A Parallel Approach of the Enhanced Craig–Bampton Method

The enhanced Craig–Bampton (ECB) method is a novel extension of the original Craig–Bampton (CB) method, which has been widely used for component mode synthesis (CMS). The ECB method, using residual modal compensation that is neglected in the CB method, provides dramatic accuracy improvement of reduced matrices without an increasing number of eigenbasis. However, it also needs additional computational requirements to treat the residual flexibility. In this paper, an efficient parallelization of the ECB method is presented to handle this issue and accelerate the applicability for large-scale structural vibration problems. A new ECB formulation within a substructuring strategy is derived to achieve better scalability. The parallel implementation is based on OpenMP parallel architecture. METIS graph partitioning and Linear Algebra Package (LAPACK) are used to automated algebraic partitioning and computational linear algebra, respectively. Numerical examples are presented to evaluate the accuracy, scalability, and capability of the proposed parallel ECB method. Consequently, based on this work, one can expect effective computation of the ECB method as well as accuracy improvement.

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Reliability Estimation of Large-Scale Structural Vibration Problems

49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference <br> 16th AIAA/ASME/AHS Adaptive Structures Conference<br> 10t ◽

10.2514/6.2008-1991 ◽

2008 ◽

Author(s):

Zissimos Mourelatos ◽

Efstratios Nikolaidis ◽

Jared Song

Keyword(s):

Large Scale ◽

Reliability Estimation ◽

Structural Vibration ◽

Vibration Problems

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An Efficient Parallelization Strategy For The Adaptive Integral Method Based On Graph Partitioning

2020 14th European Conference on Antennas and Propagation (EuCAP) ◽

10.23919/eucap48036.2020.9135887 ◽

2020 ◽

Cited By ~ 1

Author(s):

Damian Marek ◽

Shashwat Sharma ◽

Piero Triverio

Keyword(s):

Graph Partitioning ◽

Integral Method ◽

Parallelization Strategy ◽

Efficient Parallelization

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Research in Numerical Linear Algebra and Numerical Methods for Large-Scale Least Squares and Other Problems.

10.21236/ada131400 ◽

1983 ◽

Author(s):

Robert J. Plemmons

Keyword(s):

Numerical Methods ◽

Least Squares ◽

Linear Algebra ◽

Large Scale ◽

Numerical Linear Algebra

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Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

Big Data and Cognitive Computing ◽

10.3390/bdcc5010014 ◽

2021 ◽

Vol 5 (1) ◽

pp. 14

Author(s):

Christos Makris ◽

Georgios Pispirigos

Keyword(s):

Social Networks ◽

Graph Partitioning ◽

Large Scale ◽

Real Life ◽

Information Networks ◽

Digital Marketing ◽

Partitioning Problems ◽

Iterative Solutions ◽

Community Extraction ◽

Stability And Accuracy

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

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A Parallel Unmixing-Based Content Retrieval System for Distributed Hyperspectral Imagery Repository on Cloud Computing Platforms

Remote Sensing ◽

10.3390/rs13020176 ◽

2021 ◽

Vol 13 (2) ◽

pp. 176

Author(s):

Peng Zheng ◽

Zebin Wu ◽

Jin Sun ◽

Yi Zhang ◽

Yaoqin Zhu ◽

...

Keyword(s):

Cloud Computing ◽

Large Scale ◽

Retrieval System ◽

Hyperspectral Image ◽

Parallel Implementation ◽

Remotely Sensed Data ◽

Web Interfaces ◽

Content Retrieval ◽

Service Mode ◽

Computing Platforms

As the volume of remotely sensed data grows significantly, content-based image retrieval (CBIR) becomes increasingly important, especially for cloud computing platforms that facilitate processing and storing big data in a parallel and distributed way. This paper proposes a novel parallel CBIR system for hyperspectral image (HSI) repository on cloud computing platforms under the guide of unmixed spectral information, i.e., endmembers and their associated fractional abundances, to retrieve hyperspectral scenes. However, existing unmixing methods would suffer extremely high computational burden when extracting meta-data from large-scale HSI data. To address this limitation, we implement a distributed and parallel unmixing method that operates on cloud computing platforms in parallel for accelerating the unmixing processing flow. In addition, we implement a global standard distributed HSI repository equipped with a large spectral library in a software-as-a-service mode, providing users with HSI storage, management, and retrieval services through web interfaces. Furthermore, the parallel implementation of unmixing processing is incorporated into the CBIR system to establish the parallel unmixing-based content retrieval system. The performance of our proposed parallel CBIR system was verified in terms of both unmixing efficiency and accuracy.

