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Accelerating Neural BP-Based Decoder Using Coded distributed computing

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第9期73卷 13997-14002页

作者： Han, Xuesong Liu, Rui Li, Yong Yi, Chen He, Jiguang Wang, Ming Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Technol Innovat Inst Abu Dhabi 00000 U Arab Emirates North Carolina State Univ Dept Comp Sci Raleigh NC 27606 USA

While neural BP-based (NBP) decoders exhibit superior error correction performance compared to belief-propagation (BP) decoders, the NBP decoder's high computational and memory requirements impede its practical deployment in communication systems. To overcome this challenge, we propose a Coded Neural BP (CNBP) scheme to accelerate the NBP decoder in distributed environments, while considering storage constraints and providing resilience to stragglers. The key idea is to reformulate the primary operations of the NBP decoder as matrix-vector multiplications by introducing weight matrices and transformations. Based on this, the acceleration of the NBP decoder is achieved by speeding up matrix-vector multiplications using coded distributed computing. Extensive experiments conducted on Amazon EC2 cluster demonstrate that CNBP achieves notable acceleration and scalability performance without any loss in error correction performance.

关键词： BP-based decoder coding theory distributed computing neural network straggler resilience BP-based decoder coding theory distributed computing neural network straggler resilience

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Coded distributed computing With Pre-Set Data Placement and Output Functions Assignment

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IEEE TRANSACTIONS ON INFORMATION THEORY 2025年第3期71卷 2195-2217页

作者： Wang, Yuhan Wu, Youlong Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

Coded distributed computing can reduce the communication load for distributed computing systems by introducing redundant computation and creating multicasting opportunities. However, the existing schemes require delicate data placement and output function assignment, which is not feasible when distributed nodes fetch data without the orchestration of a central server. In this paper, we consider the general systems where the data placement and output function assignment are arbitrary but pre-set. We propose two coded computing schemes, One-shot Coded Transmission (OSCT) and Few-shot Coded Transmission (FSCT), to reduce the communication load. Both schemes first group the nodes into clusters and divide the transmission of each cluster into multiple rounds, and then design coded transmission in each round to maximize the multicast gain. The key difference between OSCT and FSCT is that the former uses a one-shot transmission where each encoded message can be decoded independently by the intended nodes, while the latter allows each node to jointly decode multiple received symbols to achieve potentially larger multicast gains. Furthermore, based on the lower bound proposed by Yu et al., we derive sufficient conditions for the optimality of OSCT and FSCT, respectively. This not only recovers the existing optimality results but also includes some cases where our schemes are optimal while others are not.

关键词： Power capacitors distributed databases Optimization Servers Multicast communication Encoding Resource management Multitasking Lower bound Linear programming distributed computing coding heterogeneous

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Preface for the special issue on selected software artifacts from DisCoTec 2023-the 18th International Federated Conference on distributed computing Techniques

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SCIENCE OF COMPUTER PROGRAMMING 2025年 243卷

作者： Casadei, Roberto Cogo, Vinicius Vielmo van Dijk, Tom Scalas, Alceste Alma Mater Studiorum Univ Bologna Bologna Italy Univ Lisbon Lisbon Portugal Univ Twente Twente Netherlands Tech Univ Denmark Lyngby Denmark

This special issue includes a selection of the artefacts presented at the 18th International Federated Conference on distributed computing Techniques (DiScoTec 2023), held at the NOVA University Lisbon (Lisbon, Portugal), in June 18-23, 2023. The federated conference included: COORDINATION 2023, the 25th International Conference on Coordination Models and Languages);DAIS 2023, the 23rd International Conference on distributed Applications and Interoperable Systems;and FORTE 2023, the 43rd International Conference on Formal Techniques for distributed Objects, Components, and Systems. All the three conferences welcomed submissions describing technological artefacts, including innovative prototypes supporting the modelling, development, analysis, simulation, or testing of systems in the broad spectrum of distributed computing subjects. The artefact evaluation chairs have selected a subset of high- quality accepted artefacts to be invited for submission to this special issue. Following the revision process, nine artefacts have been accepted to be part of this special issue. The published contributions include different types of artefacts, including programming libraries, frameworks, as well as tools for the analysis, verification, and simulation of distributed systems.

