A GPU-accelerated parallel K-means algorithm

Cuomo, Salvatore; Angelis, V. De; Farina, Gennaro; Marcellino, Livia; Toraldo, Gerardo

doi:10.1016/j.compeleceng.2017.12.002

Cited by 32 publications

(15 citation statements)

References 12 publications

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“…The speed-up varies from 4× to 386×, but also, in this case, it is not possible to perform a direct comparison since there are not enough details about the dataset composition. Finally, in [25], the K-means algorithm was developed on GPU with the Cartesian distance. They adopt a modern GPU with 1536 CUDA cores obtaining a maximum speed-up of 88×, which is very similar to our results.…”

Section: Comparisons and Discussionmentioning

confidence: 99%

“…Maximum Image Size Data Dimensionality Technology Speed-Up [18] 2,000,000 8 GPU NVIDIA GTX 280 220 [19] 500,000 2 GPU NVIDIA 9600 GT 14 [20] 1,000,000 2 GPU NVIDIA 8800 GTX 60 [21] 15,052,800 3 4 × GPU NVIDIA GTX 750Ti 60 [22] 1,000,000 32 GPU NVIDIA GTX 280 N. A. [23] 16,777,216 3 GPU NVIDIA Tesla C2050 25 [24] 245,057 4 GPU NVIDIA GeForce 210 386 [25] 500,000 16 GPU NVIDIA Quadro K5000 88 [26] N. A. N. A. GPU NVIDIA GTX 1080 18.5 [27] 20,000 10 2 × AMD Opteron quad-core 8 [27] 65,536 10 GPU NVIDIA Tesla 2050 60 [27] 17,692 9 Mitrion MVP FPGA Simulator N. A. Our work 264,408 128 GPU NVIDIA GTX 1060 126…”

Section: Papermentioning

confidence: 99%

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Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images

et al. 2018

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The precise delineation of brain cancer is a crucial task during surgery. There are several techniques employed during surgical procedures to guide neurosurgeons in the tumor resection. However, hyperspectral imaging (HSI) is a promising non-invasive and non-ionizing imaging technique that could improve and complement the currently used methods. The HypErspectraL Imaging Cancer Detection (HELICoiD) European project has addressed the development of a methodology for tumor tissue detection and delineation exploiting HSI techniques. In this approach, the K-means algorithm emerged in the delimitation of tumor borders, which is of crucial importance. The main drawback is the computational complexity of this algorithm. This paper describes the development of the K-means clustering algorithm on different parallel architectures, in order to provide real-time processing during surgical procedures. This algorithm will generate an unsupervised segmentation map that, combined with a supervised classification map, will offer guidance to the neurosurgeon during the tumor resection task. We present parallel K-means clustering based on OpenMP, CUDA and OpenCL paradigms. These algorithms have been validated through an in-vivo hyperspectral human brain image database. Experimental results show that the CUDA version can achieve a speed-up of ~ 150 × with respect to a sequential processing. The remarkable result obtained in this paper makes possible the development of a real-time classification system.

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Section: Comparisons and Discussionmentioning

confidence: 99%

Section: Papermentioning

confidence: 99%

Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images

et al. 2018

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“…In connection with this, there is a growing need to parallelise big data clustering while preserving the main clustering structure and reducing computational costs. So, k‐means based parallel algorithms are used to cluster data in various applications [20, 25, 26].…”

Section: Related Workmentioning

confidence: 99%

Efficient algorithm for big data clustering on single machine

Alguliyev

Alıguliyev

Sukhostat

2020

CAAI trans. intell. technol.

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Big data analysis requires the presence of large computing powers, which is not always feasible. And so, it became necessary to develop new clustering algorithms capable of such data processing. This study proposes a new parallel clustering algorithm based on the k‐means algorithm. It significantly reduces the exponential growth of computations. The proposed algorithm splits a dataset into batches while preserving the characteristics of the initial dataset and increasing the clustering speed. The idea is to define cluster centroids, which are also clustered, for each batch. According to the obtained centroids, the data points belong to the cluster with the nearest centroid. Real large datasets are used to conduct the experiments to evaluate the effectiveness of the proposed approach. The proposed approach is compared with k‐means and its modification. The experiments show that the proposed algorithm is a promising tool for clustering large datasets in comparison with the k‐means algorithm.

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“…The procedure of data classification is simple and easy for clustering [23,44,45]. K-Means is an algorithm leading to a local optimum satisfying the following conditions [46,47] It is obvious that in K-Means, we need all the training sequence for every iteration and the quantizer is not available as long as the procedure is not completed. Thus, the data must be part of the training sequence, and the groups change with each iteration.…”

Section: B K-means Algorithmmentioning

confidence: 99%

A Machine Learning Algorithm for Biomedical Images Compression Using Orthogonal Transforms

Kouanou¹,

Tchiotsop²,

Tchinda³

et al. 2018

IJIGSP

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Compression methods are increasingly used for medical images for efficient transmission and reduction of storage space. In this work, we proposed a compression scheme for colored biomedical image based on vector quantization and orthogonal transforms. The vector quantization relies on machine learning algorithm (K-Means and Splitting Method). Discrete Walsh Transform (DWaT) and Discrete Chebyshev Transform (DChT) are two orthogonal transforms considered. In a first step, the image is decomposed into sub-blocks, on each sub-block we applied the orthogonal transforms. Machine learning algorithm is used to calculate the centers of clusters and generates the codebook that is used for vector quantization on the transformed image. Huffman encoding is applied to the index resulting from the vector quantization. Parameters Such as Mean Square Error (MSE), Mean Average Error (MAE), PSNR (Peak Signal to Noise Ratio), compression ratio, compression and decompression time are analyzed. We observed that the proposed method achieves excellent performance in image quality with a reduction in storage space. Using the proposed method, we obtained a compression ratio greater than 99.50 percent. For some codebook size, we obtained a MSE and MAE equal to zero. A comparison between DWaT, DChT method and existing literature method is performed. The proposed method is really appropriate for biomedical images which cannot tolerate distortions of the reconstructed image because the slightest information on the image is important for diagnosis.

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A GPU-accelerated parallel K-means algorithm

Cited by 32 publications

References 12 publications

Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images

Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images

Efficient algorithm for big data clustering on single machine

A Machine Learning Algorithm for Biomedical Images Compression Using Orthogonal Transforms

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