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

共 14635 条记录，以下是161-170 订阅

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Unleashing Multicore Strength for Efficient Execution of Blockchain Transactions 21st

Unleashing Multicore Strength for Efficient Execution of Blo...

引用

21st international conference on distributed Computing and Intelligent Technology

作者： Ravish, Ankit Tejwani, Akshay Manaswini, Piduguralla Peri, Sathya Indian Inst Technol Hyderabad Hyderabad Telangana India

ISBN: (纸本)9783031814037;9783031814044

Blockchain technology is characterized by its distributed, decentralized, and immutable ledger system which serves as a fundamental platform for managing smart contract transactions (SCTs). However, these SCTs undergo sequential validation within a block which introduces performance bottlenecks in blockchain. In response, this paper introduces a framework called the Multi-Bin parallel Scheduler (MBPS) designed for parallelizing blockchain smart contract transactions to leverage the capabilities of multicore systems. Our proposed framework facilitates concurrent execution of SCTs, enhancing performance by allowing non-conflicting transactions to be processed simultaneously while preserving deterministic order. The framework comprises of three vital stages: conflict detection, bin creation, and execution. We conducted an evaluation of our MBPS framework in Hyperledger Sawtooth v1.2.6, revealing substantial performance enhancements compared to existing parallel SCT execution frameworks across various smart contract applications. This research contributes to the ongoing optimization efforts in blockchain technology demonstrating its potential for scalability and efficiency in real-world scenarios.

关键词： Blockchain Smart Contracts parallel Execution Conflict Detection

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distributed Optimal Control Strategy of New Energy in Novel Power systems 1

Distributed Optimal Control Strategy of New Energy in Novel ...

引用

1st international conference on Cyber-Energy systems and Intelligent Energy, ICCSIE 2022

作者： Jia, Yilin Zheng, Qiao Pan, Zhiming Wang, Yibo Tian, Runduo Anshan Power Supply Company State Grid Liaoning Electric Power Co. LTD. Anshan114000 China Northeastern University College of Information Science and Engineering Shenyang110819 China

ISBN: (纸本)9781665452151

While novel power systems are developing in the direction of electrification and cleaning, there are many unstable factors in system. To alleviate the influence of random factors of external excitation on the stability of novel power systems, virtual synchronous generator (VSG) can simulate the operation mechanism of synchronous generator and provide inertia and damping support for the system. In island mode, the parallel operation mode of multiple distributed generation is usually adopted to improve the capacity and reliability of microgrid, but the parallel system is prone to the problem that reactive power cannot be accurately distributed. The paper proposes a parallel VSG distributed control method based on consensus secondary control strategy, which can improve the distribution characteristics of active power and reactive power of the system. Integration link is introduced into the reactive power control loop of the microgrid inverter. The adjacent VSGs can reduce the voltage fluctuation at the point of common coupling and realize the on-demand distribution of reactive power by only a small amount of information interaction among multiple agents. The simulation model verifies the effectiveness of the proposed method. © 2023 IEEE.

关键词： Synchronous generators

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Methods of increasing data reliability based on distributed and parallel technologies based on blockchain

Methods of increasing data reliability based on distributed ...

引用

international conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Makhmadiyarovich, Nazarov Fayzullo Sherzodjon, Yarmatov Samarkand State University named after Sharof Rashidov Samarkand Uzbekistan

ISBN: (纸本)9781032669663

This article examines directions and mechanisms for increasing data reliability in computer networks. Currently, the rapid development of information technologies, the rapid growth of data flow, high-quality data processing carried out in network technologies, and the increase in the volume of data lead to an increase in the problem of data reliability. It is an urgent issue to find solutions based on the use of modern technologies to solve these problems. The simultaneous processing of various types of data in information systems, video, audio, text and digital data, creates big data. The variety of data types in bigdata creates the problem of quality data processing, which greatly affects the reliability of the data. Research shows that breaches of data integrity mainly manifest in three directions. In this case, there is a violation of the reliability of interrelated data in data transmission and storage, in the processing of large volumes of data and in the transcription of video data. It is created due to errors created during data transmission based on artificial and natural redundancy. To solve the mentioned problems, increasing data reliability based on blockchain mechanisms for payment systems in data transmission, increasing data reliability based on error minimization mechanisms in video information systems, and distributed computing and parallel mechanisms in large-scale information systems based on methods of increasing data reliability are researched. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Information systems

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SQMG: An Optimized Stochastic Quantization Method Using Multivariate Gaussians for distributed Learning

