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Coverage Probability of distributed CoMP UAV-Assisted Cellular Networks

Coverage Probability of Distributed CoMP UAV-Assisted Cellul...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yan Li Qingmin Long Qiang Tang Shiming He Bing Xiong School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information System Engineering Laboratory National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

It is well-established that terrestrial communication systems may fail during emergencies such as earthquakes, tsunamis, and floods. Fortunately, with the rapid advancement of unmanned aerial vehicle (UAV) network technology, deploying UAV nodes as aerial base stations (BSs) is assuming an increasingly crucial role in facilitating downlink transmissions and restoring ground communication capabilities. However, a single UAV node is not sufficient to meet the requirements. Inspired by distributed communication, we introduce a performance analysis framework based on stochastic geometry to analyze the distributed coordinated multi-point (CoMP) UAV-assisted communication network. Specifically, we assume that all UAV nodes follow a homogeneous Poisson point process (PPP) and maintain a constant altitude. The entire space is tessellated by multiple hexagons, with multiple UAV nodes within each hexagonal region working together to serve terrestrial user equipments (UEs). For this region-centric cooperative model, we derive an exact expression for the coverage probability to quantify the performance improvement enabled by UAVs, analyze the upper bound of the coverage probability, and provide a simplified approximation. We then compare this model to a user-centric model. Our numerical findings demonstrate that the cooperation of UAV nodes can significantly enhance the coverage probability and save spectrum resources.

关键词： Analytical models Upper bound Spectral efficiency Earthquakes Stochastic processes Probability Autonomous aerial vehicles Tsunami Terrestrial communications Numerical models

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A parallel Partial Merge Repair Algorithm for Multi-block Failures for Erasure Storage systems

A Parallel Partial Merge Repair Algorithm for Multi-block Fa...

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Shuaipeng Zhang Shiyi Li Chentao Wu Ruobin Wu Saiqin Long Wen Xia Harbin Institute of Technology Shenzhen Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Wuhan National Laboratory for Optoelectronics Shanghai Jiao Tong University Jinan University

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

In order to achieve high availability and low storage costs in distributed storage systems, erasure code is widely used instead of replication. Compared to replication, erasure code can reduce storage costs, but also brings higher repair costs. There are currently many repair algorithms to reduce the block reconstruction time of single block failure. However, applying the existing methods to multi-block failures may lead to unbalanced network traffic, unnecessary network transfers, and network congestion at data collection node during the repair process, which can not make full use of the bandwidth between *** solve this problem, we propose a novel repair algorithm called Partial Merge Repair (PMR) for multi-block failures, which is a scheduling algorithm that considers network load between nodes and combines multiple failed blocks to recover together. It first divides all surviving nodes into different groups, and then the data collection nodes within the group collect the data needed to repair multiple blocks through cross merging. Finally, the data collection node sends the collected blocks to the repair node to complete the repair. Our study presents a formal definition and proof of network transfer time in the modeled repair process of PMR, highlighting its superior efficiency compared to existing methods in homogeneous *** implement a prototype of PMR to evaluate its performance. The experimental results indicate that compared to existing repair technologies, PMR improves repair throughput by 28%-256% for various scenes.

关键词： Costs Codes Scheduling algorithms Bandwidth Telecommunication traffic Maintenance engineering Data collection

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Autonomous Mobile Robots: Refining the Computational Landscape

Autonomous Mobile Robots: Refining the Computational Landsca...

引用

35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Buchin, Kevin Flocchini, Paola Kostitsyna, Irina Peters, Tom Santoro, Nicola Wada, Koichi TU Eindhoven Dept Math & Comp Sci Eindhoven Netherlands Univ Ottawa EECS Ottawa ON Canada Carleton Univ Sch Comp Sci Ottawa ON Canada Hosei Univ Fac Sci & Engn Tokyo Japan

ISBN: (纸本)9781665435772

Within distributed computing, the study of distributed systems of identical mobile computational entities, called robots, operating in a Euclidean space is rather extensive. When a robot is activated, it executes a Look-Compute-Move cycle: it takes a snapshot of the environment (Look);with this input, it computes its destination (Compute);and then it moves towards that destination (Move). The choice of the times a robot is activated and how long its cycle lasts is made by a fair (but adversarial) scheduler;three schedulers are usually considered: fully synchronous (FSYNC), semi-synchronous (SSYNC), and asynchronous (ASYNC). Extensive investigations have been carried out, under all those schedulers, within four models, corresponding to different levels of computational and communication powers of the robots: OBLOT (the weakest), LUMI (the strongest), and two intermediate models FSTA and FCOM. The many results for specific problems have provided insights on the relationships between the models and with respect to the activation schedulers. Recently, a comprehensive characterization of these relationships has been provided with respect to the FSYNC and SSYNC schedulers;however, in several cases, the results were obtained under some restrictive assumptions (chirality and/or rigidity). In this paper, we improve the characterization by removing those assumptions, providing a refined map of the computational landscape for those robots. We also establish some preliminary results with respect to the ASYNC scheduler.

