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28th international European conference on parallel and Distributed Computing, Euro-Par 2022

28th International European Conference on Parallel and Distr...

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28th international European conference on parallel and Distributed Computing, Euro-Par 2022

ISBN: (纸本)9783031125966

the proceedings contain 25 papers. the special focus in this conference is on parallel and Distributed Computing. the topics include: Analysing Supercomputer Nodes Behaviour with the Latent Representation of Deep Learning Models;Accelerating parallel Operation for Compacting Selected Elements on GPUs;A Methodology to Scale Containerized HPC Infrastructures in the Cloud;cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads;multi-objective Hybrid Autoscaling of Microservices in Kubernetes Clusters;two-Agent Scheduling with Resource Augmentation on Multiple Machines;IP. LSH. DBSCAN : Integrated parallel Density-Based Clustering through Locality-Sensitive Hashing;graphGuess: Approximate Graph processing System with Adaptive Correction;deterministic parallel Hypergraph Partitioning;MARTINI: the Little Match and Replace Tool for Automatic Application Rewriting with Code Examples;OmpSs-2@Cluster: Distributed Memory Execution of Nested OpenMP-style Tasks;Generating Work Efficient Scan Implementations for GPUs the Functional Way;automatic parallelization of Python Programs for Distributed Heterogeneous Computing;A Hybrid Piece-Wise Slowdown Model for Concurrent Kernel Execution on GPU;accelerating Brain Simulations with the Fast Multipole Method;high-Performance Spatial Data Compression for Scientific Applications;accurate Fork-Join Profiling on the Java Virtual Machine;characterization of Different User Behaviors for Demand Response in Data Centers;on-the-Fly Calculation of Model Factors for Multi-paradigm Applications;relative Performance Projection on Arm architectures;exploring Scheduling algorithms for parallel Task Graphs: A Modern Game Engine Case Study;Decentralized Online Scheduling of Malleable NP-hard Jobs;A Bi-Criteria FPTAS for Scheduling with Memory Constraints on Graphs with Bounded Tree-Width.

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16th international conference on parallel Computational Technologies, PCT 2022

16th International Conference on Parallel Computational Tech...

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16th international conference on parallel Computational Technologies, PCT 2022

ISBN: (纸本)9783031116223

the proceedings contain 22 papers. the special focus in this conference is on parallel Computational Technologies. the topics include: High-Performance Calculations for Modeling the Propagation of Allergenic Plant Pollen in an Atmospheric Boundary Layer;simulation of Nonstationary thermal Fields in Permafrost Using Multicore Processors;HPC TaskMaster – Task Efficiency Monitoring System for the Supercomputer Center;modeling of Two-Phase Fluid Flow Processes in a Fractured-Porous Type Reservoir Using parallel Computations;MPI-Based PFEM-2 Method Solver for Convection-Dominated CFD Problems;construction of a parallel Algorithm for the Numerical Modeling of Coke Sediments Burning from the Spherical Catalyst Grain;parallel Simulations of Dynamic Interaction Between Train Pantographs and an Overhead Catenary Line;a New Approach to the Supercomputer Simulation of Carbon Burning Sub-grid Physics in Ia Type Supernovae Explosion;visualizing Multidimensional Linear Programming Problems;Tuning of a Matrix-Matrix Multiplication Algorithm for Several GPUs Connected by Fast Communication Links;Measuring the Effectiveness of SAT-Based Guess-and-Determine Attacks in Algebraic Cryptanalysis;Implementation of the Algebraic Multigrid Solver Designed for Graphics processing Units Based on the AMGCL Framework;VGL Rating: A Novel Benchmarking Suite for Modern Supercomputing architectures;kinetic Modeling of Isobutane Alkylation with Mixed C4 Olefins and Sulfuric Acid as a Catalyst Using the Asynchronous Global Optimization Algorithm;quantum-Chemical Calculations of the Enthalpy of Formation of Some Tetrazine Derivatives;preface;Analysis of Block Stokes-Algebraic Multigrid Preconditioners on GPU Implementations;Compact LRnLA algorithms for Flux-Based Numerical Schemes;parallel Methods for Solving Saddle Type Systems;optimization of the Computational Process for Solving Grid Equations on a Heterogeneous Computing System.