关键词： distributed computing distributed systems distributed applications Coordination Formal methods Programming languages Software Tools Simulators Analysis Model checkers Machine learning

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Asymptotically Optimal Coded distributed computing via Combinatorial Designs

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IEEE-ACM TRANSACTIONS ON NETWORKING 2024年第4期32卷 3018-3033页

作者： Cheng, Minquan Wu, Youlong Li, Xianxian Wu, Dianhua Guangxi Normal Univ Minist Educ Key Lab Educ Blockchain & Intelligent Technol Guilin 541004 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Miningand Sec Guilin 541004 Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Guangxi Normal Univ Sch Math & Stat Guilin 541006 Peoples R China

Coded distributed computing (CDC) introduced by Li et al. can greatly reduce the communication load for MapReduce computing systems. In the cascaded CDC with K workers, N input files and Q output functions, each input file will be mapped by r workers and each output function will be computed by s workers such that coding techniques can be applied to create multicast opportunities. The main drawback of most existing CDC schemes is that they require the original data to be split into a large number of input files that grows exponentially with K , which would significantly increase the coding complexity and degrade the system performance. In this paper, we first use a classical combinatorial structure t -design, for any integer t >= 2 >= 2 , to develop a low-complexity and communication-efficient CDC with r=s. Our scheme has much smaller N and Q than the existing schemes under the same parameters K , r , and s;and achieves smaller communication loads compared with the state-of-the-art schemes when K is relatively large. Remarkably, unlike the previous schemes that realize on large operation fields, our scheme operates in one-shot communication on the minimum binary field F-2 . With a derived lower bound on the communication load under one-shot linear delivery, we show that the t -design scheme is asymptotically optimal. Furthermore, we show that our construction method can incorporate the other combinatorial structures that have a similar property to t -design. For instance, we use t -GDD to obtain another one-shot asymptotically optimal CDC scheme over F-2 that has different parameters from t -design. Finally, we show that our construction method can also be used to construct CDC schemes with r not equal s that have small file number and output function number. Like what you're reading?

关键词： distributed computing Costs Task analysis Encoding Vectors Technological innovation Symbols Coded distributed computing asymptotically optimal t-design t-GDD

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Coded distributed computing Over Unreliable and Insecure D2D Networks

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IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第5期72卷 2563-2576页

作者： Chen, Jiajun Sung, Chi Wan City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

With the development of wireless computing devices, extending distributed computing to wireless networks deserves a closer look. This paper considers distributed computing over unreliable and insecure device-to-device (D2D) networks, in which each device is not always available to perform computation. The process of distributed devices exchanging calculated results with each other is vulnerable to eavesdropping in wireless environments. To handle the unreliable devices, we adopt repetition codes to build a novel system that supports general computations, called rho-replication system, where each device has rho -1 replicas with duplicate data. A coded computation scheme for the $\rho $ -replication system is proposed, which not only achieves the minimum communication load of the system but also ensures weak security of wireless transmissions during data exchange. Furthermore, the replication nature of the system can be exploited for beamforming transmissions, naturally leading to the idea of energy optimization. Simulation results show that increasing rho does not necessarily improve energy efficiency, as the benefit of increased beamforming gain may be outweighed by the drawback of heavier communication load.

关键词： distributed computing Wireless communication Array signal processing Task analysis Security Communication system security Encoding Coded distributed computing D2D networks computation replication weak security computation-communication tradeoff energy efficiency

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Coded computing for Half-Duplex Wireless distributed computing Systems via Interference Alignment

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2024年第11期23卷 17399-17414页

作者： Huang, Zhenhao Yuan, Kai Ma, Shuai Bi, Yue Wu, Youlong ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China LTCI Telecom Paris F-91120 Paris France

distributed computing frameworks such as MapReduce and Spark are often used to process large-scale data computing jobs. In wireless scenarios, exchanging data among distributed nodes would seriously suffer from the communication bottleneck due to limited communication resources such as bandwidth and power. To address this problem, we propose a coded parallel computing (CPC) scheme for distributed computing systems where distributed nodes exchange information over a half-duplex wireless interference network. The CPC scheme achieves the multicast gain by utilizing coded computing to multicast coded symbols intended to multiple receiver nodes and the cooperative transmission gain by allowing multiple transmitter nodes to jointly deliver messages via interference alignment. To measure communication performance, we apply the widely used latency-oriented metric: normalized delivery time (NDT). It is shown that CPC can significantly reduce the NDT by jointly exploiting the parallel transmission and coded multicasting opportunities. Surprisingly, when the number of computation nodes K tends to infinity and the computation load is fixed, CPC approaches zero NDT while all state-of-the-art schemes achieve positive values of NDT. Finally, we establish an information-theoretic lower bound for the NDT-computation load trade-off over the half-duplex network, and prove our scheme achieves the minimum NDT within a multiplicative gap of 3, i.e., our scheme is order optimal.