SQMG: An Optimized Stochastic Quantization Method Using Mult...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Zhang, Jianan Xie, Zaipeng Li, Hongxing Jie, Xuanyao Wang, Yunfei Li, Bowen Hohai Univ Key Lab Water Big Data Technol Minist Water Resources Nanjing Peoples R China Hohai Univ Dayu Coll Nanjing Peoples R China Hohai Univ Dept Comp Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9798350359329;9798350359312

distributed Learning is pivotal for training extensive deep neural networks across multiple nodes, leveraging parallel computation to hasten the learning process. However, it faces challenges in communication efficiency and resource utilization. Asynchronous Quantized Stochastic Gradient Descent (AQSGD) addresses communication bottlenecks by updating quantized model parameters, thereby expediting training and reducing bandwidth usage. Yet, current stochastic quantization methods may inadequately capture varied gradient distributions, leading to accumulated biases and amplified quantization errors. These issues are amplified as the number of distributed nodes grows. This study proposes a novel Stochastic Quantization with Multivariate Gaussians (SQMG) for distributed machine learning. SQMG employs a multivariate Gaussian model to represent the relationships in the gradient updates for quantization. The SQMG approach allows for constructing an optimized quantization target space, coupled with an iterative mapping scheme that effectively projects the parameters onto this space while minimizing quantization errors. Experiments on DNN and CNN models for MNIST and CIFAR-10 show that SQMG increases accuracy by 0.92% and 1.54% for DNN and CNN models, respectively, compared to conventional quantization methods. The results validate SQMG's ability to reduce quantization errors and improve model accuracy in distributed learning systems.

关键词： Asynchronous Stochastic Gradient Descent Stochastic quantization method Multivariate Gaussian model Quantization mapping Quantization error

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2025 14th international ITG conference on systems, Communications and Coding, SCC 2025

2025 14th International ITG Conference on Systems, Communica...

引用

14th international ITG conference on systems, Communications and Coding, SCC 2025

ISBN: (纸本)9798331522896

The proceedings contain 37 papers. The topics discussed include: enhanced practical integer-forcing source coding;exploiting transformers and attention mechanisms for modulation classification;symbol error probability constrained precoding with zero-crossing modulation for channels with 1-bit quantization and oversampling;iterative recovery algorithms for complex-valued distributed compressed sensing;relevance-based multi-user data compression for fronthaul rate reduction in cell-free massive MIMO systems;secret key rate of quantum key distribution assuming worst-case attacks;parallel decoding of trellis stages for low latency decoding of tail-biting convolutional codes;and on the design and performance of machine learning based error correcting decoders.

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SepJoin: A distributed Stream Join System with Low Latency and High Throughput 28

SepJoin: A Distributed Stream Join System with Low Latency a...

引用

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

作者： Wang, Qihang Zuo, Decheng Zhang, Zhan Chen, Siyuan Liu, Tianming Harbin Inst Technol Dept Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9781665473156

In the field of real-time analytics, stream joins are the basis for complex queries and greatly affect system performance. In order to satisfy the real-time requirements of streaming applications, the system imposes high requirements on the latency and throughput of the stream join operator. In this paper, we model the latency and throughput of distributed stream join systems based on queuing theory. Based on the analysis of this model, we demonstrate the impact of indexing-related overhead on the latency and throughput of stream join systems and propose a new distributed stream join system, SepJoin, which is oriented to the hash join problem. SepJoin reduces the number of tuples stored in each processing unit belonging to each input stream by designing a novel partitioning scheme that uses as many processing units as possible to store tuples belonging to each input stream, thereby reducing the index-related overhead of each processing unit when performing join operations and ultimately achieving performance benefits in terms of latency and throughput. We provide both theoretical analysis and extensive experimental evaluations to evaluate the processing latency and max throughput of SepJoin.

关键词： big data distributed stream join system queuing theory partitioning scheme

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Distributing Compilation to Enable High Throughput Scalable Quantum Workloads 5

Distributing Compilation to Enable High Throughput Scalable ...