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VLSI Architecture for Fractional-Order Adaptive Filter

VLSI Architecture for Fractional-Order Adaptive Filter

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international conference on Trends in Electronics and Informatics (ICEI)

作者： L Dharma Teja Middinti Archana Reddy Bommakanti Pravali Kolupula Prashanth Department of ECE VNRVJIET Hyderabad

Traditional adaptive filters, which function under the assumption that both the signal and the noise have a Gaussian distribution, show a significant drop in performance when operating in environments that are not Gaussian. the proposed fractional-order adaptive filters (FoAFs) provide a solution to this issue. these filters operate under the assumption that both the signal and the noise are symmetric-stable random processes. Because this proposed system makes use of parallel processing, it is possible to increase throughput while simultaneously minimizing the amount of hardware that is duplicated and the amount of power that is wasted. the results are analyzed in light of research that has been done and published earlier. the newly created parallel FoAF is going to be suitable for use as a sensor node in both ASIC and FPGA. this is because both of these technologies are programmable logic devices.

关键词： Technological innovation Programmable logic devices Adaptive filters Very large scale integration parallel processing throughput Market research

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Accelerating Sample-based GNN Training by Feature Caching on GPUs 7

Accelerating Sample-based GNN Training by Feature Caching on...

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7th IEEE international conference on Smart Cloud, SmartCloud 2022

作者： He, Yuqi Lai, Zhiquan Ran, Zhejiang Zhang, Lizhi Li, Dongsheng National University of Defense Technology National Key Laboratory of Parallel and Distributed Processing College of Computer Changsha China

ISBN: (数字)9781665451796

ISBN: (纸本)9781665451796

the existing graph neural network (GNN) systems adopt sample-based training on large-scale graphs over multiple GPUs. Although they support large-scale graph training, large data loading overhead is still a bottleneck. In this work, we propose SCGraph, a method that supports GPU high-speed feature caching. We classify the graph vertices sorted by out-degrees. For high out-degree vertices, we set grading caches via different GPUs to increase the overall cache content through NVLink high-speed data transmission between them. For low out-degree vertices, we expand training vertices' neighborhood in advance to regenerate cache. We evaluate SCGraph against two state-of-the-art industrial GNN frameworks, i.e., DGL and PaGraph on two datasets Reddit and ogbn-products. Experimental results show that SCGraph achieves up to 1.83× performance speedup over the state-of-the-art baselines. © 2022 IEEE.

关键词： Graph neural networks

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7th Latin American High Performance Computing conference, CARLA 2020

7th Latin American High Performance Computing Conference, CA...

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7th Latin American High Performance Computing conference, CARLA 2020

ISBN: (纸本)9783030680343

the proceedings contain 15 papers. the special focus in this conference is on Latin American High Performance Computing. the topics include: Electricity Demand Forecasting Using Computational Intelligence and High Performance Computing;parallel/Distributed Generative Adversarial Neural Networks for Data Augmentation of COVID-19 Training Images;analysis of Regularization in Deep Learning Models on Testbed architectures;computer Application for the Detection of Skin Diseases in Photographic Images Using Convolutional Neural Networks;Neocortex and Bridges-2: A High Performance AI+HPC Ecosystem for Science, Discovery, and Societal Good;Fostering Remote Visualization: Experiences in Two Different HPC Sites;high Performance Computing Simulations of Granular Media in Silos;performance Analysis of Main Public Cloud Big Data Services processing Brazilian Government Data;accelerating Machine Learning algorithms with TensorFlow Using thread Mapping Policies;Methodology for Design and Implementation an Efficient HPC Cluster;estimating the Execution Time of the Coupled Stage in Multiscale Numerical Simulations;Using HPC as a Competitive Advantage in an international Robotics Challenge;a Survey on Privacy-Preserving Machine Learning with Fully Homomorphic Encryption.