关键词： Wireless sensor networks MapReduce distributed computing interference alignment communication latency Wireless sensor networks MapReduce distributed computing interference alignment communication latency

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Experimental evaluation of a multi-installment scheduling strategy based on divisible load paradigm for SAR image reconstruction on a distributed computing infrastructure

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JOURNAL OF PARALLEL AND distributed computing 2024年 193卷

作者： Chinnappan, Gokul Madathupalyam Veeravall, Bharadwaj Mouthaan, Koen Lee, John Wen-Hao Natl Univ Singapore Dept Elect & Comp Engn 4 Engn Dr 3 Singapore 119077 Singapore DSO Natl Labs Sensors Div 12 Sci Pk Dr Singapore 118225 Singapore

Radar loads, especially Synthetic Aperture Radar (SAR) image reconstruction loads use a large volume of data collected from satellites to create a high-resolution image of the earth. To design near-real-time applications that utilise SAR data, speeding up the image reconstruction algorithm is imperative. This can be achieved by deploying a set of distributed computing infrastructures connected through a network. Scheduling such complex and large divisible loads on a distributed platform can be designed using the Divisible Load Theory (DLT) framework. We performed distributed SAR image reconstruction experiments using the SLURM library on a cloud virtual machine network using two scheduling strategies, namely the Multi-Installment Scheduling with Result Retrieval (MIS-RR) strategy and the traditional EQual-partitioning Strategy (EQS). The DLT model proposed in the MIS-RR strategy is incorporated to make the load divisible. Based on the experimental results and performance analysis carried out using different pixel lengths, pulse set sizes, and the number of virtual machines, we observe that the time performance of MIS-RR is much superior to that of EQS. Hence the MIS- RR strategy is of practical significance in reducing the overall processing time, and cost, and in improving the utilisation of the compute infrastructure. Furthermore, we note that the DLT-based theoretical analysis of MIS-RR coincides well with the experimental data, demonstrating the relevance of DLT in the real world.

关键词： Synthetic Aperture Radar distributed computing Heterogeneous cluster Divisible Load Theory SLURM Multi-Installment Scheduling MIS-RR strategy EQual-partitioning Strategy Makespan

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Coded distributed computing for Resilient, Secure and Private Matrix-Vector Multiplication in Edge-Enabled Metaverse

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2024年第5期10卷 1944-1958页

作者： Qiu, Houming Zhu, Kun Luong, Nguyen Cong Niyato, Dusit Wu, Qiang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Collaborat Innovat Ctr Novel Software Technol & In Nanjing 210016 Peoples R China PHENIKAA Univ Fac Comp Sci Hanoi 12116 Vietnam Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

Metaverse is an immersive and photorealistic shared virtual world that requires efficient rendering and processing of millions of virtual objects and scenes. This leads to the requirements of computing time-sensitive and computation-intensive tasks, primarily focused on matrix multiplication. Cloud computing can be leveraged to process computation-intensive tasks. However, it is not able to meet the ultra-low latency requirements of immersive experiences due to the remote servers. In this paper, we propose an effectively distributed edge computing framework to compute high-dimensional matrix multiplication for the Metaverse. With the distributed edge computing, the high-dimensional matrix multiplication task is divided into multiple smaller subtasks, which are then assigned to nearby edge servers (workers). However, leveraging distributed edge servers raises emerging issues due to the existence of stragglers, malicious, and colluding servers, which limits the applications of distributed edge computing in the Metaverse system. Thus, we design a resilient, secure, and private coded distributed computing (RSPCDC) scheme to jointly address the aforementioned issues. Firstly, the RSPCDC scheme reduces overall computation latency by lowering the recovery threshold. Secondly, to identify malicious (e.g., Byzantine) workers, a verification approach is embedded in the scheme to promptly detect the Byzantine attack without requiring additional workers. Thirdly, the RSPCDC scheme provides (information-theoretic) privacy protection for the input data against the collusion of workers. Fourthly, the results of subtasks computed by stragglers are fully utilized to enhance the computation performance during the recovery of the final result. In addition, the RSPCDC scheme is designed and deployed in practical scenarios in which the computing resources of the workers are heterogeneous. Extensive performance evaluations are provided to demonstrate the improvement and effectiveness of

