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

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

24th International Conference on Algorithms and Architecture...

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

ISBN: (纸本)9789819615278

The proceedings contain 131 papers. The special focus in this conference is on Algorithms and Architectures for parallel Processing. The topics include: MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework;SSC: An SRAM-Based Silence computing Design for On-chip Memory;TP-BFT: A Faster Asynchronous BFT Consensus with parallel Structure;LTP: A Lightweight On-Chip Temporary Prefetcher for Data-Dependent Memory Accesses;A Neural Network-Based PUF Protection Method Against Machine Learning Attack;Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs;multidimensional Intrinsic Identity Construction and Dynamic Seamless Authentication Schemes in IoT Environments;invisible Backdoor Attack with Image Contours Triggers;finestra: Multi-aggregator Swarm Learning for Gradient Leakage Defense;DIsFU: Protecting Innocent Clients in Federated Unlearning;multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning;dynamic Privacy Protection with Large Language Model in Social Networks;a Dynamic Symmetric Searchable Encryption Scheme for Rapid Conjunctive Queries;a Data Watermark Scheme Base on Data Converted Bitmap for Data Trading;distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc Networks;mitigating Over-Unlearning in Machine Unlearning with Synthetic Data Augmentation;AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection;efficient and Privacy-Preserving Ranking-Based Federated Learning;on-Chain Dynamic Policy Evaluation for Decentralized Access Control;DPG-FairFL: A Dual-Phase GAN-Based Defense Framework Against Image-Based Fairness Data Poisoning Attacks in Federated Learning.

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

24th International Conference on Algorithms and Architecture...

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

ISBN: (纸本)9789819615247

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

24th International Conference on Algorithms and Architecture...

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

ISBN: (纸本)9789819615476

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Multivariate Workload Aware Correlation Model for Container Workload Prediction 29

Multivariate Workload Aware Correlation Model for Container ...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Zhang, Man An, Chunyan Yang, Conghao Inner Mongolia University College of Computer Science Hohhot China

ISBN: (纸本)9798350330717

The container has become the mainstream technology in the industry due to its fast delivery speed, low resource consumption, and good portability. Accurate and effective container workload prediction models are crucial for proactive autoscaling. Most current workload prediction models either focused on a single application or just predicted the demand for a specific resource type, such as CPU. They can hardly support the comprehensive prediction of multiple resource demands for hybrid applications in container cloud scenarios. In this paper, we proposed a novel framework called "MAN4Container"to bridge the existing gap. This framework is comprised of two essential components: a Multi-Resource Correlation Weight (MRCW) algorithm and a Multivariate Workload Aware Correlation (MWAC) model. The MRCW algorithm classifies multivariate workloads into different types, and the MWAC model predicts each type of workload by combining long-term and short-term predictions. The experimental results on the Alibaba-Cluster-Trace-V2018 and Google cluster datasets have shown that our framework MAN4Container has higher prediction accuracy than other baseline models. © 2023 IEEE.

关键词： Cloud computing Container Workload Multivariate Prediction Approaches Prediction Time Series Prediction

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

24th International Conference on Algorithms and Architecture...

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

ISBN: (纸本)9789819615414

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

24th International Conference on Algorithms and Architecture...

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

ISBN: (纸本)9789819615506

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MUSE: A Programmable Metadata Load Estimation Interface for Ceph File System 29

MUSE: A Programmable Metadata Load Estimation Interface for ...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Shao, Xinyang Li, Cheng Xu, Yinlong University of Science and Technology of China Anhui Hefei China Ustc Anhui Province Key Laboratory of High Performance Computing Anhui Hefei China

