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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111-120 订阅

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TSR: a Location Privacy Preservation Mechanism in Public Transportation Route Planning Service 25th

TSR: a Location Privacy Preservation Mechanism in Public T...

引用

25th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Wu, Wei Sang, Yingpeng Zhang, Haibo Hong, Shen School of Computer Science and Engineering Sun Yet-sen University Guangzhou China School of Computing University of Otago Dunedin New Zealand School of Engineering and Technology Central Queensland University Brisbane Australia

ISBN: (纸本)9789819642069

Location-based service (LBS) applications are increasingly popular for travelling. The public transit scenario is very common in urban areas, yet there is a lack of effective privacy protection mechanisms to safeguard users’ location privacy when using services from untrusted third-party LBS providers. This paper introduces Transportation Shift-based Routing (TSR), a privacy protection mechanism for public transportation travel scenarios. It allows users to query public transit routes through applications that comply with this mechanism without disclosing any significant details about the location, and fills the gap in privacy protection mechanisms in public transportation scenarios. The fundamental concept is to shift the query’s starting and ending points to nearby stations and repeatedly perform perturbations that satisfy geo-indistinguishability, submitting a collection of perturbed station pairs to the LBS. TSR computes the set of obtained routes to derive an optimal route plan. The best route calculated can maintain service availability and protect user location privacy, with only a minimal difference from the best route obtained by direct query. TSR also allows users to freely choose privacy parameters for different scenarios and strives to ensure service quality, offering high flexibility. We implement the TSR application and conduct experiments with real-world map services. The experimental outcomes indicate that TSR successfully strikes a balance between preserving location privacy and maintaining service usability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jia, Yan Cheng, Yuqing Xu, Kele Dou, Yong Qiao, Peng He, Zhouyu National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue's surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network's ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI. © 2025 IEEE.

关键词： contour extraction counterfactual inference self-supervised learning speech production ultrasound image

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A Cloud+HPC hub for astrophysics and cosmology communities

A Cloud+HPC hub for astrophysics and cosmology communities

引用

Euromicro conference on parallel, distributed and Network-Based Processing

作者： Matteo Pasqui ICSC “Centro Nazionale di Ricerca in HPC Big Data and Quantum Computing” INFN Sezione di Roma Tor Vergata

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

The advancement of data-intensive analysis has emphasised the need for extensive, scalable environments that enable easy access to diverse computational resources. This project proposes the development of a Scientific Hub for the astrophysics and cosmology communities, leveraging a global, cloud-based infrastructure that integrates Kubernetes-based offloading to ensure efficient, user-friendly access to High Performance computing (HPC), High Throughput computing (HTC) and cloud resources. This solution introduces “transparent offloading” of containerized workloads using Kubernetes API primitives, creating a unified cloud-native interface that abstracts complexities and optimally allocates resources based on the specific needs of each task. Through the Scientific Hub, users could access JupyterHub-supported environments with configurable resources, persistent data storage and secure authentication, facilitating the execution and sharing of scientific workflows in a robust, scalable manner. This contribution showcases the aims and challenges of the development of the Scientific Hub on ICSC partners’ resources (including the Leonardo supercomputer at CINECA), along with some key use cases.

关键词： Astrophysics High performance computing Memory Logic gates Fasteners Throughput Data processing Supercomputers Computational efficiency Complexity theory

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Utilizing Network Hardware parallelism for MPI Partitioned Collective Communication

Utilizing Network Hardware Parallelism for MPI Partitioned C...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Yıltan Hassan Temuçin Amirreza Barati Sedeh Whit Schonbein Ryan E. Grant Ahmad Afsahi ECE Department Queen’s University Kingston ON Canada Sandia National Laboratories Albuquerque New Mexico USA

