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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121-130 订阅

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Batch Updates of distributed Streaming Graphs using Linear Algebra 24

Batch Updates of Distributed Streaming Graphs using Linear A...

引用

Proceedings of the SC '24 Workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Elaheh Hassani Md Taufique Hussain Ariful Azad Dept. of Intelligent Systems Engineering Indiana University Bloomington IN USA

ISBN: (纸本)9798350355543

We develop a distributed-memory parallel algorithm for performing batch updates on streaming graphs, where vertices and edges are continuously added or removed. Our algorithm leverages distributed sparse matrices as the core data structures, utilizing equivalent sparse matrix operations to execute graph updates. By reducing unnecessary communication among processes and employing shared-memory parallelism, we accelerate updates of distributed graphs. Additionally, we maintain a balanced load in the output matrix by permuting the resultant matrix during the update process. We demonstrate that our streaming update algorithm is at least 25 times faster than alternative linear-algebraic methods and scales linearly up to 4,096 cores (32 nodes) on a Cray EX supercomputer.

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A Multi-holder Role and Strange Attractor-Based Data Possession Proof in Medical Clouds 24th

A Multi-holder Role and Strange Attractor-Based Data Possess...

引用

24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Guo, Jinyuan Sun, Lijuan Wu, Jingchen Chu, Chiawei Gao, Yutong Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Cybersecurity Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University Beijing China Intellectual Property Information Service Center BHU Beihang University Beijing China City University of Macau Taipa China Li’an International Education Innovation Pilot Zone Hainan International College of Minzu University of China Hainan Beijing China

ISBN: (纸本)9789819615278

Proof of Data Possession is a technique for ensuring the integrity of data stored in cloud storage. However, most audit schemes assume only one role for data owners, which is not suitable for complex Smart Healthcare Network with multiple roles. In such schemes addressing the risk of third-party auditors (TPA) colluding with other entities, blockchain is typically employed. However, this necessitates extensive computation, resulting in unacceptably high energy consumption and prolonged verification times. In this paper, we propose a Proof of Data Possession scheme tailored for medical network environments, which incorporates protocols designed for both healthcare professionals and patients, considering the diverse roles that generate data within medical network applications and the varying performance of the devices they use. During the generation of metadata for verification, the protocol incorporates the varying security risk levels associated with medical data. For subsequent metadata selection, an audit block extraction strategy based on strange attractors is employed, effectively mitigating the risk of collusion by third-party TPA, thereby enhancing the security and efficiency of the scheme. Additionally, we conducted experimental analysis and comparisons, demonstrating higher security and practical applicability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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parallel Runtime Interface for Fortran (PRIF): A Multi-Image Solution for LLVM Flang 24

Parallel Runtime Interface for Fortran (PRIF): A Multi-Image...

引用

Proceedings of the SC '24 Workshops of the international conference on High Performance computing, Network, Storage, and Analysis

作者： Dan Bonachea Katherine Rasmussen Brad Richardson Damian Rouson Computer Languages and Systems Software Group and NERSC Lawrence Berkeley National Laboratory USA

ISBN: (纸本)9798350355543

Fortran compilers that provide support for Fortran's native parallel features often do so with a runtime library that depends on details of both the compiler implementation and the communication library, while others provide limited or no support at all. This paper introduces a new generalized interface that is both compiler- and runtime-library-agnostic, providing flexibility while fully supporting all of Fortran's parallel features. The parallel Runtime Interface for Fortran (PRIF) was developed to be portable across shared- and distributed-memory systems, with varying operating systems, toolchains and architectures. It achieves this by defining a set of Fortran procedures corresponding to each of the parallel features defined in the Fortran standard that may be invoked by a Fortran compiler and implemented by a runtime library. PRIF aims to be used as the solution for LLVM Flang to provide parallel Fortran support. This paper also briefly describes our PRIF prototype implementation: Caffeine.

关键词： Caffeine

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Enhancing Distribution System Efficiency Through Vector Space Control of Power Inverters for Improved Power Factor

Enhancing Distribution System Efficiency Through Vector Spac...

引用

advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), international conference on

作者： Adriàn Mullo Manuel Jaramillo Diego Carrion Electrical Engineering Department Universidad Politécnica Salesiana Quito Ecuador

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Regarding stability and efficiency, DG, characterized by decentralized energy production on a small scale, presents unique challenges and opportunities. This study investigates the integration of dynamic loads into the test system, validating the effective operation of the implemented systems and controls while emphasizing improvements in power *** factor (PF) is a vital indicator of efficiency and is essential for optimizing electrical loads. This paper proposes utilizing space vector pulse width modulation (SVPWM) control strategies in power inverters to enhance the PF. The research implements distributed generation (DG) and dynamic reactive power compensation control to improve system conditions further. The study presents theoretical foundations, experimental results, and comparative analyses focused on PF enhancement. Regarding stability and efficiency, DG, characterized by decentralized energy production on a small scale, presents unique challenges and opportunities. This study investigates the integration of dynamic loads into the test system, validating the effective operation of the implemented systems and controls while emphasizing improvements in power factor.

