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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421-430 订阅

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BalanceNet Orchestrator: A KQV-based Dynamic Task Allocation for distributed Deep Learning 38

BalanceNet Orchestrator: A KQV-based Dynamic Task Allocation...

引用

38th international conference on Information Networking (ICOIN)

作者： Sun, Teh-Jen Ngo, Thien-Thu Huh, Eui-Nam Kyung Hee Univ Dept Artificial Intelligence Yongin South Korea Kyung Hee Univ Dept Comp Sci & Engn Yongin South Korea

ISBN: (纸本)9798350330946;9798350330953

In Artificial Intelligence(AI), training expansive models with billions of parameters necessitates substantial computational resources. This requirement has led to the adoption of parallel computing frameworks. However, these frameworks often confront node performance imbalances due to disparities in computational capabilities and network conditions. To address this issue, we introduce the BalanceNet Orchestrator(BNO), a dynamic task allocation algorithm designed to equilibrate workloads in parallel training environments. BalanceNet Orchestrator assesses and adjusts to node-specific performance in real time, facilitating optimal workload distribution and resource utilization. This method significantly enhances training efficiency and accelerates model convergence, presenting an efficient approach for training large-scale AI models within parallel training architecture.

关键词： AI parallel training distributed heterogeneous environment

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Feasibility and Utilization of Information Integration Technology in Digital Power grid

Feasibility and Utilization of Information Integration Techn...

引用

2023 international conference on Internet of Things, Robotics and distributed computing, ICIRDC 2023

作者： Yan, Songqi Power Research Institute of State Grid Shaanxi Electric Power Company Limited Shaanxi Xi'an710000 China

ISBN: (纸本)9798350375275

Modern society has put forward higher requirements for the power system, and the reliability and intelligence of the power system have received widespread attention. In this paper, data transmission in the digitalized grid includes three aspects, that is, communication between intelligent electronic devices at the device level, communication within the digitalized substation, and communication between the digitalized substation and the dispatch center. This article presented a unified programming (or component) interface for heterogeneous systems, which was based on a unified programming (or component) interface to obtain common data and exchange information in different ways. At the same time, file interaction mode can also be adopted for the security requirements of various systems. In the testing of digital power grid environment, the efficiency of data integration was evaluated. The efficiency of data integration in the first test was 94.9%, and the efficiency of data integration in the second test was 94.5%. This article focused on integrating and organizing various data in the power grid, with the aim of providing rich and effective basic information of the power grid for higher-level application systems. © 2023 IEEE.

关键词： Data integration

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CAPTURE: Memory-Centric Partitioning for distributed DNN Training with Hybrid parallelism 30

CAPTURE: Memory-Centric Partitioning for Distributed DNN Tra...

引用

30th international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Dreuning, Henk Verstoep, Kees Bal, Henri E. van Nieuwpoort, Rob V. Univ Amsterdam Amsterdam Netherlands Vrije Univ Amsterdam Amsterdam Netherlands Leiden Univ Leiden Netherlands

ISBN: (纸本)9798350383225

Deep Learning (DL) model sizes are increasing at a rapid pace, as larger models typically offer better statistical performance. Modern Large Language Models (LLMs) and image processing models contain billions of trainable parameters. Training such massive neural networks incurs significant memory requirements and financial cost. Hybrid-parallel training approaches have emerged that combine pipelining with data and tensor parallelism to facilitate the training of large DL models on distributed hardware setups. However, existing approaches to design a hybrid-parallel partitioning and parallelization plan for DL models focus on achieving high throughput and not on minimizing memory usage and financial cost. We introduce CAPTURE, a partitioning and parallelization approach for hybrid parallelism that minimizes peak memory usage. CAPTURE combines a profiling-based approach with statistical modeling to recommend a partitioning and parallelization plan that minimizes the peak memory usage across all the Graphics Processing Units (GPUs) in the hardware setup. Our results show a reduction in memory usage of up to 43.9% compared to partitioners in state-of-the-art hybrid-parallel training systems. The reduced memory footprint enables the training of larger DL models on the same hardware resources and training with larger batch sizes. CAPTURE can also train a given model on a smaller hardware setup than other approaches, reducing the financial cost of training massive DL models.

关键词： HPC Deep Learning Hybrid parallelism GPU Memory

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Soft Actor-Critic-Based grid Dispatching with distributed Training

Soft Actor-Critic-Based Grid Dispatching with Distributed Tr...