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

parallel distributed applications running on large-scale high-performance computing systems depend on effective point-to-point and collective communication to meet performance goals. Beginning with version 4.0, the Message Passing Interface (MPI) introduced the partitioned communication API, providing tools for addressing communication bottlenecks raised by hybrid communication models. This API allows individual actors (CPU threads, GPU threads, etc.) to initiate communication on portions of complete buffers, enabling additional communication/computation overlap. Intuitively, the utility of partitioned communication could benefit from network-level support: If there are multiple paths between endpoints, an MPI-aware network could disperse partitions across these paths, avoiding the data serialization entailed by a dependency on a single path. The Cerio Rockport Ethernet Fabric has the ability to expose this capability to communication middleware. In this work we develop this capability to allow for user-level path selection for MPI partitioned communication and explore how this capability impacts point-to-point performance, collective design, and Allreduce efficiency in a Large Language Model task

关键词： Message passing Large language models High performance computing Ethernet parallel processing Hardware Fabrics Libraries Middleware Message systems

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Communication-reduced Conjugate Gradient Variants for GPU-accelerated Clusters

Communication-reduced Conjugate Gradient Variants for GPU-ac...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Massimo Bernaschi Mauro G. Carrozzo Alessandro Celestini Giacomo Piperno Pasqua D’Ambra Institute for Applied Computing (IAC) Via dei Taurini 19 Rome Institute for Applied Computing (IAC) Via P. Castellino 111 Naples

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Linear solvers are key components in any software platform for scientific and engineering computing. The solution of large and sparse linear systems lies at the core of physics-driven numerical simulations relying on partial differential equations (PDEs) and often represents a significant bottleneck in data-driven procedures, such as scientific machine learning. In this paper, we present an efficient implementation of the preconditioned s-step Conjugate Gradient (CG) method, originally proposed by Chronopoulos and Gear in 1989, for large clusters of Nvidia GPU-accelerated computing nodes. The method, often referred to as communication-reduced or communication-avoiding CG, reduces global synchronizations and data communication steps compared to the standard approach, enhancing strong and weak scalability on parallel computers. Our main contribution is the design of a parallel solver that fully exploits the aggregation of low-granularity operations inherent to the s-step CG method to leverage the high throughput of GPU accelerators. Additionally, it applies overlap between data communication and computation in the multi-GPU sparse matrix-vector product. Experiments on classic benchmark datasets, derived from the discretization of the Poisson PDE, demonstrate the potential of the method.

关键词： Linear systems Scalability Partial differential equations Machine learning Throughput Numerical simulation Software Data communication Sparse matrices Standards

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A synchronization-free incomplete LU factorization for GPUs with level-set analysis

A synchronization-free incomplete LU factorization for GPUs ...

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Euromicro conference on parallel, distributed and Network-Based Processing

作者： Manuel Freire Ernesto Dufrechou Pablo Ezzatti Instituto de Computación Facultad de Ingeniería - UDELAR Montevideo Uruguay

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Incomplete factorization methods are powerful algebraic preconditioners widely used to accelerate the convergence of linear solvers. The parallelization of ILU methods has been extensively studied, particularly for GPUs, which are ubiquitous parallel computing devices. In recent years, synchronization-free methods have become the mainstream approach for solving sparse triangular linear *** the sparse triangular solver and ILU factorization are closely related, the application of synchronization-free strategies to ILU factorization has not been explored in the literature to the same extent as the triangular solver. In this work, we present synchronization-free implementations of the ILU-0 preconditioner on GPUs. Specifically, we propose three implementations that vary in how row updates are handled after each coefficient elimination, as well as an additional approach that leverages a prior level-set analysis to optimize the execution schedule.

关键词： Linear systems Schedules parallel processing Synchronization Convergence

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Introduction to parallel and distributed Programming using N-Body Simulations 24

Introduction to Parallel and Distributed Programming using N...