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2024 international conference on Quantum Computing and Engineering

作者： Waring, Harry J. Oxford Quantum Circuits Reading Berks England

ISBN: (纸本)9798331541378

The increasing quality and availability of Quantum Processing Units (QPUs) is fueling a growing interest in quantum computing across many technological areas. The resulting increase in demand for QPU resources necessitates Quantum Computing as a Service (QCaaS) providers to support a high throughput of quantum workloads. A major runtime bottleneck in current QCaaS software stacks is the computationally-intensive compilation step which requires significant compute. To address this, Oxford Quantum Circuits has introduced distributed compilation whereby quantum programs are compiled in parallel and stored until the QPU is available. This has replaced our previous serial compilation approach where each program was compiled immediately prior to execution. From experiments using our production compilers and a simulated backend representing the QPU, we show that distributed compilation has resulted in a 78% reduction in processing time as compared to serial compilation. This demonstrates that there are sizeable performance gains to program throughput attainable through the introduction of distributed compilation into a QCaaS architecture. We posit that the usefulness of this feature will only grow given the increasing complexity of quantum programs and the growing popularity of quantum -classical hybrid algorithms.

关键词： distributed Compilation Quantum Computing Quantum Computing as a Service Quantum systems Software

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Query Compilation based distributed Morsel-driven parallel Spatial Query Processing 24

Query Compilation based Distributed Morsel-driven Parallel S...

引用

32nd ACM SIGSPATIAL international conference on Advances in Geographic Information systems, ACM SIGSPATIAL 2024

作者： Sahni, Rahul Zhang, Xiaozheng Chatterjee, Sudip Ray, Suprio University of New Brunswick Fredericton Canada

ISBN: (纸本)9798400711077

Driven by the need to support spatial data applications, most relational databases offer spatial SQL query features. However, traditional relational databases are not scalable, and their query processing follows a pull-based tuple-at-a-time model, which is not efficient for large data volumes. Although several specialized spatial data processing systems were developed by extending frameworks, such as Spark and Hadoop, these systems offer limited or no support for spatial SQL query execution. The few systems that support SQL querying, suffer from the overheads of the pull-based model. We present a compilation-based distributed spatial query processing system. It follows a data-centric push-based query compilation approach that involves generating distributed C++ (UPC++) based physical query plans from SQL queries, which are then compiled and executed on a distributed in-memory framework based on the Partitioned Global Address Space (PGAS) paradigm. We also introduce morsel-driven parallelism for scalable spatial query execution. Experimental evaluation of our system on a number of spatial query workloads demonstrate that it performs significantly better than a leading Spark-based spatial data system Apache Sedona. © 2024 Copyright held by the owner/author(s).

关键词： Structured Query Language

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FedSKG: Self-supervised Federated Learning with Structural Knowledge of Global Model 29

FedSKG: Self-supervised Federated Learning with Structural K...

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

作者： Li, Jun Luo, Dan Wei, Kang Mei, Zhen Shao, Yumeng Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (纸本)9798350330717

Federated self-supervised learning (FedSSL) is an emerging method in the domain of machine learning. It collaboratively learns a powerful feature extractor among multiple participants by utilizing distributed unlabeled data. However, conventional FedSSL suffers from statistical heterogeneity due to the non-independent and identically distributed (Non-IID) data among participants. In this work, we introduce a novel method to tackle the Non-IID data issue in FedSSL. First, the relation knowledge distillation is utilized to enhance the learning from global models. Then, we dynamically update the local model with divergence-aware update (DAU) method to preserve the client's knowledge of Non-IID data. Our experimental results demonstrate that the proposed approach outperforms other methods by up to 8% on linear evaluation, verifying the effectiveness of our approach. © 2023 IEEE.

关键词： Federated self-supervised learning knowledge distillation non-independent and identically distributed

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parallel-Driven Edge Computing Task Offloading for Profit Maximization Based on DDPG 29

Parallel-Driven Edge Computing Task Offloading for Profit Ma...

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

作者： Fu, Lequn Qiu, Houming Zhu, Kun Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350330717

Leveraging mobile edge computing (MEC) for task offloading is an effective strategy to address the computational limitations of mobile devices. However, current offloading strategies largely cater to user-centric objectives, neglecting the motives of service providers, unintentionally diminishing their profits. In this work, we propose a novel allocation strategy to improve resource utilization efficiency based on a parallel edge computing system. Specifically, we jointly consider incentives and cross-server resource allocation in parallel-driven MEC. This approach facilitates the distribution of user workloads across multiple edge servers, enhancing system performance and efficiency. In our approach, we leverage the deep deterministic policy gradient (DDPG) algorithm to support task offloading decisions, maximizing the overall profits of service providers. Simulation results show that our approach, compared to non-parallel edge systems, efficiently boosts resource utilization by an average of 13.37%. © 2023 IEEE.

关键词： Deep deterministic policy gradient(DDPG) Mobile edge computing (MEC) parallel computing Resource allocation

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