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检索条件"任意字段=International Symposium on Databases in Parallel and Distributed Systems"

共 9684 条记录，以下是481-490 订阅

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STZIP-GNN: A Robust Model for Taxi Demand Prediction in Sparse Urban Environments

STZIP-GNN: A Robust Model for Taxi Demand Prediction in Spar...

引用

international Conference on parallel and distributed systems (ICPADS)

作者： Yifei Shen Jiaxing Shen School of Data Science Lingnan University Tuen Mun Hong Kong China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Accurate prediction of taxi demand is crucial for optimizing urban transportation systems, improving passenger experiences, and ensuring efficient resource allocation. However, few studies pay close attention to the issue of data sparsity, particularly in high temporal-spatial resolution data, which presents a significant challenge due to the large number of zeros that can affect model prediction performance. Traditional methods predominantly rely on historical order data for predictions, often failing to capture dynamic and contextual information effectively. To address this problem, we propose a spatiotemporal Zero-Inflated Poisson Graph Neural Network (STZIP-GNN) to enhance prediction performance. Our approach leverages the Zero-Inflated Poisson (ZIP) distribution to effectively capture the large number of zeros in sparse data and incorporates additional richer data sources, such as crowdsensing geolocation data, to mitigate the impact of data sparsity on the model. By utilizing the representational power of spatiotemporal graph neural networks, our model fits the parameters of the probability distribution, enhancing prediction performance. Experimental results demonstrate that our model outperforms other baseline models and validates its effectiveness on real-world datasets.

关键词： Soft sensors Urban areas distributed databases Predictive models Data models Graph neural networks Spatiotemporal phenomena Resource management Reliability Public transportation

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A PORTABLE APPROACH TO INTEGRATING DIVERSE GEO-SCIENCE DATA USING STARE-AWARE databases AND TRANSITIONING TO CLOUD

A PORTABLE APPROACH TO INTEGRATING DIVERSE GEO-SCIENCE DATA ...

引用

IEEE international Geoscience and Remote Sensing symposium (IGARSS)

作者： Rilee, Michael L. Kuo, Kwo-Sen Griessbaum, Niklas Frew, James Gallagher, James NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA Rilee Syst Technol LLC Derwood MD USA Bayesics LLC Bowie MD USA Univ Calif Santa Barbara Santa Barbara CA USA OPeNDAP Inc Narragansett RI USA

ISBN: (纸本)9781665403696

Big Data technologies such as Cloud and parallel distributed computing and storage are necessary to treat Earth Science data volume. Yet the great diversity of Earth Science data renders it nearly impossible to organize that data on scalable platforms without costly data movement or undesired interpolation that straitjackets scientific research. The SpatioTemporal Adaptive Resolution Encoding (STARE) is an alternative geolocation and indexing scheme for harmonizing data for integrative analysis on scalable systems. STARE uses a hierarchical, recursive partitioning of space and time in which the index or coordinates of each node are integers from the same index space, usually allowing quick comparison without floating-point calculation. STARE is well suited to provide a unifying geo-semantics for arranging data in databases. In this work, we outline the technical principles underlying STARE and its application to SQLite as an example. The STARELite STARE-aware lightweight geo-database can be used to catalogue diverse data for geographical querying and integration on local resources and Cloud.

关键词： STARE SQLite Geodatabase Cloud

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Practice and Experience in using parallel and Scalable Machine Learning with Heterogenous Modular Supercomputing Architectures

Practice and Experience in using Parallel and Scalable Machi...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Riedel, Morris Sedona, Rocco Barakat, Chadi Einarsson, Petur Hassanian, Reza Cavallaro, Gabriele Book, Matthias Neukirchen, Helmut Lintermann, Andreas Univ Iceland Dept Comp Sci Reykjavik Iceland Forschungszentrum Julich Julich Supercomp Ctr Julich Germany