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

作者： Alexander Van Craen Marcel Breyer Dirk Pflüger University of Stuttgart IPVS SC Stuttgart Germany

ISBN: (纸本)9798350355543

This paper describes how we use n-body simulations as an interesting and visually compelling way to teach efficient, parallel, and distributed programming. Our first course focuses on bachelor students introducing them to algorithmic complexities and their implications in real-world problems and state-of-the-art tools like Git, remote development, or C++, by simulating the collision of two galaxies. This project teaches the mapping of mathematical functions into code, efficient implementations, and the pitfalls around complexity and scaling. Our second course targets master students introducing them to intra- and inter-node parallelization. Here, the students simulate our solar system using OpenMP and MPI. The master students further deepen their knowledge of parallelization and scientific computing by choosing custom projects like simulating water molecules or implementing an interactive live visualization using *** courses are structured such that they can easily be adapted by other instructors. All our material is publicly available at https://***/orgs/SCTeaching-NBody.

关键词： education

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advances on P2P, parallel, Grid, Cloud and Internet computing: Proceedings of the 11th international conference 1

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丛书名： Lecture Notes on Data Engineering and Communication Technologies 1

2016年

作者： Fatos Xhafa, Leonard Barolli, Flora Amato (eds.)

ISBN: (纸本)3319491083;9783319491080

P2P, Grid, Cloud and Internet computing technologies have been very fast established as breakthrough paradigms for solving complex problems by enabling aggregation and sharing of an increasing variety of distributed computational resources at large scale. The aim of this volume is to provide latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to P2P, Grid, Cloud and Internet computing as well as to reveal synergies among such large scale computing paradigms. This proceedings volume presents the results of the 11th international conference on P2P, parallel, Grid, Cloud And Internet computing (3PGCIC-2016), held November 5-7, 2016, at Soonchunhyang University, Asan, Korea

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Development of a Collaborative Design Platform Based on Bim (Building Information Modeling) and Cloud computing

Development of a Collaborative Design Platform Based on Bim ...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE international conference on

作者： Zekun Chang Shandong Vocational College of Industry Zibo China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This study proposes a collaborative design platform based on BIM and cloud computing, combined with an innovative real-time synchronization algorithm, to solve the problems of delay, data consistency and synchronization conflict in cloud collaborative design. The proposed real-time synchronization algorithm is based on a distributed computing model. By introducing a distributed lock and timestamp mechanism, it effectively guarantees the data consistency and real-time performance in large-scale collaborative design, and can reduce data conflicts and improve collaborative efficiency when multiple design teams operate in parallel. In order to verify the effectiveness of the platform, this study conducted experiments in multiple architectural design projects, and the platform runs in mainstream cloud computing environments (such as AWS and Azure). The experimental results show that the collaborative design platform based on the innovative algorithm has significant advantages over traditional collaborative design tools in terms of synchronization delay, data transmission speed and number of concurrent users. When multiple design teams collaborate, the design cycle of the platform is shortened by 15 %, the overall work efficiency is improved by more than 20 %, and it can maintain stability and low response time in a high concurrent user environment. In addition, the platform's real-time rendering and version control mechanism also significantly improve the design quality and accuracy.

关键词： Cloud computing Visualization Computational modeling Buildings Collaboration distributed databases Real-time systems Delays Synchronization Security

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SMPPO: A Shared Memory Scheduling Approach for Real-Time Heterogeneous Serverless Edge Environment

SMPPO: A Shared Memory Scheduling Approach for Real-Time Het...

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international conference on Web Research (ICWR)

作者： Armin Mohammadi Ghaleh K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Serverless edge computing is transforming distributed systems, especially for IoT, by merging serverless architecture with edge computing. This evolution demands efficient scheduling methods, as traditional approaches struggle with dynamic and heterogeneous environments. Deep Reinforcement Learning (DRL), particularly the Proximal Policy Optimization (PPO) algorithm, offers a promising alternative. Our proposed method, SMPPO, introduces a global trajectory memory where agents learn from both their own and others' experiences. Simulations show SMPPO consistently outperforms others, improving total performance by 15% and response time by 25%. This robust, scalable approach advances scheduling in serverless edge computing.

关键词： Processor scheduling Simulation Merging Dynamic scheduling Deep reinforcement learning Real-time systems Trajectory Time factors Optimization Edge computing

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