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检索条件"任意字段=Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 4313 条记录，以下是91-100 订阅

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Comparative Analysis of Algorithms and Computational Architectures for Efficient Biological Data processing

Comparative Analysis of Algorithms and Computational Archite...

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euromicro conference on parallel, distributed and network-based processing

作者： Giuseppe Agapito Gaetano Guardasole Mario Cannataro Dep. of Law Economics and Social Sciences Cultura Romana del Diritto e Sistemi Giuridici Contemporanei Res. Cent. Lab. di Storia Giuridica ed Economica Res. Cent. University Magna Græcia Catanzaro Italy ex EP University of Calabria Rende Italy Dep. of Medical and Surgical Sciences Data Analytics Research Center University Magna Grecia Catanzaro Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

This paper presents a comparative analysis of algorithms and computational architectures for the efficient processing of biological data, particularly focusing on sparse matrix representations for biological pathways. Biological networks, such as protein-protein interaction (PPI) networks and metabolic pathways, are represented as graphs with nodes corresponding to molecular entities and edges representing biochemical interactions. These graphs are typically sparse, leading to challenges in data storage and computational efficiency. The study examines diverse sparse matrix formats, including Coordinate (COO), Compressed Sparse Row (CSR), and Compressed Sparse Column (CSC), emphasizing their application in biological data analysis. It further explores the potential of modern high-performance computing architectures, such as Graphics processing Units (GPUs), to accelerate the execution of graph-based algorithms. Algorithms such as Dijkstra’s, Breadth-First Search (BFS), and PageRank are adapted to leverage the efficiencies of sparse matrix representations, optimizing the analysis of large-scale biological networks for improved performance and scalability. Performance evaluations show that GPUs significantly outperform Central processing Units (CPUs) in processing largescale biological networks, reducing execution time and energy consumption while enhancing scalability. This research demonstrates how the use of an appropriate sparse matrix format and computational architecture can optimize the analysis of complex biological networks, providing insights into biological processes and therapeutic targets.

关键词： Proteins Performance evaluation Scalability Memory management Biological processes Biology Life sciences Computational efficiency Sparse matrices Resource management

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A Performance Model of In-Situ Techniques

A Performance Model of In-Situ Techniques

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euromicro conference on parallel, distributed and network-based processing

作者： Yi Ju Nicolas Vidal Adalberto Perez Ana Gainaru Fred Suter Stefano Markidis Philipp Schlatter Scott Klaskyll Erwin Laure Max Planck Computing and Data Facility Oak Ridge National Laboratory Friedrich-Alexander-Universität Erlangen-Nürnberg KTH Royal Institute of Technology

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The computational capacity of High-Performance Computing (HPC) systems increases continuously with the rapid development of central processing units (CPUs) and graphic processing units (GPUs), while the in-/output (IO) subsystem develops relatively slowly and storage capacity is also limited. Data-intensive applications, which are designed to leverage the high computational capacity of HPC resources, typically generate a considerable amount of data for post-processing visualizations and data analytics. The limited IO speed and storage space could lead to constraints in the actual performance of these applications and, therefore, scientific discovery. In-situ techniques, where data is visualized/analysed while still in memory rather than through disk, can contribute to alleviating these problems as they can reduce or even fully avoid data writing/reading through the IO subsystem to/from storage. However, the overall efficiency of insitu techniques crucially depends on the characteristics of both the in-situ tasks and the applications, and the resource distribution among them. Therefore, choosing the right in-situ approach (synchronous, asynchronous, or hybrid) and resource allocation is essential to minimize overhead and maximize the benefits of concurrent execution. In this paper, we present a performance model of in-situ techniques to find the most beneficial in-situ approach and the preferred resource configuration. We verify the high accuracy of our approach with over 6800 measurements and provide use cases with different applications.

关键词： Lead acid batteries Data analysis Accuracy High performance computing Graphics processing units Data visualization Central processing Unit Resource management

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Contributions to Accelerating a Numerical Simulation of Free Flow parallel to a Porous Plane

Contributions to Accelerating a Numerical Simulation of Free...

