Dynamic load balancing on GPU clusters for large-scale K-Means clustering

Kijsipongse, Ekasit; U-ruekolan, Suriya

doi:10.1109/jcsse.2012.6261977

Cited by 15 publications

(3 citation statements)

References 11 publications

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“…By keeping load balance on GPU clusters, the evaluation's findings demonstrate the parallelism K-Means' enhanced results. [10].…”

Section: Related Workmentioning

confidence: 99%

Unsupervised-based Distributed Machine Learning for Efficient Data Clustering and Prediction

Baligodugula,

Amsaad,

Schmidt

et al. 2024

Preprint

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Unsupervised ML-based approaches have emerged for driving critical decisions about training data samples to help solve challenges in many life critical applications. This paper proposes parallel and distributed computing unsupervised ML techniques to improve the execution time of different ML algorithms. Various unsupervised ML models are developed, implemented, and tested to demonstrate the efficiency, in terms of execution time and accuracy, of the serial methods as compared to the parallelized ones. We developed sequential, parallel, and distributed cloud computing unsupervised ML models based and determined the most efficient model through comparative analysis. As a case study, sequential, parallel, and distributed approaches of Simple K-Means, Minibatch K-means, and Fuzzy C-Means are investigated to study the developed models’ efficiency using country datasets for multiple organizations to train and test the developed model. Parallel and distributed computing models are developed utilizing could computing architect, i.e., cloud Amazon SageMaker, to study their efficiency in the execution time and model accuracy. The results show that the proposed parallel and distributed Fuzzy C-Means outperforms the other two clustering methods in terms of execution time with 0.932ms and 0.623ms with a minimal impact on the accuracy of the developed models.

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“…By keeping load balance on GPU clusters, the evaluation's findings demonstrate the parallelism K-Means' enhanced results. [10].…”

Section: Related Workmentioning

confidence: 99%

Unsupervised-based Distributed Machine Learning for Efficient Data Clustering and Prediction

Baligodugula,

Amsaad,

Schmidt

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…This iteration goes on until either no (or minimal) instance remains to be assigned or no (or minimal) centroid moves. After the training of iteration, once fed with an instance, the algorithm assign it to the nearest cluster [75]. Despite the simplicity and speed, the K-means technique is limited in terms of robustness, since the clusters are determined by the initial random assignments [76].…”

Section: Plos Onementioning

confidence: 99%

Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring

Zhang

Qiu

2020

PLoS ONE

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Neural networks are widely used in automatic credit scoring systems with high accuracy and outstanding efficiency. However, in the absence of prior knowledge, it is difficult to determine the set of hyper-parameters, which makes its application limited in practice. This paper presents a novel framework of credit-scoring model based on neural networks trained by the optimal swarm intelligence (SI) algorithm. This framework incorporates three procedures. Step 1, pre-processing, including imputation, normalization, and reordering of the samples. Step 2, training, where SI algorithms optimize hyper-parameters of back-propagation artificial neural networks (BP-ANN) with the area under curve (AUC) as the evaluation function. Step 3, test, applying the optimized model in Step 2 to predict new samples. The results show that the framework proposed in this paper searches the hyper-parameter space efficiently and finds the optimal set of hyper parameters with appropriate time complexity, which enhances the fitting and generalization ability of BP-ANN. Compared with existing credit-scoring models, the model in this paper predicts with a higher accuracy. Additionally, the model enjoys a greater robustness, for the difference of performance between training and testing phases.

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“…Li et al [7] use two different strategies for low-dimensional data sets and high-dimensional data sets respectively to make the best use of GPU computing horsepower. Kijsipongse [8] employed the dynamic load balancing to distribute workload equally on different GPUs installed in the clusters and improve the performance of the parallel K-Means at the inter-node level.…”

Section: Introductionmentioning

confidence: 99%

Cluster Center Initialization Parallel Algorithm for K-Means Algorithm

Yan

Zhang

et al. 2014

AMR

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K-Means algorithm is a one of the most famous unsupervised clustering algorithm. It has many disadvantages, such as sensitivity to the initial clustering centers and computes all the data points multiple times when facing the increasing data volume. In order to overcome the above limitations, this paper proposes to make use of density idea to find k cluster centers by adjusting the threshold. Finally, we design and implementation of the K-Means algorithm on the modern Graphic Processing Unit (GPU). The ratio of distance between classes to distance within classes and speedup are used as evaluation criteria. The experiments indicate that the proposed algorithm significantly improves the stability and efficiency of K-Means algorithm.

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Dynamic load balancing on GPU clusters for large-scale K-Means clustering

Cited by 15 publications

References 11 publications

Unsupervised-based Distributed Machine Learning for Efficient Data Clustering and Prediction

Unsupervised-based Distributed Machine Learning for Efficient Data Clustering and Prediction

Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring

Cluster Center Initialization Parallel Algorithm for K-Means Algorithm

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