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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

共 24647 条记录，以下是411-420 订阅

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Research and Development of Algorithms for Dispatching Tasks in distributed computing Systems

Research and Development of Algorithms for Dispatching Tasks...

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2024 international conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024

作者： Thant Sin, Si Thu Portnov, E.M. Bain, A.M. Moscow Russia

ISBN: (纸本)9798350395013

This article delves into heterogeneous computing systems, which employ multi-core processors (CPUs) and graphics processing units (GPUs) concurrently, facilitating efficient handling of resource-intensive tasks demanding substantial computing power. Heterogeneous systems primarily serve to judiciously allocate resources among users and computational processes. The veracity and dependability of the work's outcomes hinge upon the accurate utilization of mathematical tools, findings from experimental studies on heterogeneous SoCs, and practical validation. These systems represent a paradigm shift in computing, leveraging the strengths of both CPUs and GPUs to tackle diverse workloads effectively. By harnessing the parallel processing capabilities of GPUs alongside the general-purpose computing prowess of CPUs, heterogeneous systems optimize performance and throughput, catering to the burgeoning demands of modern computing applications. Through meticulous resource allocation and rigorous validation methodologies, these systems ensure the delivery of reliable and consistent results across a spectrum of tasks. The symbiotic integration of diverse processing units within heterogeneous architectures heralds a new era in computing, empowering users with unparalleled computational capabilities for addressing an array of challenges across various domains. © 2024 IEEE.

关键词： Dynamic programming

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PE-FedAvg: A Privacy-Enhanced Federated Learning for distributed Android Malware Detection 21

PE-FedAvg: A Privacy-Enhanced Federated Learning for Distrib...

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： Tang, Junwei Xu, Zilong Ye, Luyao Peng, Tao He, Ruhan Hu, Xinrong Tian, Wenlong Wuhan Textile University Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion China Wuhan Textile University School of Computer Science and Artificial Intelligence China University of South China School of Computer Science China Nanyang Technological University School of Physical and Mathematical Sciences Singapore

ISBN: (纸本)9798350329223

Android malware detection has become research hotspot in mobile security. When security service providers obtain feature information from target samples, they may involve user privacy information such as identity and behavioral habits, creating an urgent need for a distributed machine learning model to carry out malicious application detection with privacy protection. However, the information exchange between clients and servers in basic federated learning still poses risks of model data leakage, and the likelihood of inferring privacy information from the original feature data is high. Therefore, This paper propose a privacy-enhanced federated learning system called PE-FedAvg for distributed Android malware detection. Firstly, Gaussian noise is added to the feature data uploaded from the client to the server, satisfying differential privacy characteristics while retaining data distribution for privacy protection. Secondly, an improved homomorphic encryption algorithm based on modular repeated squaring is used to encrypt and transmit the gradient weight information exchanged between federated learning clients and servers, preventing model-targeted attacks. By conducting the experiments on the Drebin and Malgenome datasets, our design has demonstrated exceptional performance compared to the state-of-the-art work, achieving accuracy rates of 98.61% and 99.3%, respectively. This achievement highlights that our approach not only enhances the security of the federated learning system, but also more effectively accomplishes the detection of Android malware. © 2023 IEEE.

关键词： Android malware

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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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Assessing the Performance of Docker in Docker Containers for Microservice-based Architectures 32

Assessing the Performance of Docker in Docker Containers for...

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32nd Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Bedinotto Fava, Felipe Laviola Leite, Luiz Felipe Alves da Silva, Luis Fernando da Silva Amalfi Costa, Pedro Ramires Diniz Nogueira, Angelo Gaspar Gobus Lopes, Amanda Fagundes Schepke, Claudio Luis Kreutz, Diego Brandao Mansilha, Rodrigo Fed Univ Pampa UNIPAMPA Lab Adv Studies Computat LEA Grad Software Engn PPGES Alegrete Brazil

ISBN: (纸本)9798350363074;9798350363081

We provide a comprehensive and updated assessment of Docker versus Docker in Docker (DinD), evaluating its impact on CPU, memory, disk, and network. Using different workloads, we evaluate DinD's performance across distinct hardware platforms and GNU/Linux distributions on cloud Infrastructure as a Service (IaaS) platforms like Google Compute Engine (GCE) and traditional server-based environments. We developed an automated tools suite to achieve our goal. We execute four well-known benchmarks on Docker and its nested-container variant. Our findings indicate that nested-containers require up to 7 seconds for startup, while the Docker standard containers require less than 0.5 seconds for Debian and Alpine operating systems. Our results suggest that Docker containers based on Debian consistently outperform their Alpine counterparts, showing lower CPU latency. A key distinction among these Docker images lies in the varying number of installed libraries (e.g., stretching from 13 to 119) across different Linux distributions for the same system (e.g., MySQL). Furthermore, the number of events and CPU latency indicates that the influence of DinD over Docker proves that it is insignificant for both operating systems. In terms of memory, running containers of Debian-based images consume 20% more size of memory than those based on Alpine. No significant differences are between nested-containers and Dockers for disk and network IO. It is worth emphasizing that some of the disparities, such as a bigger memory footprint, appear to be a direct result of the software stack in use, including different kernel versions, libraries, and other essential packages.

