A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality

Kwedlo, Wojciech; Czochanski, Pawel J.

doi:10.1109/access.2019.2907885

Cited by 32 publications

(19 citation statements)

References 45 publications

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“…We used a version of these last two algorithms parallelized using OpenMP threads. The code implementing the Annulus and Exponion algorithms is a simplification of a hybrid MPI/OpenMP code [44] that does not execute any MPI calls. The last variant is an implementation of Lloyd's method for NVIDIA graphics GPUs from an open source KMCUDA package 1 .…”

Section: Resultsmentioning

confidence: 99%

“…Regarding accelerated algorithms, shared-memory parallelization of the triangle inequality-based methods has been described by Hamerly and Drake [28], Ding et al [32], and Newling and Fleuret [31]. Kwedlo and Czochanski [44] proposed a hybrid MPI/OpenMP parallelization of some triangle inequality-based methods for clusters of multiprocessors. As for kd-tree-based methods, a version for distributed systems, parallelized using MPI bindings for Java, was described by Pettinger and Di Fatta [45].…”

Section: Related Researchmentioning

confidence: 99%

“…The pseudo-code of Lloyd's algorithm [28], [44] is shown in Figure 1. The assignment step (lines 3-4) allocates each feature vector to the cluster represented by the nearest centroid; d is a distance (for the Euclidean distance d E , its square d 2 E may be taken to avoid computing costly square roots).…”

Section: Lloyd's Algorithm and Its Parallelizationmentioning

confidence: 99%

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Accelerated K-Means Algorithms for Low-Dimensional Data on Parallel Shared-Memory Systems

Kwedlo

Łubowicz

2021

IEEE Access

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This paper considers the problem of exact accelerated algorithms for the K-means clustering of low-dimensional data on modern multi-core systems. A version of the filtering algorithm parallelized using the OpenMP (Open Multi-Processing) standard is proposed. The algorithm employs a kd-tree structure to skip some unnecessary calculations between cluster centroids and feature vectors. In our approach, both the kd-tree construction and the iterations of the K-means are parallelized using the OpenMP tasking mechanism. A new task is created for a recursive call performed during kd-tree construction and traversal. The tasks are executed in parallel by the cores of a shared-memory system. In computational experiments, we evaluated the parallel efficiency of our approach and compared its performance to the parallel Lloyd's method, a GPU (Graphics Processing Unit) formulation of the K-means algorithm, and two parallel triangle inequality-based algorithms intended for low-dimensional data. The evaluation was performed on six synthetic datasets from two distributions and seven real-life datasets. The experiments, executed on a 24core system, indicated that our version of the filtering algorithm had satisfactory or high parallel efficiency. Its runtime was much shorter than those of competing algorithms. However, the advantage of the parallel filtering algorithm decreased rapidly as the dimension of data increased.INDEX TERMS acceleration of K-means, K-means clustering, kd-trees, OpenMP tasks, parallelization

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Section: Resultsmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

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Accelerated K-Means Algorithms for Low-Dimensional Data on Parallel Shared-Memory Systems

Kwedlo

Łubowicz

2021

IEEE Access

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“…[29] Computational complexity Multiple kernel k-means with late fusion. [30] Image processing A hybrid parallelization of k-means algorithm.…”

Section: Referencementioning

confidence: 99%

The k-means Algorithm: A Comprehensive Survey and Performance Evaluation

2020

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The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions.

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“…W. Kwedlo et al [19] proposed four approaches namely Drake's, Elkan's, Annulus, and Yinyang algorithms to minimize the unnecessary distance calculation in K-means clustering algorithm. In this paper a hybrid MPI/ OpenMP programming models are used to parallelize these algorithms.…”

Section: Related Workmentioning

confidence: 99%

An Application based Efficient Thread Level Parallelism Scheme on Heterogeneous Multicore Embedded System for Real Time Image Processing.

Indragandhi

Jawahar

2020

SCPE

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The recent advent of the embedded devices is equipped with multicore processor as it significantly improves the system performance. In order to utilize all the core in multicore processor in an efficient manner, application programs need to be parallelized. An efficient thread level parallelism (ETLP) scheme is proposed in this paper and uses computationally intensive edge detection algorithm for evaluation. Edge detection is the important process in various real time applications namely vehicle detection in traffic control, medical image processing etc. The main objective of ETLP scheme is to reduce the execution time and increase the CPU core utilization. The performance of ETLP scheme is evaluated with basic edge detection scheme (BEDS) for different image size. The experimental results reveal that the proposed ETLP scheme achieves efficiency of 49% and 72% for the image size 300 × 256 and 1024 × 1024 respectively. Furthermore an ETLP scheme reducing 66% execution time for image size 1024 × 1024 when compared with BEDS.

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A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality

Cited by 32 publications

References 45 publications

Accelerated K-Means Algorithms for Low-Dimensional Data on Parallel Shared-Memory Systems

Accelerated K-Means Algorithms for Low-Dimensional Data on Parallel Shared-Memory Systems

The k-means Algorithm: A Comprehensive Survey and Performance Evaluation

An Application based Efficient Thread Level Parallelism Scheme on Heterogeneous Multicore Embedded System for Real Time Image Processing.

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