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Distributed Exact Structural Clustering on Large Graph

Distributed Exact Structural Clustering on Large Graph

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international conference on parallel and Distributed Systems (ICPADS)

作者： Jinyu Zhou Chuitian Rong Ding Liu Zhengyi Chai Tiangong University Tianjin China

Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for Networks) is a well-studied graph clustering algorithm that has been widely applied over the years. However, the processing time cost of sequential SCAN and its variants cannot be tolerable on large graphs. the existing parallel variants of SCAN are focusing on fully utilizing the computing capacity of multi-core computer architectures and inventing sophisticated optimization techniques on single computing node. As the objects and their relationships in cyberspace are varying over time, the scale of graph data is increasing with high rate. the graph clustering algorithms on single node are facing challenges from limited computing resources, such as computing performance, memory size and storage volume. the distributed processing algorithm is called for processing large graphs. this work presents a distributed structural graph clustering algorithm using Spark. Furthermore, the edge pruning technique and adaptive checking are optimized to improve clustering efficiency. And the label propagation clustering is simplified to reduce the communication cost in the distributed clustering iterations. It also conduct extensive experiments on real-world datasets to testify the efficiency and scalability of the distributed algorithm. Experimental results show that efficient clustering performance can be achieved and it scales well under different settings.

关键词： Distributed processing Costs Social networking (online) Scalability Clustering algorithms Focusing Cyberspace

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Research on Optimization of Stream Data processing Algorithm for Multi-Core Processor Based on Mapreduce

Research on Optimization of Stream Data Processing Algorithm...

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IEEE international conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Yingmei Shang Genjie Wang Jing Ruan Anhui Sanlian University Hefei Anhui China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

With the rapid development of information technology and the popularization of the Internet, streaming data processing has become one of the core tasks of big data analysis. With their parallel computing capabilities, multi-core processors show great potential in efficiently processing massive streams of data. However, the traditional streaming data processing algorithm often faces the problems of uneven resource allocation, unbalanced load and high communication overhead when facing multi-core architecture, which limits the overall performance of the system. Based on MapReduce programming model, this paper discusses the optimization strategy of streaming data processing algorithm in multi-core processor environment. Firstly, this paper analyzes the limitations of the existing MapReduce framework in the stream data processing scenario, including the delay increase caused by batch processing tendency and the lack of adaptability to dynamic data flow. then, an improved Partitioning Strategy is proposed, which can dynamically adjust task allocation according to data flow characteristics, ensure load balancing among cores, and minimize cross-core communication costs. In order to further improve the processing efficiency, this paper also designs a kernel processor stream data processing algorithm based on MapReduce-partitioning strategy, which can dynamically adjust the task priority and optimize the execution order according to real-time performance monitoring information. the experimental results show that the proposed optimization method not only effectively improves the speed of upstream data processing, but also enhances the stability and reliability of the system, which provides theoretical support and technical reference for future large-scale streaming data analysis.

关键词： Multicore processing Heuristic algorithms Data processing Load management throughput Stability analysis Partitioning algorithms Resource management Streams thermal stability

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Combined Deep learning architectures using Fusion Rules for Target Recognition in SAR images

Combined Deep learning architectures using Fusion Rules for ...