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euromicro conference on parallel, distributed and network-based processing

作者： Claudio Schepke Roberta A. Spigolon José Rufino Cesar F. Da C. Cristaldo Glener L. Pizzolato Laboratory of Advances Studies in Computation (LEA) Federal University of Pampa (UNIPAMPA) Alegrete Brazil Research Centre in Digitalization and Intelligent Robotics (CeDRI) Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC) Instituto Politécnico de Bragancá Bragança Portugal Laboratory of Computational Fluid Dynamics and Atmospheric Turbulence (LFCTA) Federal University of Pampa (UNIPAMPA) Alegrete Brazil

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Flow models over flat porous surfaces have applications in natural processes, such as material, food, chemical processing, or mountain mudflow simulations. The development of simplified analytical or numerical models can predict characteristics such as velocity, pressure, deviation length, and even temperature of such flows for geophysical and engineering purposes. In this context, there is considerable interest in theoretical and experimental models. Mathematical models to represent such phenomena for fluid mechanics have continuously been developed and implemented. Given this, we propose a mathematical and simulation model to describe a free-flowing flow parallel to a porous material and its transition zone. The objective of the application is to analyze the influence of the porous matrix on the flow under different matrix properties. We implement a Computational Fluid Dynamics scheme using the Finite Volume Method to simulate and calculate the numerical solutions for case studies. However, computational applications of this type demand high performance, requiring parallel execution techniques. Due to this, it is necessary to modify the sequential version of the code. So, we propose a methodology describing the steps required to adapt and improve the code. This approach decreases 5.3% the execution time of the sequential version of the code. Next, we adopt OpenMP for parallel versions and instantiate parallel code flows and executions on multi-core. We get a speedup of 10.4 by using 12 threads. The paper provides simulations that offer the correct understanding, modeling, and construction of abrupt transitions between free flow and porous media. The process presented here could expand to the simulations of other porous media problems. Furthermore, customized simulations require little processing time, thanks to parallel processing.

关键词： Codes Temperature Computational modeling Computational fluid dynamics Media parallel processing Numerical simulation Mathematical models Numerical models Surface treatment

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Federal Hierarchical Mutual Assistance based on Maritime Intelligent Decision Recommendation 24

Federal Hierarchical Mutual Assistance Based on Maritime Int...

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3rd International conference on Algorithms, Data Mining, and Information Technology, ADMIT 2024

作者： Liu, Xinru Kang, Jiabao Yang, Tingting Zhang, Shubo Navigation College Dalian Maritime University China School of Future Technology South China University of Technology China Peng Cheng Laboratory China

ISBN: (纸本)9798400718120

With the rapid development of artificial intelligence, edge computing and distributed collaborative processing, more effective solutions have been provided to solve the problems in the field of maritime communications. The surge in maritime activities and the growing number of vessels have precipitated an escalating demand for the real-time processing of oceanic data. However, the vast ocean environment and limited communication resources make the management and intelligent decision-making of heterogeneous data complex. In handling the collaborative processing of large-scale maritime fleet tasks, such as emergency responses to marine pollution, the network nodes of individual vessels are dispersed, and data timeliness is poor, which significantly impacts decision-making for ship operators. Additionally, the information exchanged between cooperating vessels must maintain privacy, making it difficult to develop personalized intelligent communication strategies. To address these potential issues, we propose a Federated Hierarchical Mutual Assistance (FHMA) algorithm for maritime recommendation systems. By integrating federated learning with layered privacy protection techniques, this approach ensures data privacy while also enabling model personalization. Additionally, it introduces compute power sharing and reward distribution mechanisms, incentivizing client participants to actively contribute their computational resources, thereby improving network efficiency. This algorithm not only efficiently handles large-scale, dynamically changing spatiotemporal data but also maintains high-quality recommendation results in parallel and distributed data processing environments, providing new directions for intelligent decision-making in the maritime communication field. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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HPC and GPU solutions for radio interferometry using RICK

HPC and GPU solutions for radio interferometry using RICK

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euromicro conference on parallel, distributed and network-based processing

作者： Emanuele De Rubeis Claudio Gheller Giovanni Lacopo Luca Tornatore Pascal Jahan Elahi Maciej Cytowski Giuliano Taffoni Ugo Varetto Dipartimento di Fisica e Astronomia Università di Bologna Istituto di Radioastronomia INAF via Gobetti 93/2 Bologna Italy Istituto di Radioastronomia INAF Via P. Gobetti 101 Bologna Italy INAF Astronomical Observatory of Trieste Università degli studi di Trieste Trieste Italy INAF Astronomical Observatory of Trieste via GB Tiepolo 11 Trieste Italy Pawsey Supercomputing Centre 1 Bryce Avenue Kensington WA Australia