关键词： Space vector pulse width modulation Reactive power Production Aerospace electronics Control systems Vectors Stability analysis Inverters distributed power generation Voltage control

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The Cloud-HPC infrastructure for Hazard Mapping and vulnerability Monitoring (HaMMon)

The Cloud-HPC infrastructure for Hazard Mapping and vulnerab...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Mauro Imbrosciano Eva Sciacca Fabio Vitello Leonardo Pelonero Francesco Franchina Ugo Becciani Iacopo Colonnelli Doriana Medić Istituto Nazionale di Astrofisica Osservatorio Astrofisico di Catania Catania Italy Dipartimento di Informatica Università degli Studi di Torino Torino Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The HaMMon project is the outcome of an industrial partnership that includes many Italian research institutions and private companies. It is led by UnipolSai and Leitha, and funded by the ICSC, the Italian National Research Center for High Performance computing, Big Data and Quantum *** ambition of HaMMon is to build a flexible and scalable platform to analyze the hydrogeological and atmospheric balance of the Italian territory. The project aims to expand the current knowledge in hazard mapping, monitoring, and forecasting from an industrial perspective by leveraging innovative technologies and the interdisciplinary activities carried out by the *** this work, we present the cloud-HPC infrastructure deployed in the High-Performance computing for Artificial Intelligence (HPC4AI) green data center of the University of Turin which supports the testing and development of HaMMon’s applications and services. We describe the current activities and preliminary results related to the integration of Photogrammetry techniques, Data Visualization and Artificial Intelligence technologies, applied on aerial images, to assess extreme natural events and evaluate their impact on risk-exposed assets.

关键词： Quantum computing Clouds High performance computing Hazards Artificial intelligence Forecasting Photogrammetry Monitoring Testing Drones

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Dynamic Bio-Information Image Recognition of Cancel Cell Death based on parallel Multiple Artificial Intelligence Modules

Dynamic Bio-Information Image Recognition of Cancel Cell Dea...

引用

international conference on Advanced Communication Technology (ICACT)

作者： Ming-Shen Jian Yi-Ling Ye Wen-Yu Chung Wen-Xuan Li Yan-Ting Lin Bo-Yuan Huang Javeria Sikandar Yu-Jung Chang Dept. of CSIE CCIS Lab National Formosa University Yunlin Taiwan Dept. of Biotechnology National Formosa University Yunlin Taiwan

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

This investigation purpose a unique Dynamic Bio-Information Image Recognition System (DBIRS) built for the real-time monitoring of cancer cell apoptosis produced by available medical therapy. Utilizing advances in microscopy, AI-based image recognition, and parallel computing across distributed edge devices, the proposed system dramatically boosts the precision and efficiency of cellular investigation. By deploying numerous AI modules adapted to distinct cellular states, the system provides automatic, high-accuracy recognition of morphological changes in cancer cells, hence minimizing the need on manual procedures. This technique offers remote monitoring and fast data processing via cloud integration, permitting continuous observation and timely intervention. The system's efficiency is proven through trials on K-562 cells, where it attained a recognition accuracy of 97.41%. These outcomes underline the potential of the proposed method to refine cytotoxicity assays, delivering a strong tool for increasing the efficacy and safety of immune cell treatments in clinical and research settings.

关键词： Training Image recognition Accuracy Real-time systems Safety Artificial intelligence Biological cells Remote monitoring Biomedical imaging Cancer

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Cost Minimization in Residential Hybrid Energy Systems Using Advanced Scheduling Algorithms

Cost Minimization in Residential Hybrid Energy Systems Using...

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advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), international conference on

作者： Chodagam Srinivas A. V. Pavan Kumar V. Pranay Teja Reddy B Pavan K Himasagar Reddy Y Yugandhar Reddy Department of EEE Madanapalle Institute of Technology & Science Andhra Pradesh India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

This study proposes an advanced optimization methodology for managing a grid-connected residential hybrid thermal and electrical energy system, incorporating a combined heat and power (CHP) fuel cell and a battery-based energy storage system (ESS). A predictive scheduling framework is designed to optimize the operational plan for distributed energy resources (DER) over a 24-hour period. The main objective is to reduce the operational expenses of a smart home by strategically allocating resources while considering dynamic electricity tariffs and the efficiency of the ESS. To achieve this, an enhanced Adaptive Gravitational Search Algorithm (AGSA) is employed. The research also includes a comparative evaluation of the AGSA against conventional Gravitational Search Algorithm (GSA) and Harmony Search Algorithm (HSA) methods. This comparison underscores the AGSA's superior performance in optimizing residential energy systems. The findings offer significant insights into the application of optimization algorithms for improving cost-efficiency and energy management in modern smart homes.

