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

共 24638 条记录，以下是151-160 订阅

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BrahMap: A scalable and modular map-making framework for the CMB experiments

BrahMap: A scalable and modular map-making framework for the...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Avinash Anand Giuseppe Puglisi Dipartimento di Fisica Università di Roma Tor Vergata Via della Ricerca Scientifica 1 Roma Italy INFN Sezione di Roma2 Università di Roma Tor Vergata Via della Ricerca Scientifica 1 Roma Italy Dipartimento di Fisica e Astronomia Università degli Studi di Catania Via S. Sofia 64 Catania Italy INAF Osservatorio Astrofisico di Catania Via S.Sofia 78 Catania Italy INFN Sezione di Catania Via S.Sofia 64 Catania Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The cosmic microwave background (CMB) experiments have reached an era of unprecedented precision and complexity. Aiming to detect the primordial B-mode polarization signal, these experiments will soon be equipped with $10^{4}$ to $10^{5}$ detectors. Consequently, future CMB missions will face the substantial challenge of efficiently processing vast amounts of raw data to produce the initial scientific outputs - the sky maps - within a reasonable time frame and with available computational resources. To address this, we introduce BrahMap, a new map-making framework that will be scalable across both CPU and GPU platforms. Implemented in C++ with a user-friendly Python interface for handling sparse linear systems, BrahMap employs advanced numerical analysis and high-performance computing techniques to maximize the use of super-computing infrastructure. This work features an overview of the BrahMap’s capabilities and preliminary performance scaling results, with application to a generic CMB polarization experiment.

关键词： Numerical analysis Scalability Object oriented modeling Pipelines Graphics processing units Numerical models parallel algorithms Object oriented programming Physics Python

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Improving Cloud Energy Efficiency through Machine Learning Models

Improving Cloud Energy Efficiency through Machine Learning M...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Eugenio Cesario Paolo Lindia Federica Lobello Andrea Vinci Shabnam Zarin Santina Capalbo DICES Department University of Calabria Rende Italy DIMES Department University of Calabria Rende Italy CNRNational Research Council of Italy Institute for HPC and Networking (ICAR) Rende Italy Computer Science Department Monmouth University West Long Branch NJ USA

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The rapid growth of Cloud computing and the expansion of large data centers have led to significant increases in energy consumption for hardware and cooling systems. This surge in power usage has raised operational costs, making energy efficiency a pressing issue for data center management. Virtual Machines (VMs) consolidation is a widely studied strategy to mitigate these costs by minimizing the number of active physical servers while maintaining user Service Level Agreements (SLAs). However, the success of consolidation largely depends on accurately forecasting VM resource needs. This paper presents the design and development of an energy-aware VMs allocation system that leverages predictive machine learning models to forecast future computational demands (CPU) of each VM. By anticipating these needs, the system optimally allocates VMs to available servers, effectively balancing energy savings with performance. A preliminary experimental evaluation, conducted on synthetic data, leverages predictive modeling within a server simulation to assess CPU and energy usage, demonstrating the effectiveness of energy-aware VMs allocation in optimizing resource utilization and reducing energy consumption.

关键词： Energy consumption Data centers Cloud computing Machine learning Predictive models Virtual machines Energy efficiency Servers Resource management Optimization

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Edge and Fog computing in Cyber-Physical Systems

Edge and Fog Computing in Cyber-Physical Systems

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Intelligent Control, computing and Communications (IC3), international conference on

作者： Priyanshi Gangrade Virendra Singh Kushwah Saroj S. Date Archana D. Jagdale Kamal Upreti Vinai K. Singh School of CSE and AI VIT Bhopal University Sehore India Dept. of AI and DS CSMSS Chh. Shahu college of Engineering Chh. Sambhajinagar Maharashtra India Barclays Services Corp New York USA Department of Computer Science Christ University Delhi NCR Ghaziabad India Department of Computer Science & Engineering GL Bajaj Group of Institutions Mathura India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The benefits of cyber-physical system advances include low latency and high bandwidth data processing in areas such as automotive, healthcare, and business automation. Traditional environments are often located in centralized and remote locations and cannot meet the demand. Edge computing and cloud computing have become fundamental concepts that will bring computing closer to the center of the data. Edge computing can reduce latency and bandwidth consumption by processing data on or near IoT devices. Fog computing adds another layer to this by distributing work and storage across multiple nodes, thus providing a scalable and flexible infrastructure. This article discusses the principles, benefits, and challenges of integrating edge and cloud computing into a CPS environment. It leverages the power of proximity-based edge computing and the centralized capabilities of cloud computing to provide scalable, instantaneous responses to CPS applications or time to optimize services. The demonstration shows a variety of things from smart cities to the use of IoT in healthcare in CPS. The article also covers some specific security and privacy issues and future directions in distributed computing, including the role of AI and 5G, which are supposed to offer additional resources in various applications.

