centers

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Research on Market Power Platform for Power Generation Based on Spark distributed Framework computing

Research on Market Power Platform for Power Generation Based...

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2025 Asia-Europe conference on Cybersecurity, Internet of Things and Soft computing, CITSC 2025

作者： Fan, Wenjie Jia, Mingzhen Liu, Zhitong State Grid Liaoning Marketing Service Center Shenyang110006 China

ISBN: (纸本)9798331504205

The Spark distributed algorithm is a distributed computing implementation based on the map reduce algorithm, which has the advantages of Hadoop MapReduce. However, unlike MapReduce, the intermediate output and results of jobs can be saved in memory, eliminating the need to read and write HDFS. Therefore, Spark is better suited for iterative map reduce algorithms such as data mining and machine learning. Spark distributed algorithm has been widely used in the Internet, advertising, finance and other industries, but less in the power industry. At present, the research on market power risk of power generators has been very in-depth, but there is little research on simulation platform algorithms for identifying market power risk. This article proposes a market power identification platform for power generation enterprises based on the Spark distributed framework, which can effectively accelerate the calculation speed of market power indicators. Furthermore, using a certain regional power grid as a simulation example, the monopoly situation of various power generation enterprises and the entire wholesale market is analyzed, proving the effectiveness of the Spark distributed framework algorithm. As the number of grid nodes increases, the advantages of the distributed algorithm will become more prominent. © 2025 IEEE.

关键词： Market Power Platform Power Generation Spark distributed Framework computing

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Out-of-memory GPU Sorting Using Asynchronous CUDA Streams 25th

Out-of-Memory GPU Sorting Using Asynchronous CUDA Streams

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25th International conference on Parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： HoseinyFarahabady, MohammadReza Zomaya, Albert Y. School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia

ISBN: (纸本)9789819642069

Graphics Processing Units (GPUs) are widely used as powerful hardware accelerators for data-intensive tasks. However, their efficacy can be hindered by constraints in device memory and data transfer speeds via the PCI-E link. This paper explores the use of GPUs for executing scalable sorting operations that exceeds the GPU’s global memory by a factor of at least two orders of magnitude. To address this challenge, we leverage the power of NVIDIA’s asynchronous CUDA streams. Our solution enhances GPU performance by implementing a pipeline model to offload sorting operations from the Solid-State Drive (SSD) to GPU memory via the host’s main memory. This strategy maximizes the multiway concurrency level to reduce the overhead associated with data transfer within the GPU memory hierarchy. We perform extensive performance evaluations by sorting 128 billion data elements initially stored on an external NVMe SSD. The results confirm the effectiveness of our approach, showcasing a speed-up of 4.3× compared to the results for CPU-based out-of-memory sorting of skewed data based on Intel’s Threading Building Blocks (TBB) sorting algorithm. Our results include the cost of transfer times to and from the main memory, a factor often overlooked in similar studies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Graphics processing unit

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Inference method for power large-scale model operation based on distributed computing resource scheduling

Inference method for power large-scale model operation based...

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2024 International conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Huang, Xiaoguang Qin, Yu Liu, Yaqing Cui, Yingbao Guo, Ting Gu, Qingtong State Grid Information and Communication Industry Group Co. Ltd Beijing102211 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

Electric power computing resources are distributed in the respective regions of provincial data centers and transmission and transformation equipment, and the scheduling efficiency of computing resources is limited by conditions such as long distribution locations. Composite data model inference has been widely used in power equipment maintenance analysis, and large-scale model inference requires the use of distributed computing resources. In order to improve the scheduling and fusion efficiency of distributed computing power resources, this paper adopts a lightweight language model to infer and calculate the scheduling parameters of computing power resources in different regions. At the same time, in order to make more rational use of computing power resources, the intelligent models and algorithms used for power equipment maintenance are optimized. Configure the adaptation parameters of the model according to different application scenarios, and adjust the fusion method of input data for the fault diagnosis model and the classification strategy for the fault prediction model separately. Using a fine-tuning language model to map the predicted results to the semantics of power business, and then enhancing the rationality and interoperability of the answer results based on historical knowledge memory, ultimately pushing the reasoning results of the reasons and the classification results of the equipment defect diagnosis classifier to the equipment diagnosis designer. © 2025 SPIE.

关键词： Data modeling distributed computing Education and training Video Machine learning Semantics Video coding Instrument modeling Power grids Target detection

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On Constructing a Byzantine Linearizable SWMR Atomic Register from SWSR Atomic Registers 25

On Constructing a Byzantine Linearizable SWMR Atomic Registe...