关键词： Costs Cogeneration Heuristic algorithms Tariffs Fuel cells Smart homes Optimal scheduling Batteries distributed power generation Thermal energy

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Exploiting GPU computing for effective Agent-Based simulation: initial experiments

Exploiting GPU computing for effective Agent-Based simulatio...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Marzio Pennisi Giuliana Franceschinis Daniele Baccega Simone Pernice Irene Terrone Computer Science Institute - DiSIT Università del Piemonte Orientale Alessandria Italy Department of Computer Science University of Turin Turin Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Agent-Based Modeling and Simulation (ABMS) has been increasingly applied in various research fields, thanks to the capability of these models to describe fine-grained realworld behavior and to the ease of interpretation by domain experts. However, such models lack a formal definition and well-defined semantics that are common to the different tools supporting ABMS. This may occasionally lead to greater complexity in interpreting the results with respect to other modeling approaches. To address this issue, an ABM semantics that adopts a continuous-time approach and a next-event time advance simulation algorithm has been formally defined and presented. Such an approach may lead to high computation times as it requires recalculations of activity rates for all the agents after each event. In this preliminary study, we exploit the FLAME GPU framework to evaluate the benefits that GPU computing may bring to the performance of our simulation algorithm.

关键词： Semantics Graphics processing units Fires Agent-based modeling Complexity theory

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A Cost-Effective Data Placement Strategy Based on Battle Royale Optimization in Multi-cloud Edge Environments 24th

A Cost-Effective Data Placement Strategy Based on Battle Ro...

引用

24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Zhang, Sen Xiao, Lili Luo, Xin Zhang, Zhaohui Wang, Pengwei School of Computer Science and Technology Donghua University Shanghai China

ISBN: (纸本)9789819615278

With the advent of big data era, there is a growing demand for users to store data in the cloud. To enhance the availability and privacy of data in the cloud and mitigate risks such as vendor lock-in, distributed multi-cloud storage has attracted widespread attention. However, accessing data from the cloud often encounters issues such as high latency and significant bandwidth costs. Utilizing edge resources can effectively address these problems, but it may potentially reduce data availability and increase storage costs. To this end, we combine multi-cloud and edge resources in this work, taking into account access restrictions on edge resources, and construct a multi-cloud edge storage model. Then, an Opposition-Based Learning Binary Battle Royale Optimizer (OBL-BinBRO) strategy is proposed to find optimal data placement solution, which assists users in determining the cloud and edge service providers for storing and accessing data. Using real-world data for extensive experiments, compared to several representative data placement strategies, the proposed method can effectively reduce the total cost of data placement for users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud storage Data availability Data placement Edge computing Multi-cloud

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AutoMiner: Reinforcement Learning-Based Mining Attack Simulator 24th

AutoMiner: Reinforcement Learning-Based Mining Attack Simula...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Li, Wei Xue, Lide Han, Ziyang Chen, Bingren Zhang, Xishan Zhou, Xuehai University of Science and Technology of China Hefei China ShangHai Municipal Big Data Center Shanghai China Anban Tech Co. Shanghai China State Key Laboratory of Processor Technologies Institute of Computing Technology Chinese Academy of Sciences Beijing China Cambricon Technologies Beijing China

ISBN: (纸本)9789819615247

As blockchain technology rapidly advances, it faces significant security threats from attacks like selfish mining, which exploit consensus algorithm vulnerabilities, undermining system security. Traditional analysis methods, primarily reliant on Markov models, are often utilized to address these complex threats. However, these methods frequently fall short in accurately simulating the multifaceted nature of blockchain attacks, leading to gaps in security measures. To bridge this gap, we introduce AutoMiner, an innovative reinforcement learning-based framework that integrates Miner Monte Carlo Tree Search (MMCTS) with Long Short-Term Memory (LSTM) networks for simulating and detecting potential mining attacks within the Proof of Work (PoW) framework. Our experimental results demonstrate AutoMiner’s capability to outperform traditional selfish and honest mining strategies, achieving up to 20% higher profits under specific scenarios. This highlights AutoMiner’s potential in enhancing blockchain security by providing a more comprehensive and effective approach to analyzing and mitigating mining attacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Blockchain Reinforcement learning Security analysis

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