关键词： mobile robots oblivious persistent memory finite communication finite-state distributed computing

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FPGA Based Database Sort-Aggregation Query Acceleration Architecture

FPGA Based Database Sort-Aggregation Query Acceleration Arch...

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Computer and Information Processing Technology (ISCIPT), international symposium on

作者： Haijun Xue Shuai Wang Xinxin Zhao Hong Hao Xiongru Wang Kai Jiang Shandong Inspur Academy of Science and Technology Inspur Group Co. Ltd Jinan China

ISBN: (数字)9798350388404

ISBN: (纸本)9798350388411

In AI era, the scale of data involved in relational databases has shown an exponential growth trend, which leads to a sharp rise in the CPU load of database management systems and a significant increase in SQL query latency. Improving the response efficiency of databases to SQL queries to massive storage resources through heterogeneous hardware optimization design has become a research hotspot in related fields. This work proposes an FPGA based database sort-aggregation acceleration architecture that meets the requirements of contemporary high-performance database applications. The design introduces heterogeneous FPGAs to offload high concurrency large-scale SQL sort-aggregation query tasks that would need to be executed by CPU. The acceleration kernels included in the architecture is based on practical relational database SQL queries application, and an iterative merge-sort kernel and corresponding aggregation query kernel design are implemented through FPGA logic. In addition, the architecture also supports multi-threaded concurrent processing to maximize the FPGA's excellent parallel capabilities and improve the efficiency of SQL queries. Experiments on the open-source database KaiwuDB show that the FPGA based sort-aggregation acceleration architecture can reduce the average time cost by 88.26% compared with the database CPU independent query scheme.

关键词： Concurrent computing databases Computer architecture Relational databases Hardware Logic Iterative methods

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AI-Blueprint: A Real-Time System for Automated Identification and Analysis of Electrical Blueprints

AI-Blueprint: A Real-Time System for Automated Identificatio...

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international Conference on parallel and distributed systems (ICPADS)

作者： Chao Gu Ye Tian Jiahui Hou Xiang-Yang Li LINKE Lab School of Computer Science and Technology University of Science and Technology of China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In recent years, technologies such as Artificial Intelligence have been of great help in driving smart production in industry. However, some links with high complexity and flexibility still rely on manual labor. This paper focuses on the design process of electrical equipment. Based on the realistic needs of enterprises, we design a real-time system for automated recognition and analysis of blueprints of electrical equipment, AI-Blueprint. It automatically extracts multi-modal information from the user’s blueprints, comprehends it, and quickly matches it with the best-standardized schemes in the enterprise database. On average, each blueprint is processed in as little as ${1 . 2 3}$ seconds, which significantly reduces labor costs. We carefully designed several specific modules to build our system, including the Document Element Detector, Diagram Extractor, Table Extractor, and Feature Extractor. To achieve good performance and fast response of AI-Blueprint, we proposed some innovative algorithms and tested their impact on the system performance under different parameters. Numerous experiments have shown that AI-Blueprint can accurately understand information such as circuit diagrams and tables in electrical equipment blueprints, and recognize and classify them precisely and quickly.

关键词： Industries Costs databases System performance Production Manuals Detectors Feature extraction Real-time systems Data mining

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Secure Storage Using Maximally Recoverable Locally Repairable Codes

Secure Storage Using Maximally Recoverable Locally Repairabl...

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IEEE international symposium on Information Theory

作者： Tim Janz Hedongliang Liu Rawad Bitar Frank R. Kschischang Institute of Telecommunications University of Stuttgart Germany Institute of Communications Engineering Technical University of Munich Germany Edward S. Rogers Sr. Department of Electrical & Computer Engineering University of Toronto Canada

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

This paper considers data secrecy in distributed storage systems (DSSs) using maximally recoverable locally repairable codes (MR-LRCs). Conventional MR-LRCs are in general not secure against eavesdroppers who can observe the transmitted data during a global repair operation. This work enables nonzero secrecy dimension of DSSs encoded by MR-LRCs through a new repair framework. The key idea is to associate each local group with a central processing unit (CPU), which aggregates and transmits the contribution from the intact nodes of their group to the CPU of a group needing a global repair. The aggregation is enabled by so-called local polynomials that can be generated independently in each group. Two different schemes - direct repair and forwarded repair - are considered, and their secrecy dimension using MR-LRCs is derived. Positive secrecy dimension is enabled for several parameter regimes.

关键词： Codes Aggregates distributed databases Spread spectrum communication Maintenance engineering Polynomials Central Processing Unit

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Machine Learning for CUDA+MPI Design Rules 36

Machine Learning for CUDA+MPI Design Rules

引用

36th IEEE international parallel and distributed Processing symposium Workshops, IPDPSW 2022

作者： Pearson, Carl Javeed, Aurya Devine, Karen Sandia National Laboratories AlbuquerqueNM United States

ISBN: (纸本)9781665497473

We present a new strategy for automatically exploring the design space of key CUDA + MPI programs and providing design rules that discriminate slow from fast implementations. In such programs, the order of operations (e.g., G PU kernels, MPI communication) and assignment of operations to resources (e.g., G PU streams) makes the space of possible designs enormous. systems experts have the task of redesigning and reoptimizing these programs to effectively utilize each new platform. This work provides a prototype tool to reduce that burden. In our approach, a directed acyclic graph of CUDA and MPI operations defines the design space for the program. Monte-Carlo tree search discovers regions of the design space that have large impact on the program's performance. A sequence-to-vector transformation defines features for each explored im-plementation, and each implementation is assigned a class label according to its relative performance. A decision tree is trained on the features and labels to produce design rules for each class;these rules can be used by systems experts to guide their implementations. We demonstrate our strategy using a key kernel from scientific computing - sparse-matrix vector multiplication - on a platform with multiple MPI ranks and GPU streams. © 2022 IEEE.