ISBN: (纸本)9781665435772

We observe a continuously increased use of Deep Learning (DL) as a specific type of Machine Learning (ML) for data-intensive problems (i.e., 'big data') that requires powerful computing resources with equally increasing performance. Consequently, innovative heterogeneous High-Performance Computing (HPC) systems based on multi-core CPUs and many-core GPUs require an architectural design that addresses end user communities' requirements that take advantage of ML and DL. Still the workloads of end user communities of the simulation sciences (e.g., using numerical methods based on known physical laws) needs to be equally supported in those architectures. This paper offers insights into the Modular Supercomputer Architecture (MSA) developed in the Dynamic Exascale Entry Platform (DEEP) series of projects to address the requirements of both simulation sciences and data-intensive sciences such as High Performance Data Analytics (HPDA). It shares insights into implementing the MSA in the Julich Supercomputing Centre (JSC) hosting Europe No. 1 Supercomputer Julich Wizard for European Leadership Science (JUWELS). We augment the technical findings with experience and lessons learned from two application communities case studies (i.e., remote sensing and health sciences) using the MSA with JUWELS and the DEEP systems in practice. Thus, the paper provides details into specific MSA design elements that enable significant performance improvements of ML and DL algorithms. While this paper focuses on MSA-based HPC systems and application experience, we are not losing sight of advances in Cloud Computing (CC) and Quantum Computing (QC) relevant for ML and DL.

关键词： High performance computing cloud computing quantum computing machine learning deep learning parallel and distributed algorithms remote sensing health sciences modular supercomputer architecture

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Optimizing MEC System Performance Through M/M/m Queueing Model Analysis

Optimizing MEC System Performance Through M/M/m Queueing Mod...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Jiale Liang Xiaowei Ma Houjian Ding Zhenli He School of Software Yunnan University Kunming China Hongta Tobacco (Group) Co. Ltd China Yunnan Key Laboratory of Software Engineering Yunnan University Kunming China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

This paper investigates the performance of Mobile Edge Computing (MEC) systems using the M/M/m queuing model, focusing on how server configurations and queuing rules affect task response times on edge servers. The study considers two types of tasks with different processing requirements: special tasks and general tasks. The study analyzes the response time under two queuing rules: first-come-first-served (FCFS) and head-of-line (HOL) priority. By evaluating the impact of server execution speed and the number of cores, this work provides insights for optimizing task scheduling and resource management in MEC environments, which ultimately improves system performance and reduces latency.

关键词： Analytical models distributed processing Multi-access edge computing System performance Computational modeling Focusing Servers Time factors Resource management Queueing analysis

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ESSA 2022 Invited Speaker: The Curious Incident of the Data in the Scientific Workflow

ESSA 2022 Invited Speaker: The Curious Incident of the Data ...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Lavanya Ramakrishnan Lawrence Berkeley National Laboratory

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

The volume, veracity, and velocity of data generated by the accelerators, colliders, supercomputers, light sources and neutron sources have grown exponentially in the last decade. Data has fundamentally changed the scientific workflow running on high performance computing (HPC) systems. It is necessary that we develop appropriate capabilities and tools to understand, analyze, preserve, share, and make optimal use of data. Intertwined with data are complex human processes, policies and decisions that need to be accounted for when building software tools. In this talk, I will outline our work addressing data lifecycle challenges on HPC systems including effective use of storage hierarchy, managing complex scientific data processing, and enabling search on large-scale scientific data.

关键词： Software tools distributed processing distributed databases Conferences Buildings Supercomputers Neutrons

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GCSM: GPU-Accelerated Continuous Subgraph Matching for Large Graphs

GCSM: GPU-Accelerated Continuous Subgraph Matching for Large...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Yihua Wei Peng Jiang University of Iowa

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Continuous subgraph matching (CSM) is a key building block in many graph mining applications. Previous research has primarily focused on CSM algorithms on CPU. However, due to the dynamic nature of input graphs and the data movement bottlenecks, it has been challenging to efficiently run CSM on heterogeneous systems with GPUs. This work proposes the first system that exploits GPU to accelerate CSM. The main idea of our system is to cache the frequently accessed graph data in GPU memory so that most of the data communication between CPU and GPU can be avoided during the matching process. To identify the frequent data, we propose an efficient frequency estimation technique based on random walks on the input graph. We also provide an end-to-end design that achieves efficient dynamic graph updates on the CPU and efficient incremental matching on the GPU. The experiments show that our system significantly improves the performance of CSM compared to existing CPU solutions and supports CSM on extremely large graphs.