关键词： Docker in Docker DinD Docker Containers Nested-container Performance Evaluation Benchmarks Cloud computing

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Comparative Analysis of Sequential and parallel computing for Object Detection Using Deep Learning Model 24

Comparative Analysis of Sequential and Parallel Computing fo...

引用

24th international Arab conference on Information Technology, ACIT 2023

作者： Amsaad, Fathi Al-Eidi, Shorouq Darwish, Omar Computer Science and Engineering Wright State University Ohio United States Tafila Technical University Computer Science Department Tafila Jordan Information Security and Applied Computing Eastern Michigan University Michigan United States

ISBN: (纸本)9798350384307

Recently, parallel and distributed computing has become indispensable in various application domains, such as cloud computing and extensive data analysis, to meet the growing demands for efficiency and reliability. In this study, we compared sequential and parallel computing techniques applied to a deep learning model for object detection. By leveraging multiple evaluation metrics, such as F1 score, execution time, and accuracy, we meticulously evaluated the model's performance. Our analysis revealed that parallel execution considerably decreased the training time of model, resulting in an average time savings of 40 % while maintaining the training process's accuracy and quality. These finding have significant implications for real world applications, as they demonstrate tremendously the potential for expedited deep learning model training without compromising accuracy. This can tremendously benefit medical imaging and realtime video analysis, where accuracy and efficiency are paramount. © 2023 IEEE.

关键词： Deep neural networks parallel computing Sequential computing

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2nd international conference on Next-Generation computing and Information Systems, ICNGCIS 2023

2nd International Conference on Next-Generation Computing an...

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2nd international conference on Next-Generation computing and Information Systems, ICNGCIS 2023

ISBN: (纸本)9781032738659

The proceedings contain papers. The special focus in this conference is on . The topics include: Impact of artificial intelligence on high-penetration renewable physical infrastructure;collaborative attacks and defense;building a safe and secure metaverse;distributed-computing based versatile healthcare services framework for diagnostic markers;supervised context-aware Latent Dirichlet allocation-based drug recommendation model;combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment;Time series analysis of vegetation change using remote sensing, GIS and FB prophet;advances in smart farming for precision agriculture: Green-IoT and machine learning as a solution;Towards a serverless computing and edge-intelligence architecture for the Personal-Internet-of-Things (PIoT);IOT and developed deep learning based road accident detection system and societal knowledge management;Healthcare CAD model for hierarchical processing of surgical navigation system;applicability of eye tracking technology in virtual keyboard for human-computer interactions;a digital twin framework for smart contract-based DeFi applications in the metaverse: Towards interoperability, service scaleup & resilience;a novel approach to glass identification using ensemble learning for forensics;deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification;Multi-class instance segmentation for the detection of cervical cancer cells using modified mask RCNN;dilated convolution model for lightweight neural network;artificial intelligence based Monte Carlo model for epidemic forecasting for societal aspect;efficient data visualization through novel recurrent neural network-based dimension reduction;An optimized CNN-based model for pneumonia detection;EEG-based emotion recognition: Leveraging CNNs for precision;A seven layer DNN approach for social-media multilevel image-based text classification.

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Atlas: Hierarchical Partitioning for Quantum Circuit Simulation on GPUs 24

Atlas: Hierarchical Partitioning for Quantum Circuit Simulat...

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2024 international conference for High Performance computing, Networking, Storage and Analysis

作者： Xu, Mingkuan Cao, Shiyi Miao, Xupeng Acar, Umut A. Jia, Zhihao Carnegie Mellon Univ Comp Sci Dept Pittsburgh PA 15213 USA Univ Calif Berkeley Dept EECS Berkeley CA USA

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

This paper presents techniques for theoretically and practically efficient and scalable Schrodinger-style quantum circuit simulation. Our approach partitions a quantum circuit into a hierarchy of subcircuits and simulates the subcircuits on multi-node GPUs, exploiting available data parallelism while minimizing communication costs. To minimize communication costs, we formulate an Integer Linear Program that rewards simulation of "nearby" gates on "nearby" GPUs. To maximize throughput, we use a dynamic programming algorithm to compute the subcircuit simulated by each kernel at a GPU. We realize these techniques in Atlas, a distributed, multi-GPU quantum circuit simulator. Our evaluation on a variety of quantum circuits shows that Atlas outperforms state-of-the-art GPU-based simulators by more than 2x on average and is able to run larger circuits via offloading to DRAM, outperforming other large-circuit simulators by two orders of magnitude.