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Parallel Framework for Dimensionality Reduction of Large-Scale Datasets

Scientific Programming ◽

10.1155/2015/180214 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Sai Kiranmayee Samudrala ◽

Jaroslaw Zola ◽

Srinivas Aluru ◽

Baskar Ganapathysubramanian

Keyword(s):

Dimensionality Reduction ◽

Organic Solar Cells ◽

Large Scale ◽

Parallel Implementation ◽

High Dimensional Data ◽

Real Life ◽

Processing Parameters ◽

High Dimensional ◽

Morphology Evolution ◽

Reduction Techniques

Dimensionality reduction refers to a set of mathematical techniques used to reduce complexity of the original high-dimensional data, while preserving its selected properties. Improvements in simulation strategies and experimental data collection methods are resulting in a deluge of heterogeneous and high-dimensional data, which often makes dimensionality reduction the only viable way to gain qualitative and quantitative understanding of the data. However, existing dimensionality reduction software often does not scale to datasets arising in real-life applications, which may consist of thousands of points with millions of dimensions. In this paper, we propose a parallel framework for dimensionality reduction of large-scale data. We identify key components underlying the spectral dimensionality reduction techniques, and propose their efficient parallel implementation. We show that the resulting framework can be used to process datasets consisting of millions of points when executed on a 16,000-core cluster, which is beyond the reach of currently available methods. To further demonstrate applicability of our framework we perform dimensionality reduction of 75,000 images representing morphology evolution during manufacturing of organic solar cells in order to identify how processing parameters affect morphology evolution.

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A heterogeneous parallel implementation of the Markov clustering algorithm for large-scale biological networks on distributed CPU–GPU clusters

The Journal of Supercomputing ◽

10.1007/s11227-021-04204-6 ◽

2022 ◽

Author(s):

You Fu ◽

Wei Zhou

Keyword(s):

Biological Networks ◽

Large Scale ◽

Clustering Algorithm ◽

Parallel Implementation ◽

Gpu Clusters ◽

Markov Clustering

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Local Graph Edge Partitioning

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3466685 ◽

2021 ◽

Vol 12 (5) ◽

pp. 1-25

Author(s):

Shengwei Ji ◽

Chenyang Bu ◽

Lei Li ◽

Xindong Wu

Keyword(s):

Real World ◽

Graph Partitioning ◽

Large Scale ◽

Complete Information ◽

Local Information ◽

Experimental Results ◽

Two Stage ◽

Graph Computation ◽

Local Graph ◽

Edge Partitioning

Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models can be classified into two categories: offline and streaming graph partitioning models. The former requires global graph information during the partitioning, which is expensive in terms of time and memory for large-scale graphs. The latter creates partitions based solely on the received graph information. However, the streaming model may result in a lower partitioning quality compared with the offline model. Therefore, this study introduces a Local Graph Edge Partitioning model, which considers only the local information (i.e., a portion of a graph instead of the entire graph) during the partitioning. Considering only the local graph information is meaningful because acquiring complete information for large-scale graphs is expensive. Based on the Local Graph Edge Partitioning model, two local graph edge partitioning algorithms—Two-stage Local Partitioning and Adaptive Local Partitioning—are given. Experimental results obtained on 14 real-world graphs demonstrate that the proposed algorithms outperform rival algorithms in most tested cases. Furthermore, the proposed algorithms are proven to significantly improve the efficiency of the real graph computation system GraphX.

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