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Since the last decade, radio astronomy has started a new era: the advent of the Square Kilometer Array (SKA), preceded by its pathfinders, will produce a huge amount of data that will be hard to process with a traditional approach. This means that the current state-of-the-art software for data reduction and imaging will have to be re-modeled to face such data challenge. In order to manage such an increase in data size and computational requirements, scientists need to exploit modern high-performance computing (HPC) architectures. In particular, heterogeneous systems, based on complex combinations of CPUs, accelerators, high-speed networks and composite storage devices need to be used in an efficient and effective way. In this paper, we present an overview on Radio Imaging Code Kernels (RICK; [1]; [2]; [3]), a code able to perform the most computationally demanding steps of w-stacking gridder algorithm exploiting distributed parallelism and GPU acceleration. GPU offloading is possible through CUDA, HIP, and OpenMP, aiming at the largest possible usability among multiple architectures. After detailing the (multi-)GPU approach to the problem and listing all the new code implementations, we analyze its performances considering both the computational and communication workload. We will show how the full, distributed GPU offload of the code, first of its kind and crucial to deal with increasingly large interferometric data, represents not only an extremely fast and optimized approach, but also the greenest one if compared to its parallel CPU counterpart. This code, now publicly available, has been tested with a wide variety of modern interferometers and SKA pathfinders. This represents, to date, the first example of radio imaging software fully enabled to GPUs, becoming a potential state-of-the-art approach for the upcoming SKA. Finally, we will also present the future perspectives about the code, planned to be converted into a library and possibly be used by any of the m

关键词： Performance evaluation Codes Radio astronomy High performance computing Graphics processing units Imaging Radio interferometry Computer architecture Software Usability

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Covariances computation in the Gaia AVU–GSR parallel Solver with I/O techniques: a performance study as a function of writing cycle length

Covariances computation in the Gaia AVU–GSR Parallel Solver...

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euromicro conference on parallel, distributed and network-based processing

作者： Valentina Cesare Ugo Becciani Alberto Vecchiato Mario Gilberto Lattanzi Marco Aldinucci Beatrice Bucciarelli Astrophysical Observatory of Catania National Institute for Astrophysics Italy Astrophysical Observatory of Turin National Institute for Astrophysics Italy Department of Computer Science University of Turin Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The solver module of the Astrometric Verification Unit-Global Sphere Reconstruction (AVU-GSR) pipeline aims to find the astrometric parameters of $\sim 10^{8}$ stars in the Milky Way, the attitude and instrumental settings of the Gaia satellite, and the parametrized post Newtonian parameter $\gamma$ with a resolution of 1 0 - 100 micro – arc seconds. To perform this task, the code, which runs in production on Leonardo CINECA infrastructure, solves a system of linear equations with the iterative LSQR algorithm, where the coefficient matrix is large (10-50 TB) and sparse and the iterations stop when least square convergence is reached. The solver was ported to GPU with CUDA, obtaining a $\sim 14 x$ acceleration factor over an original version CPU-parallelized with OpenMP. This work concentrates on a code section dedicated to covariances calculation, representing an important scientific task for Gaia mission, since the problems unknowns present strong correlations. Given the number of unknowns at mission end, the variances-covariances matrix is expected to occupy $\sim 1$ EB, which represents a substantial “Big Data” issue. To compute a subset of the total covariances, we defined an I/Obased pipeline made of two jobs. The first job, the LSQR, writes the files every $i \operatorname{tnCov} C P$ iterations, and the second job reads them and calculates the corresponding covariances. The two jobs can be launched either in sequence or concurrently. Previous studies demonstrated that the covariances calculation does not significantly slowdown the AVU-GSR production up to $\sim 3 \times 10^{7}$ covariances. Here we investigate the performance of the covariances pipeline as a function of $i t n \operatorname{Cov} C P$. The results show that writing smaller files more frequently or writing larger files less frequently does not affect the global performance of the solver, whose speed only depends on the number of covariances to calculate and of system unknowns.

关键词： Codes Satellites Pipelines Graphics processing units Stars Production Writing Mathematical models Sparse matrices Covariance matrices

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CTXNL: A Software-Hardware Co-designed Solution for Efficient CXL-based Transaction processing 25

CTXNL: A Software-Hardware Co-designed Solution for Efficien...

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30th ACM International conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Wang, Zhao Chen, Yiqi Li, Cong Guan, Yijin Niu, Dimin Guan, Tianchan Du, Zhaoyang Wei, Xingda Sun, Guangyu School of Integrated Circuits School of Computer Science Peking University Beijing China Peking University School of Integrated Circuits Beijing China Damo Academy Alibaba Group Hupan Lab Hangzhou China Shanghai Jiao Tong University Institute of Parallel and Distributed Systems Seiee Shanghai China School of Integrated Circuits Beijing Advanced Innovation Center for Integrated Circuits Peking University Beijing China

ISBN: (纸本)9798400710797

Transaction processing systems are the crux for modern data-center applications, yet current multi-node systems are slow due to network overheads. This paper advocates for Compute Express Link (CXL) as a network alternative, which enables low-latency and cache-coherent shared memory accesses. However, directly adopting standard CXL primitives leads to performance degradation due to the high cost of maintaining cross-node cache coherence. To address the CXL challenges, this paper introduces CTXNL, a software-hardware co-designed system that implements a novel hybrid coherence primitive tailored to the loosely coherent nature of transactional data. The core innovation of CTXNL is empowering transaction system developers with the ability to selectively achieve data coherence. Our evaluations on OLTP workloads demonstrate that CTXNL enhances performance, outperforming current network-based systems and achieves up to 2.08x greater throughput than vanilla CXL memory sharing architectures across universal transaction processing policies. © 2025 ACM.