ISBN: (纸本)9798350330717

CephFS represents a prominent distributed file system that utilizes directory fragment migration to achieve improved runtime balance. However, its imprecise imbalance model and subtree selection algorithms can result in suboptimal performance. Our prior work, Lunule, enhances CephFS by introducing an imbalance factor model and a workload-aware load estimation policy. Nevertheless, Lunule's built-in workload-aware planner still relies on a unified formula with adjustable coefficients, representing a one-size-fits-all approach. In this study, we introduce MUSE, a novel and user-friendly programmable interface that specifically focuses on subtree migration planning. MUSE effectively separates the complex and challenging task of evaluating subtree loads for different workloads, enabling designers to manipulate expected loads in the subsequent epoch. This facilitates the selection of appropriate subtrees for migration and opens up possibilities for implementing true isolated workloadaware policies. Through the utilization of two small Lua scripts, we demonstrate that MUSE achieves comparable load balancing effects and performance to CephFS and Lunule in workloads characterized by temporal and spatial locality. © 2023 IEEE.

关键词： distributed file system Load balance Metadata

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

24th International Conference on Algorithms and Architecture...

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

ISBN: (纸本)9789819615445

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parallel Network - A Deep Learning Approach for Blood Vessel Segmentation in Retinal fundus Images 2

Parallel Network - A Deep Learning Approach for Blood Vessel...

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2nd IEEE international conference on advances in Electrical, computing, Communication and Sustainable Technologies (IEEE ICAECT)

作者： Sivapriya, G. Gowri, P. Praveen, V Varshini, Vishnu Sanjeevi, S. Tharani, B. Kongu Engn Coll Dept ECE Erode Tamil Nadu India Bannari Amman Inst Technol Dept CSE Erode Tamil Nadu India

ISBN: (纸本)9781665411202

In this modern era, computerized Retinal Blood Vessel (RVS) segmentation plays major role in diagnosis of various diseases like Diabetic Retinopathy (DR), Neovascularization, Hemorrhage. Early detection of retinal diseases can aid in the preservation of the patient's vision. Deep learning based a new modified Convolution Neural Network (CNN) architecture is proposed for RVS. The Proposed architecture has two layers in which the layer one is for detecting the blood vessels of size thick and layer two for small vessel detection. Then the output of two layers has been combined to get the desired output. The proposed method is tested on the generally accepted public databases for this research field, DRIVE and STARE. In addition, various pre-processed methods are studied to investigate network performance enhancement. The pre-processing of the raw input image is much needed for better segmented output of blood vessels. In this work CLAHE, normalization and morphological operation of opening are done in the pre-processing stage. The proposed parallel architecture is then used to segment the retinal vessels. Accuracy, specificity, and sensitivity achieved with this proposed network are 98.02, 98.02, 88.04 respectively. Regardless of vessel thickness, the developed system performs better in terms of vessel extraction. The architecture can also be used to identify blood vessels that are frequently obstructed by factors such as lesions and hemorrhages, regardless of vessel thickness.

关键词： parallel architecture Convolutional Neural Network Blood vessels Fundus images Vessel Segmentation

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UpDown: Combining Scalable Address Translation with Locality Control

UpDown: Combining Scalable Address Translation with Locality...

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2024 Workshops of the international conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Wang, Yuqing Perarnau, Swann Chien, Andrew A. University of Chicago Department of Computer Science Chicago United States Argonne National Laboratory Chicago United States

ISBN: (纸本)9798350355543

Global shared memories of petabytes are increasingly useful for applications, but traditional page-based techniques do not scale (limited reach), and scalable techniques such as segments fail to provide needed locality control. We propose a novel two-level translation scheme, UpDown, that provides compact, efficient access control to distributed segments of arbitrary size and data layout control, solving problems of reach and data locality. UpDown's novel two-level structure separates access control from data layout, allowing applications to manage locality cheaply, without privileged *** evaluate UpDown against page-based systems, using big data computations. Our results show that UpDown is scalable and provides effective global data locality management. UpDown's translation states are an average of ~620 billion times smaller total and ~130 billion times smaller local translation state. Simulations with synthetic traces show that the two-level translation scheme enables fast, user-level management of data locality in a scalable parallel machine. © 2024 IEEE.

关键词： address translation global address space page translation parallel machine process isolation segment translation virtual memory

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