引用

2023 international conference on Mobile Internet, Cloud computing and Information Security, MICCIS 2023

作者： Zhao, Long Li, Fei Zhou, Yuan Fan, Wenbin GuoChuang Cloud Technology LTd Hefei China

ISBN: (纸本)9798350300932

In response to the high energy consumption of electricity, it has become an important challenge to develop a reasonable grid dispatching strategy in power dispatch to be able to achieve energy saving and emission reduction. Some studies have applied AI technologies to grid dispatching problems, but existing studies do not consider the increasingly complex grid environment that makes it difficult to perform predictive power dispatching. Reinforcement learning has the feature of imitating human learning by interacting with the environment, which is ideal for solving complex grid intelligent dispatching problems. In this paper, we propose a new online grid dispatching method based on deep reinforcement learning, which aims to maximize the proportion of renewable energy supply and grid stability. To efficiently utilize experience in the reinforcement learning training process, we design a grid dispatching strategy with Soft Actor-Critic (SAC) algorithm based on distributed training, which can learn the optimal grid regulation policy through a multi-process approach. The simulation results show that the reward value variance is improved by more than 20% than other methods. It verifies the validity of the method in this paper. © 2023 IEEE.

关键词： Reinforcement learning

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Modeling training content for software engineers in parallel computing 15

Modeling training content for software engineers in parallel...

引用

15th international conference on Mathematics, Science and Technology Education, ICon-MaSTEd 2023

作者： Sitsylitsyn, Y.O. Osadchyi, V.V. Kruglyk, V.S. Kuzminska, O.H. Bogdan Khmelnitsky Melitopol State Pedagogical University 59 Naukovoho mistechka Str. Zaporizhzhia69000 Ukraine Borys Grinchenko Kyiv University 18/2 Bulvarno-Kudriavska Str. Kyiv04053 Ukraine National University of Life and Environmental Sciences of Ukraine 15 Heroyiv Oborony Str. Kyiv03041 Ukraine Academy of Cognitive and Natural Sciences 54 Gagarin Ave. Kryvyi Rih50086 Ukraine

This study proposes a robust framework for the training of software engineers specializing in parallel computing. We first curated essential content for parallel computing education based on international standards and evolving recommendations from computing Curricula. We then systematically structured the content and designed a well-defined learning pathway for aspiring software engineers. Concurrently, we conducted a comprehensive assessment of the current state of training for parallel computing in Ukrainian higher education institutions. We analyzed bachelor's programs in Information Technologies and scrutinized individual course syllabi to identify valuable insights. By merging our findings with the review of educational programs, we formulated a comprehensive model for training in parallel computing. We also examined the pivotal role of the course parallel and distributed computing in the developed curriculum and identified essential tools and methodologies for developing parallel and distributed programs. Our research contributes to the advancement of parallel computing education and provides a valuable reference point for curriculum designers and educators. © Published under licence by IOP Publishing Ltd.

关键词： Engineers

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Research on Abnormal Power Consumption Behavior Detection and Load Forecasting System of Power Big Data Based on FCM and Wavelet Neural Network 14th

Research on Abnormal Power Consumption Behavior Detection an...

引用

14th international conference on Frontier computing, FC 2024

作者： Pan, Jingni Chai, Yufeng Lv, Jiayu Wang, Xiaotian State Grid Shizuishan Power Supply Co. Ningxia China State Grid Ningxia Power Supply Co. Ningxia China State Grid Wuzhong Power Supply Co. Ningxia China

ISBN: (纸本)9789819627974

With the progress of smart grid, power systems are faced with more and more complex data challenges, including large-scale data, multi-sample types and low resource integration. This research takes power big data as the object, carries out abnormal power consumption behavior detection and load prediction based on Hadoop platform, and successfully realizes the visualization processing of power data. We thoroughly analyzed the needs of power big data visualization and designed the overall Hadoop-based architecture. In terms of key technologies, we adopt the visualization method based on geographic information, and use MapReduce to carry out parallel computation, and effectively identify and display different electrical behaviors. In addition, we proposed a wavelet neural network power load forecasting method based on FCM, and personalized classification of power users through FCM clustering. Combined with the prediction ability of wavelet neural network, accurate power load prediction was successfully realized, and parallel calculation was carried out on the distributed platform to further improve the prediction accuracy. Finally, we built a Hadoop big data experiment platform to verify the experimental results and performance of the proposed method. The experimental results show that our research has the ability to effectively cope with the big data challenges of power system, and provides important support and guidance for the safe and stable operation of power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Abnormal Power Consumption Behavior Detection FCM Hadoop Platform Power Big Data Wavelet Neural Network

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ADSwitch: Adaptive CPU Scheduling for distributed Systems 21

ADSwitch: Adaptive CPU Scheduling for Distributed Systems

引用

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： Zhong, Yidong Ao, Yang Li, Dingding Li, Jianguo Zeng, Deze South China Normal University School of Computer Science Guangdong Guangzhou510631 China China University of Geosciences School of Computer Science Hubei Wuhan430074 China