关键词： parallel programming

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Heterogeneous Data Processing in All-Digital Phased Array Radar

Heterogeneous Data Processing in All-Digital Phased Array Ra...

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IEEE international symposium on Phased Array systems and Technology

作者： Skyler Garner Mark Yeary Advanced Radar Research Center University of Oklahoma Norman OK United States

ISBN: (数字)9798350392142

ISBN: (纸本)9798350392159

All-digital phased array radars are an emerging technology that require new data processing techniques which are scalable, rely on open-architecture software components, and do not create bottlenecks for the real-time operation. Achieving a solution for these issues relies on heterogeneous processing and distributed software development on CPUs and FPGAs, whereby the overarching algorithm processing chain is distributed in a way that begins at the ADC of each element on an array and concludes in actionable data products. An important aspect is not only the speed of the algorithms, but the throughput of the packetized data transfer. These packets are not only routed, but they are also acted upon in a network of computing services. Here, we explore an open-source resource manager, known as Kubernetes, which connects the packetized data with computing resources on the servers, i.e., moving data in and out of server RAM to complete algorithms such as creating range-Doppler maps, as our results section shows.

关键词： Phased arrays Array signal processing Software algorithms Signal processing algorithms Radar parallel processing Throughput Arrays Servers Software development management

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Application-Attuned Memory Management for Containerized HPC Workflows

Application-Attuned Memory Management for Containerized HPC ...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Moiz Arif Avinash Maurya M. Mustafa Rafique Dimitrios S. Nikolopoulos Ali R. Butt Rochester Institute of Technology Virginia Tech

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

High-Performance Computing (HPC) jobs consist of data and memory-intensive tasks often executed as workflows or ensembles to facilitate efficient and coordinated execution. These workflows are traditionally executed on HPC systems and have unique memory requirements based on the data size, computational complexity, and I/O activity. Recently containerized execution of these workflows has been extensively explored. Containerized workflow execution of HPC jobs requires several terabytes of memory that exceed node capacity, resulting in excessive data swapping to slower storage, degraded job performance, and failures. Similarly, colocated bandwidth-intensive, latency-sensitive, or short-lived workflows suffer from degraded performance due to contention, memory exhaustion, and higher access latency due to suboptimal memory allocation. Recently, tiered memory systems comprising persistent memory and compute express link (CXL) have been explored to provide additional memory capacity and bandwidth to memory-constrained systems and applications. However, current memory allocation and management techniques for tiered memory subsystems are inadequate to meet the diverse needs of colocated containerized jobs in HPC systems that concurrently run workflows and ensembles at scale. This paper leverages tiered memory systems for containerized HPC workflows and proposes efficient memory management policies including intelligent page placement and eviction policies to improve memory access performance. Our page allocation and replacement policies incorporate task characteristics and enable efficient memory sharing between workflows. We integrate our policies with the popular HPC scheduler, SLURM, and container runtime, Singularity, to show that our approach improves tiered memory utilization and application performance and reduces workflow execution times by up to 51%, 87%, and 35% as compared to the ideal, realistic, and optimized tiered execution environments, respectively.

关键词： Concurrent computing distributed processing Runtime Scalability High performance computing Memory management Bandwidth

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AIFed: Anomaly-resilient and Imbalance-aware Federated Learning

AIFed: Anomaly-resilient and Imbalance-aware Federated Learn...

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international symposium on System and Software Reliability (ISSSR)

作者： Yi Su Ligang He Department of Computer Science University of Warwick Coventry United Kingdom

ISBN: (数字)9798350362930

ISBN: (纸本)9798350362947

In geographically distributed systems, time series data often exhibit non-identical distributions (non-IID) across nodes due to diverse features and sample imbalances, posing significant challenges for centralized AI model training. The conventional federated learning paradigm, while mitigating data privacy and communication cost concerns, does not adequately address the imbalance in sample importance nor the presence of anomalous data in model training. This work introduces AIFed, an advanced federated learning framework designed to be both anomaly-resilient and imbalance-aware. AIFed judiciously assigns weights to client models based on data segment features, accounting for sample importance and anomaly levels. By innovatively incorporating local data characteristics into a global training process, AIFed significantly enhances model robustness and effectiveness in real-time learning scenarios. We demonstrate AIFed’s efficacy through extensive experiments that show improved performance over traditional federated learning approaches.

关键词： Training Measurement Federated learning Time series analysis distributed databases Data models Robustness

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