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

作者： Kshemkalyani, Ajay D. Piduguralla, Manaswini Peri, Sathya Misra, Anshuman University of Illinois Chicago ChicagoIL United States Indian Institute of Technology Hyderabad Hyderabad India Purdue University Fort Wayne Fort WayneIN United States

ISBN: (纸本)9798400710629

The SWMR atomic register is a fundamental building block in shared memory distributed systems and implementing it from SWSR atomic registers is an important problem. While this problem has been solved in crash-prone systems, it has received less attention in Byzantine systems. Recently, Hu and Toueg gave such an implementation of the SWMR register from SWSR registers. While their definition of register linearizability is consistent with the definition of Byzantine linearizability of a concurrent history of Cohen and Keidar, it has several drawbacks. In this paper, we give a stronger definition of a Byzantine linearizable register that overcomes these drawbacks. The construction of a Byzantine linearizable SWMR atomic register from SWSR registers that meets our stronger definition is given in the full arxiv report. The construction is correct when n > 3f, where n is the number of readers, f is the maximum number of Byzantine readers, and the writer can also be Byzantine. The construction relies on a public-key infrastructure. © 2025 Copyright held by the owner/author(s).

关键词： distributed computer systems

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distributed Population-Based Simultaneous Perturbation Stochastic Approximation for Fine-Tuning Large Language Models 21st

Distributed Population-Based Simultaneous Perturbation Stoch...

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21st Pacific Rim International conference on Artificial Intelligence, PRICAI 2024

作者： Tan, Yajing Huang, Yuwei Duan, Qiqi Yang, Yijun Shi, Yuhui Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9789819601219

Very recently, a memory-efficient version (called MeZO) of simultaneous perturbation stochastic approximation (SPSA), one well-established zeroth-order optimizer from the automatic control community, has shown competitive fine-tuning performance on large language models (LLMs) with billions of weights under the limited memory of a single GPU, which often hinders the direct usage of the standard backpropagation (BP) update. Furthermore, population-based evolutionary methods (such as evolution strategies from OpenAI and population-based training from DeepMind) have shown a promising direction to improving and scaling up various large-scale AI systems via efficiently utilizing increasingly available distributed computing resources, owing to their relatively simple yet efficient data and control flows. In this paper, we propose a population-based SPSA for fine-tuning LLMs with non-differentiable objective functions. Specifically, our proposed method combines the desirable exploration ability of distributed population-based evolutionary methods with the powerful exploitation ability of SPSA in the modern distributed computing environment wherein the efficient implementation of BP is still challenging because of its complex dependencies in forward-pass and backward-pass information flows. Simulation experiments on a set of challenging (non-differentiable) natural language process tasks empirically validate the potential of our method on fine-tuning even in a relatively small-scale distributed computing scenario. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stochastic systems

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Task prioritization offloading scheme for vehicular edge computing networks: a game-theoretic DRL approach 4

Task prioritization offloading scheme for vehicular edge com...

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4th International conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Sun, Jiaojiao Jiang, Li Cao, Yuwei Wang, Shubin Inner Mongolia University Huhehaote China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

With the development of Vehicular Edge computing (VEC) computing architectures, in the study of task offloading problem, based on the differences in task delay sensitivity and the dynamic characteristics of environmental information, this paper designs a distributed DRL framework based on non-cooperative game, and introduces a memory mechanism (RNN) and a shared experience mechanism (Shared Experience Actor-Critic) in the MADDPG algorithm, which improves the learning efficiency of the algorithm as well as the convergence speed by capturing and sharing the time series data across time steps. © 2025 SPIE.

关键词： Deep reinforcement learning

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Scheduling Big Machine Learning Tasks on Clusters of Heterogeneous Edge Devices 17

Scheduling Big Machine Learning Tasks on Clusters of Heterog...

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17th International conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Mathkar, Saumya Aiyer, Shreya Bapat, Yashovardhan Pinki, Pinki Paul, Arnab K. Naik, Vinayak BITS Pilani Department of CSIS Goa India

ISBN: (纸本)9798331531195

Edge computing has transformed machine learning by using computing closer to the data sources, thereby reducing latency. The ever-increasing volume of data has necessitated forming clusters of edge devices, possibly with heterogeneous capabilities. Managing heterogeneous resources such as computation and memory remains challenging. Given the capabilities of the edge devices, we need a simple technique suitable for an Edge computing *** introduce a scheduling mechanism that leverages Integer Linear Programming (ILP) to optimize the overall computation time of ML-based tasks. We implement our scheduling mechanism that efficiently allocates resources, ensuring tasks are executed parallel across the cores of edge devices in a cluster by minimizing computation time. For tasks large enough to fit on any core, we leverage distributed learning to train the model in pieces and later combine them. We employ our ILP-based scheduler for efficient task allocation and compare its performance with a Greedy, simple approach based on best-fit technique. We evaluate our approach on three cases of sets of tasks on the spectrum of uniformity and size. Our results demonstrate two times the speed gain for our ILP-based approach over Greedy for the category with the least uniformity and large size. © 2025 IEEE.

关键词： distributed computing Edge computing Optimization

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