关键词： Cache memory

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Research on Abnormal Power Consumption Behavior Detection and Load Forecasting System of Power Big Data based on FCM and Wavelet Neural network 14th

Research on Abnormal Power Consumption Behavior Detection an...

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14th International conference on Frontier Computing, FC 2024

作者： Pan, Jingni Chai, Yufeng Lv, Jiayu Wang, Xiaotian State Grid Shizuishan Power Supply Co. Ningxia China State Grid Ningxia Power Supply Co. Ningxia China State Grid Wuzhong Power Supply Co. Ningxia China

ISBN: (纸本)9789819627974

With the progress of smart grid, power systems are faced with more and more complex data challenges, including large-scale data, multi-sample types and low resource integration. This research takes power big data as the object, carries out abnormal power consumption behavior detection and load prediction based on Hadoop platform, and successfully realizes the visualization processing of power data. We thoroughly analyzed the needs of power big data visualization and designed the overall Hadoop-based architecture. In terms of key technologies, we adopt the visualization method based on geographic information, and use MapReduce to carry out parallel computation, and effectively identify and display different electrical behaviors. In addition, we proposed a wavelet neural network power load forecasting method based on FCM, and personalized classification of power users through FCM clustering. Combined with the prediction ability of wavelet neural network, accurate power load prediction was successfully realized, and parallel calculation was carried out on the distributed platform to further improve the prediction accuracy. Finally, we built a Hadoop big data experiment platform to verify the experimental results and performance of the proposed method. The experimental results show that our research has the ability to effectively cope with the big data challenges of power system, and provides important support and guidance for the safe and stable operation of power system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Abnormal Power Consumption Behavior Detection FCM Hadoop Platform Power Big Data Wavelet Neural network

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Dynamic Offloading Control for Waste Sorting based on Deep Q-network 24th

Dynamic Offloading Control for Waste Sorting Based on Deep...

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24th International conference on Algorithms and Architectures for parallel processing, ICA3PP 2024

作者： Wang, Jing Wang, Xiaoyang Guo, Jianxiong Tang, Zhiqing Ding, Xingjian Wang, Tian Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9789819615278

With the increasing concern for environmental protection and resource optimization, efficient waste sorting has become a serious challenge today. In this paper, we propose a new offloading control problem that aims to solve waste sorting in wireless bin communication networks. Due to limited computational power, bins belonging to embedded devices rely on simple classification models with varying accuracy. In this scenario, consider a network of intelligent bins, each acting as an independent agent capable of deciding to offload an image to the edge server with a more accurate but resource-intensive model when the local classification is deemed inaccurate. Thus, Our goal is to find a lightweight online offloading policy that can achieve the best possible sorting accuracy while balancing transmission traffic. The method utilizes a Deep Q-network algorithm that enables each intelligent bin to make image-processing decisions autonomously. In the experiment, we validate the effectiveness of improving the performance of waste classification compared with existing flow control methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： distributed Resource Allocation Intelligent Waste Management Waste Sorting Wireless Communication network

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STB-GraCapsNet: A Novel Capsule network Structure with Swin Transformer Block 25th

STB-GraCapsNet: A Novel Capsule Network Structure with Swin...

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25th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2024

作者： Zhang, Chunying Dong, Ziao Wang, Liya Liu, Lu Ren, Jing Ma, Jiang Liu, Bin College of Science North China University of Science and Technology 21 Bohai Road Caofeidian Xincheng Hebei Tangshan063210 China Big data and Social Computing Research Center Hebei University of Science and Technology Hebei Shijiazhuang0500198 China

ISBN: (纸本)9789819642069

Capsule network is a new type of neural network encoding features into capsules and constructing the part-whole relationships, which demonstrated good performance in image classification. However, it has some issues such as weak feature extraction ability and large number of training parameters, leading to performance decline when processing images with complex background. To address these issues, this article proposes a novel capsule network structure called STB-GraCapsNet, with key improvements as follows: 1) It uses Swin Transformer Block with the shift window attention mechanism for data preprocessing to obtain richer features;2) the features are then processed through Linear Embedding and passed into the Primary Capsule Layer to generate high-dimension feature capsules, which reduces primary capsules number and improves feature extraction ability of the network. Ablation and comparative experiments based on multiple datasets show that STB-GraCapsNet performs better on complex datasets: 77.8% and 36.2% reduction in parameters number of capsule structure, and 12.07% and 5.22% improvement in model accuracy, as compared to CapsNet and GraCapsNet, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Signal encoding

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