关键词： Servers Metaverse Task analysis Codes Edge computing distributed computing Polynomials coded distributed computing edge computing stragglers security privacy matrix-vector recovery threshold

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Real-time performance evaluation and optimization of electrical substation equipment inspection algorithm based on distributed computing

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2024年第19期19卷 1878-1887页

作者： Zhang, Guoliang Du, Zexu Zhang, Yi Chen, Jiangqi Zhang, Xi State Grid Smart Grid Res Inst Co Ltd State Grid Lab Grid Adv Comp & Applicat Beijing 102209 Peoples R China

Substation equipment inspection faces problems such as large amounts of data, various information types, complex analysis and calculations, and low resource integration. distributed computing system uses networks to realize computer connectivity and collaboration, achieving in-depth sharing of computing resources, software resources, information resources, and communication resources. Therefore, firstly, this article introduces segmentation method for load balancing and recommendation calculation based on path search;secondly, an algorithm model for substation equipment inspection based on distributed computing is constructed;then, the article analyzes and discusses multi-core cluster task allocation, heterogeneous task scheduling framework, fragmented transmission mechanism, and vertical expansion mechanism;finally, case analysis and performance evaluation are carried out. distributed computing has significant advantages in the inspection algorithm of power substation equipment. It can efficiently process large-scale data, improve computing power, ensure data redundancy and fault tolerance, provide good scalability, optimize resource utilization, and support remote data processing. By integrating multi-source data, distributed computing improves the accuracy and comprehensiveness of the algorithm, enhances security, supports complex algorithms such as machine learning and deep learning, and realizes real-time monitoring and early warning. The results show that the proposed algorithm can improve the efficiency of distributed parallel computing and analysis of substation equipment inspections and can simulate, evaluate, and screen substation equipment failure scenarios that may cause serious consequences within an effective time.

关键词： substation equipment inspection distributed computing performance evaluation algorithm optimization

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Efficient and scalable crop growth simulations using standard big data and distributed computing technologies

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 236卷

作者： Knapen, Rob de Wit, Allard Buyukkaya, Eliya Petrou, Petros Paudel, Dilli Janssen, Sander Athanasiadis, Ioannis Wageningen Univ & Res Wageningen Environm Res POB 47 NL-6700 AA Wageningen Netherlands Wageningen Univ & Res Artificial Intelligence Grp Wageningen Netherlands UBITECH Thessalias 8 & Etolias Chalandri 15231 Greece

The digitization in agriculture has led to an explosion of highly detailed data generated, offering opportunities for further optimizing resource use in food production systems. However, managing and processing these growing data volumes presents significant challenges. This study investigates the suitability of standard big data and distributed computing technologies with a crop yield forecasting case study, and benchmarks performance and scalability of storage and compute. To that end a prototype system leveraging the Apache Spark big data analytics framework and using the WISS-WOFOST crop growth simulation model is assembled and evaluated for its efficiency and scalability when running large numbers of simulations using distributed computing on commonly available infrastructure. Existing data for maize and winter wheat, as typical summer and winter crops, is prepared for distributed storage and processing and used to measure the performance of the system on clusters of increasing sizes, from small Kubernetes Cloud deployments to large HPC configurations. Specific attention is paid to the aggregation of the grid-based simulation results to larger administrative regions for follow-up analysis and reporting. Our results demonstrate that the selected standard big data and distributed computing technology simplifies the application of distributed processing and storage, making the related trade-off between runtime and costs more attainable. By increasing the distribution of our system 64 times and the total number of cores used 45 times compared to the baseline, we obtained a 99% reduction in simulation processing time and a 95% decrease in the aggregation time of the simulation results, making detailed forecasting for large areas more tractable. However, distributed implementations remain inherently more complex than conventional ones. As such, the construction and use of distributed systems will continue to be a challenge for agricultural agronomists and agricultura

关键词： distributed computing Apache Spark Crop yield forecasting WOFOST crop growth model Benchmarking Kubernetes HPC

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