关键词： distributed processing Heuristic algorithms Graphics processing units Frequency estimation Data mining Data communication

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Proposal for a Flexible Benchmark for Agent Based Models

Proposal for a Flexible Benchmark for Agent Based Models

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Elizabeth Koning William Gropp Department of Computer Science University of Illinois Urbana Champaign Urbana IL USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

As hardware capabilities and scientific understanding of social and biological systems improves, researchers are able to model complex system with increasing detail. This change increases the demand on Agent Based Modeling (ABM) software, making the performance of the software more critical. For researchers to choose the best system to implement their models, they must understand not only the capabilities of the platforms, which is available in the documentation, but also the relative performance to other software options. Currently, there is a dearth of information on the performance of ABM platforms and no standardized way to compare the performance of different platforms. To address this gap, the proposed NothingModel benchmark is a flexible benchmark to reflect a wide variety of ABMs and allow the developers of models and ABM platforms to compare platforms under a range of conditions. Set up as a series of building blocks, it allows the users to vary the scale, memory use, communication, computation, and heterogeneity of a sample model, implemented with multiple tools. Along with the written description of the model, we include a reference implementation in NetLogo to demonstrate the specifics and provide comparison to other tools.

关键词： distributed processing Computational modeling Biological system modeling Conferences Documentation Benchmark testing Software

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Pose Convolutional Routing Towards Lightweight Capsule Networks

Pose Convolutional Routing Towards Lightweight Capsule Netwo...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Chengxin Lv Guangqi Jiang Shoukun Xu Yi Liu Academy of Computing and Artificial Intelligence Changzhou University Changzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

An important branch of artificial intelligence systems and architectures is Capsule Networks (CapsNets) have been known extremely large amount of parameters and computation because of the complex capsule routing algorithm, making it difficult deep architectures in the era of deep learning. To address this challenge, in this paper, we propose a simple yet effective capsule routing algorithm. Specifically, we activate the pose of the entity using its activation probability. On top of that, a convolution on the activated pose matrix to learn the high-level capsules’ pose matrices. Activations of the high-level capsules can be digged from their pose matrices via convolution and activation. Such mechanism generates fewer network parameters and lightweight computation, which make it practitable a deep CapsNets architecture. Experiments on CIFAR-10/100, Small-NORB, MINIST and even large-scale benchmark PASCAL VOC 2007, demonstrate the effectiveness of the proposed method.

关键词： Deep learning Technological innovation distributed processing Convolution Computer architecture Object detection Benchmark testing Routing Artificial intelligence

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Adaptable Machine Learning Methods for a Twitter Followee Recommendation System

Adaptable Machine Learning Methods for a Twitter Followee Re...

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IoT, Communication and Automation Technology (ICICAT), international Conference on

作者： Sandeep Yadav Shashank Srivastav Computer Science and Engineering Department Buddha Institute of Technology Gorakhpur India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Recommender systems for social networking have recently used machine learning (ML) methods. This paper describes a multi-agent system that simulates a Twitter recommender system to give users a list of helpful suggestions, i.e., a list of users who are followed, or they want to follow. When processing data in parallel on multi-node distributed systems, a machine learning technique (such as a neural network or multilayer perceptron) is evaluated for scalability using a simulator. A multi-agent modelling simulates the dispersed environment. The quantity of nodes, the method used within the model recommender framework, and the real followers and followees data are the first parameters that need to be configured on the simulator. The experimental findings were acquired on three different datasets to evaluate the speed and precision of a simulated recommender system when evaluating the machine learning method under various conditions.

关键词： Social networking (online) Scalability Blogs Neural networks distributed databases Machine learning Multilayer perceptrons Data models Recommender systems Multi-agent systems

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DeepThermo: Deep Learning Accelerated parallel Monte Carlo Sampling for Thermodynamics Evaluation of High Entropy Alloys

DeepThermo: Deep Learning Accelerated Parallel Monte Carlo S...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Junqi Yin Feiyi Wang Mallikarjun Arjun Shankar Oak Ridge National Laboratory Oak Ridge USA

Since the introduction of Metropolis Monte Carlo (MC) sampling, it and its variants have become standard tools used for thermodynamics evaluations of physical systems. However, a long-standing problem that hinders the effectiveness and efficiency of MC sampling is the lack of a generic method (a.k.a. MC proposal) to update the system configurations. Consequently, current practices are not scalable. Here we propose a parallel MC sampling framework for thermodynamics evaluation—DeepThermo. By using deep learning–based MC proposals that can globally update the system configurations, we show that DeepThermo can effectively evaluate the phase transition behaviors of high entropy alloys, which have an astronomical configuration space. For the first time, we directly evaluate a density of states expanding over a range of ~e 10,000 for a real material. We also demonstrate DeepThermo’s performance and scalability up to 3,000 GPUs on both NVIDIA V100 and AMD MI250X-based supercomputers.

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