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international conference on Advanced Technologies for Signal and Image processing (ATSIP)

作者： K. Cheikh N. Mimoun R. Aitahcene A. Toumi SISCOM Laboratory University of Constantine 1 Constantine Algeria Lab-STICC UMR CNRS 6285 ENSTA Bretagne Brest France

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

In radar image detection and classification systems, one of the main challenges lies in the analysis and interpretation of captured images, whether for moving or stationary targets. this challenge has become increasingly complex due to the large volumes of radar data processed. Consequently, various techniques and methods for radar target detection and classification have been adopted, particularly those utilizing artificial intelligence paradigms. In this context, we explore data fusion techniques for Synthetic Aperture Radar (SAR) image classification. We propose a novel parallel architecture featuring three classifiers: the basic CNN, Inception-v3, and ResNet 50. the elementary decisions from each classifier are sent to a fusion center for the final decision. At the fusion center, a majority voting system is applied to consolidate the decisions from these classifiers. To evaluate the performance of this approach, we use the MSTAR (Moving and Stationnary Target Acquisition and Recognition) dataset. Simulation results demonstrate the effectiveness of the proposed approach, achieving a correct classification rate of 99.23%, which surpasses the performance of each individual CNN model.

关键词： Target recognition Spaceborne radar Simulation Data integration Radar detection Radar imaging Radar polarimetry

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Towards Orchestration of Cloud-Edge architectures with Kubernetes 7th

Towards Orchestration of Cloud-Edge Architectures with Kuber...

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7th EAI international conference on Science and Technologies for Smart Cities, SmartCity360° 2021

作者： Böhm, Sebastian Wirtz, Guido Distributed Systems Group University of Bamberg Bamberg Germany

ISBN: (纸本)9783031063701

Edge computing brings computational resources, reliable network infrastructure, and real-time capabilities closer to devices. Providing resources and workloads at the edge is mainly realized with container technology. the appropriate placement in terms of when, where, and how to provide containerized workloads is still an ongoing problem domain. Kubernetes is nowadays the state-of-the-art platform for containerized service orchestration to tackle these issues. Although Kubernetes misses capabilities like using real-time network metrics for scheduling and topology awareness, it is still used for realizing cloud-edge architectures. In this paper, we analyze current cloud-edge architectures implemented with Kubernetes and how they solve general requirements of edge computing and orchestration. Furthermore, we identify shortcomings in these implementations based on the fundamental requirements of edge computing and orchestration. Even if issues like obtaining network-related metrics and implementing topology awareness are solved well, other requirements like real-time processing of metrics, fault-tolerance, and the placement of container registries are in early stages. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Containers

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ORL-EPM: A Profit-Aware and Load-Driven Heterogeneous Resource Management Scheme with Collaborative Edge Computing 24th

ORL-EPM: A Profit-Aware and Load-Driven Heterogeneous Resou...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Huang, Fengyi Zhang, Yutao Wang, Wenhua Guo, Jianxiong Xu, Yang Fan, Wentao Wang, Tian Hong Kong Baptist University Hong Kong China Hong Kong Baptist University United International College Beijing Normal University Zhuhai China Beijing Normal University Zhuhai China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9789819615414

To address the limitations of energy-efficient but computationally limited ARM64-based AI edge devices and general-purpose edge servers, this paper proposes a Decentralized Collaborative Heterogeneous Edge Computing (DCEC) architecture. this architecture combines edge servers with embedded AI devices to achieve low latency and enhanced computational capabilities. Commercially, the DCEC framework is divided into edge private clouds and edge public clouds, aiming to maximize the profits of Edge Service Providers (ESPs) through dynamic resource management. Considering task dynamism, complexity, and resource heterogeneity, this complex problem is formulated as a multi-stage mixed-integer nonlinear programming (MINLP) problem. We developed the ORL-EPM resource management framework—a three-layer optimization system that adjusts task scheduling based on varying latency sensitivity weights and heterogeneous resource demands. Additionally, we introduced a resource collaboration system based on resource leasing to manage resource overloads and accommodate diverse task complexities. this system includes three Economic Payment Models (EPMs) designed to achieve efficient and profitable resource utilization. Extensive simulation results indicate that this method ensures convergence and closely approximates optimal solutions across various scenarios, significantly outperforming existing methods. Testbed experiments demonstrate that the DCEC architecture can reduce latency by up to 21.83% in real-world applications, notably exceeding previous approaches. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy efficiency

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