关键词： Cloud computing Automation Smart cities Medical services Bandwidth Cyber-physical systems Data processing Internet of Things Edge computing Business

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Analysis of Time Synchronization Challenges in Digital Twins for Edge-Enabled Data Centers in Smart Cities Scenario

Analysis of Time Synchronization Challenges in Digital Twins...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Stefano Rinaldi Salvatore Dello Iacono Paolo Bellagente Marco Pasetti Department of Information Engineering University of Brescia Brescia Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The rapid adoption of Internet of Things (IoT) technologies and digital twins (DTs) is revolutionizing smart cities by facilitating improved real-time monitoring, simulation, and optimization of urban infrastructures. In data centers, digital twins offer virtual representations of physical infrastructure, enabling real-time monitoring, optimization of energy consumption, cooling efficacy, and resource utilization. This integration seeks to improve energy efficiency and sustainability in smart cities. In this context, edge computing is becoming essential solutions to mitigate latency, bandwidth, and privacy issues linked to centralized cloud system. A significant challenge in utilizing digital twins for edge-enabled data centers is attaining accurate time synchronization between the digital twin and its physical equivalent. Precise synchronization is crucial for real-time state representation, facilitating efficient monitoring, control, and optimization. The distributed characteristics of edge devices, varying network conditions, and constrained computational resources present considerable synchronization challenges. Factors such as clock drift, network jitter, and protocol constraints can result in temporal inconsistencies, compromising the reliability of digital twin applications. This paper offers an extensive examination of time synchronization issues in digital twins for edge-enabled data centers within smart city contexts. The study examines essential factors affecting synchronization accuracy and reliability, identifies significant bottlenecks, and suggests strategies to alleviate their impact. The results aid in the creation of effective synchronization mechanisms, facilitating the implementation of dependable digital twin systems.

关键词： Data centers Protocols Smart cities Real-time systems Digital twins Synchronization Reliability Sustainable development Optimization Edge computing

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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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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud computing

A Hybrid Approach Using Cryptography and K-Least Significant...

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advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), international conference on

作者： Anshika Goyal Bhumika Gupta Dept. of Computer Science & Engineering VMSB Uttarakhand Technical University Dehradun India Dept. of Computer Science & Engineering G.B. Pant Institute of Engineering & Technology Pauri India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT).

关键词： Cloud computing Steganography Image coding Computational modeling Data models Encoding Communications technology Encryption Cryptography Huffman coding

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Execution Time Optimization for Pipeline Deep Network Training on Multiple GPUs

Execution Time Optimization for Pipeline Deep Network Traini...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Bing-Jou Wu Ding-Yong Hong Pangfeng Liu Jan-Jan Wu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Institute of Information Science Academia Sinica Taipei Taiwan

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

As neural network models become gigantic, they increasingly demand more time and memory for training. To meet these demands, advanced parallel computing techniques have become essential. Our research focuses on hybrid parallelism, an extension of pipeline parallelism. Pipeline parallelism splits the neural network into sub-networks distributed across a sequence of processing units, enabling simultaneous processing of different data segments on each device. Hybrid parallelism extends this concept by allocating multiple devices to each sub-network. Our research focuses on optimizing hybrid parallelism by improving how the model is partitioned and how computational devices are assigned. We address these issues by modeling the neural network as a directed acyclic graph of tensor operators, and then demonstrating that optimally partitioning this graph is NP-complete. Then, we propose a two-step approach. The first step is to determine a sequence of nodes. The second step is dynamic programming, which partitions the sequence to maintain balance across the assigned devices. In transforming the graph into a sequence, we explore two methods: one employs topological sorting, while the other clusters non-sequential subgraphs. We apply both methods and select the more effective one based on performance outcomes. We implement our algorithm and conduct experiments. The results show substantial enhancements in both the speed of partitioning and training throughput, with speedups reaching up to 23 in partitioning time and a 1.3 -fold increase in training throughput.

关键词： Training Directed acyclic graph Computational modeling Neural networks Pipelines Computer architecture parallel processing Throughput Dynamic programming Topology

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A distributed Architecture for Particle Swarm Optimization Meta-heuristic Methods Based on Multi-agent Systems

A Distributed Architecture for Particle Swarm Optimization M...

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9th international conference on Smart City Applications, SCA 2024

作者： Adbi, Said Mouncif, Hicham Laboratory LIMATI Polydisciplinary Faculty Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (纸本)9783031886522

This research presents a novel parallel model for Particle Swarm Optimization (PSO), named Multi-agent Agent-based PSO (MAPSO). The model synergizes multi-agent system principles with parallel computing techniques to enhance optimization processes. By transforming particles into autonomous intelligent agents capable of asynchronous execution and advanced learning, the swarm operates as a cohesive multi-agent system. This innovative approach seeks to improve the efficiency and performance of conventional PSO algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Agent Artificial Intelligence (AI) Distribution Metaheuristic Multi-agent System (MAS) Optimization

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A Methodical Approach to parallel IO Analysis in distributed Deep Learning Applications 30th

A Methodical Approach to Parallel IO Analysis in Distribut...

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30th international conference on parallel and distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied computing, CSCE 2024

作者： Parraga, Edixon Leon, Betzabeth Mendez, Sandra Rexachs, Dolores Suppi, Remo Luque, Emilio Bellaterra Barcelona Spain Barcelona Spain

ISBN: (纸本)9783031856372

Deep learning applications have become crucially important for the analysis and prediction of massive volumes of data. However, these applications impose substantial input/output (I/O) loads on computing systems. Specifically, when running on distributed memory systems, they manage large amounts of data that must be accessed from parallel file systems during the training stage using the available I/O software stack. These accesses are inherently intensive and highly concurrent, which can saturate systems and adversely impact application performance. Consequently, the challenge lies in efficiently utilizing the I/O system to allow these applications to scale. When the volume of data increases, access can generate high training latency and add overhead significantly when data exceeds the main memory capacity. Therefore, it is essential to analyze the behavior of the I/O patterns generated during the training stage by reading the data set to analyze the behavior when the application scales and what amount of resources it will need. The paper presents a methodology to analyze parallel I/O patterns in Deep Learning applications in this context. Our methodological approach mainly aims at providing users with complete and accurate information. This involves a thorough understanding of how the application, the dataset, and the system parameters can significantly influence the parallel I/O of their deep learning application. We seek to empower users to make informed decisions through a structured methodology that allows them to identify and modify configurable elements effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive 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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