ISBN: (纸本)9798350329223

In traditional CPU scheduling systems, it is challenging to customize scheduling policies for datacenter workloads. Therefore, distributed cluster managers can only perform coarse-grained job scheduling rather than fine-grained CPU scheduling, although the latter is also critical to application performance in the clusters. Some scheduling systems have been proposed to facilitate the implementation and deployment of custom policies. However, these systems neither adequately capture the resource requirements of workloads nor cooperatively manage the CPU scheduling of distributed nodes, thus may failing to fully utilize CPU resources in the clusters. To tackle these issues, we present ADSwitch, a distributed CPU scheduling system that manages CPU resources adaptively in datacenter clusters. We train an efficient workload classification model based on a recurrent neural network (RNN), which enables ADSwitch to dynamically identify the demand of the running workload and switch to an appropriate scheduling policy through a reliable online update mechanism. We implement ADSwitch and evaluate it in a distributed cluster with synthetic workloads. The experimental results demonstrate that ADSwitch achieves up to 33% lower tail latency and 28% higher throughput than the existing scheduling systems without workload characteristic awareness. © 2023 IEEE.

关键词： Recurrent neural networks

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A Improved Consistent Hash Algorithm in Heterogeneous Edge computing 29

A Improved Consistent Hash Algorithm in Heterogeneous Edge C...

引用

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

作者： Chai, Ruonan Gao, Shuai Hu, Zhenghao Beijing Jiaotong University School of Electronic Information Engineering Beijing China

ISBN: (纸本)9798350330717

Load balancing plays a critical role in large-scale heterogeneous edge computing, aiming to enhance the accuracy of computing resource matching and reduce processing delays caused by imbalanced resource allocation. In highly heterogeneous distributed computing environments, scalability and accuracy in matching computing resources are essential. The Consistent Hashing (CH) algorithm is well-known for its simplicity and strong scalability, enabling extensive and flexible scheduling strategies. It achieves load balancing by evenly distributing requests among different nodes. However, the CH algorithm primarily focuses on load balancing in storage resource scenarios and often neglects load balancing for computing resources, potentially leading to reduced accuracy in matching computing resources and prolonged processing delays. This paper introduces an improved CH algorithm called chordDC, specifically designed for distributed computing scenarios. It takes into account the unified representation of user tasks and computing resources, incorporating additional discriminative criteria in the selection of computing nodes to achieve a more precise resource allocation. Through a comparative analysis with two baseline algorithms, the proposed algorithm demonstrates effective performance improvements, enhancing the accuracy of computing resource matching by 40%-50% and significantly reducing computing processing latency. © 2023 IEEE.

关键词： Heterogeneous edge computing Large scale Load balancing Matching accuracy of computing resources

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E-Agriculture Integration with Cloud computing 7

E-Agriculture Integration with Cloud Computing

引用

7th international conference on parallel, distributed and grid computing, PDGC 2022

作者： Jain, Swastika Verma, Ishu Sharma, Sachin Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9781665454018

In an effort to offer compute services to a wide range of individuals, cloud computing is a paradigm that involves integrating high-end technology infrastructure into the backend. Although cloud computing was first intended to be a commercial process, it now significantly affects many different fields, including scientific, technical, academic, and social ones. The rapid growth in population of our country demands a need for a customized solution for e-agriculture. India has a wide variety of crops and soil. Therefore a cloud based e-agriculture system would be beneficial for country's growth. Cloud computing provides applications like AgroCloud storage of data such as soil, fertilizers, pesticides can be stored. For easy communication with the farmers, a cloud -based English to native language translation application should be deployed. The proposed paper is helpful for enhancing agricultural productivity and managing agro-product expenses. Cloud computing has had a positive effect on the agricultural field and the services it offers to its customers. © 2022 IEEE.

关键词： Augmented reality

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Artificial Intelligence: Trends and Challenges 7

Artificial Intelligence: Trends and Challenges

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7th international conference on parallel, distributed and grid computing, PDGC 2022

作者： Karthick, A.V. Gopalsamy, S. Alagappa University Department of Computational Logistics Karaikudi India Alagappa University Department of International Business Karaikudi India

ISBN: (纸本)9781665454018

Artificial Intelligence is an interdisciplinary field that combines Machine Learning, Big Data, Cloud computing, and Information Theory. The major advantage of machine learning is that it builds on prior experience, so machines are self-learning and can predict the future without error. It is a kind of decision-making approach that is applied in various domains like social media, health care, finance, etc. In Big Data and Cloud computing, the volume of data is sustainable and increasing, which is processed with low power consumption with the help of machine learning. It is possible to support the next generation of smart grid by providing the platform that integrates, in addition to the communication infrastructure, the AI and IoT support, providing a multitenant system, thanks to the future Massive Internet of Things (MIoT), one of the pillars of the 5G/6G network factory. Rapid development in Deep Learning, Artificial Intelligence (AI) and Machine Learning (ML) will give better results for future applications. This paper focuses on the trends and challenges related to AI is discussed. © 2022 IEEE.

关键词： Decision making

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