关键词： parallel programming quantum simulation

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PTQ-SO: A Scale Optimization-based Approach for Post-training Quantization of Edge computing 27

PTQ-SO: A Scale Optimization-based Approach for Post-trainin...

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27th international conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Liu, Kangkang Chen, Ningjiang Guangxi Univ Coll Comp & Elect Informat Nanning Peoples R China Guangxi Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Parallel Distributed & Intelligent Comp Nanning Peoples R China

ISBN: (纸本)9798350349184;9798350349191

With the increasing performance of deep convolutional neural networks, they have been widely used in many computer vision tasks. However, a huge convolutional neural network model requires a lot of memory and computing resources, which makes it difficult to meet the requirements of low latency and reliability of edge computing when the model is deployed locally on resource-limited devices in edge environments. Quantization is a kind of model compression technology, which can effectively reduce model size, calculation cost and inference delay, but the quantization noise will cause the accuracy of the quantization model to decrease. Aiming at the problem of precision loss caused by model quantization, this paper proposes a post-training quantization method based on scale optimization. By reducing the influence of redundant parameters in the model on the quantization parameters in the process of model quantization, the scale factor optimization is realized to reduce the quantization error and thus improve the accuracy of the quantized model, reduce the inference delay and improve the reliability of edge applications. The experimental results show that under different quantization strategies and different quantization bit widths, the proposed method can improve the accuracy of the quantized model, and the absolute accuracy of the optimal quantization model is improved by 1.36%. The improvement effect is obvious, which is conducive to the application of deep neural network in edge environment.

关键词： deep neural networks edge computing scale optimization post-training quantization edge application

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A Hybrid Epoch Scheduling Scheme of Mixed Workloads with Different Types of Jobs on distributed Processors 20

A Hybrid Epoch Scheduling Scheme of Mixed Workloads with Dif...

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20th ACS/IEEE international conference on Computer Systems and Applications (AICCSA)

作者： Karatza, Helen D. Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

ISBN: (纸本)9798350319439

With the advances in the large-scale computing platforms, jobs that run on such systems are becoming complex and they present increased variability. In these systems, the workload usually has complex structure including jobs with different characteristics. Along with regular jobs, there are jobs represented as linear workflows (LWs) or even jobs which are bags of LWs. Consequently, efficient scheduling algorithms should be employed to ensure satisfactory performance as well as fair processing of jobs. This research aims to study appropriate scheduling algorithms of mixed workloads consisting of simple jobs including one task only, along with bags-of-LWs (BoLWs). Different techniques for scheduling are studied in a set of distributed resources. Extensive simulation experiments are carried out to investigate their effectiveness for various system utilization levels and variability in job/task service demands. The results demonstrate that the efficacy of the scheduling schemes depends on the variability level in job/task service demands, the system load, and the level of fairness that is considered as satisfactory.

关键词： distributed systems scheduling performance fairness

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Stochastic Large-scale Machine Learning Algorithms with distributed Features and Observations

Stochastic Large-scale Machine Learning Algorithms with Dist...

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2024 IEEE international conference on Big Data, BigData 2024

作者： Fang, Biyi Klabjan, Diego Vo, Truong Northwestern University Dept. of Engineering and Applied Mathematics EvanstonIL60208 United States Northwestern University Dept. of Industrial Engineering and Management Sciences EvanstonIL60208 United States Northwestern University Dept. of Machine Learning and Data Science EvanstonIL60208 United States

ISBN: (纸本)9798350362480

As the size of modern datasets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garnered ample attention, in particular when either observations or features are distributed, but not both. We propose a general stochastic algorithm where observations, features, and gradient components can be sampled in a double distributed setting, i.e., with both features and observations distributed. Very technical analyses establish convergence properties of the algorithm under different conditions on the learning rate (diminishing to zero or constant). Computational experiments in Spark demonstrate a superior performance of our algorithm versus a benchmark in early iterations of the algorithm, which is due to the stochastic components of the algorithm. © 2024 IEEE.

关键词： Contrastive Learning

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