K-Means clustering is a popular unsupervised machine learning method which has been used in diverse applications including image processing, information retrieval, social sciences and weather forecasting. However, clustering is computationally expensive especially when applied to large datasets. In this paper, we explore accelerating the performance of K-means clustering using three approaches: 1) shared memory using OpenMP, 2) distributed memory with message passing (MPI), and 3) heterogeneous computing with NVIDIA Graphics Processing Units (GPUs) programmed with CUDA-C. While others have looked at accelerating K-means clustering, this is the first study that compares these different approaches. In addition, K-means performance is very sensitive to the initial means chosen. We evaluate different initializations in parallel and choose the best one to use for the entire algorithm. We evaluate results on a range of images from small (300×300 pixels) to large (1164×1200 pixel). Our results show that all three parallel programming approaches give speed-up, with the best results obtained by OpenMP for smaller images and CUDA-C for larger ones. Each of these approaches gives approximately thirty times overall speed-up compared to a sequential implementation of K-means. In addition, our parallel initialization gives an additional 1.5 to 2.5 times speed-up over the accelerated parallel versions.

Accelerating K-Means clustering with parallel implementations and GPU computing

Fuzzy C-mean (FCM) is an algorithm for data segmentation and classification, robust and very popular within the scientific community. It is used in several fields such as computer vision, medical imaging and remote control. The purpose of this paper is to propose a parallel implementation of the iterative type-2 fuzzy C-mean (IT2FCM) algorithm on a massively parallel SIMD architecture to segment different MRI images. IT2FCM is an FCM standard variant; its objective is identical to that of FCM, except that the first has a higher accuracy level than the second. However, it is expensive in terms of time processing. Therefore, it is practically important to reduce its execution time while preserving the quality of the segmentation. This implementation is then compared with the sequential versions in the C language and Python using the Numpy and Numba libraries, and then, we compared it to another parallel method from the literature. The execution time obtained is faster than the sequential versions by about 15 × and 4 × for the second parallel version. The results achieved are very satisfactory compared to those taken from the literature.

The performances of iterative type-2 fuzzy C-mean on GPU for image segmentation

https://www.techscience.com/csse/v34n5/40048/pdf

A Survey and Systematic Categorization of Parallel K-means and Fuzzy-c-Means Algorithms

To address the challenging problem of vector quantization (VQ) for high dimensional vector using large coding bits, this work proposes a novel deep neural network (DNN) based VQ method. This method uses a k-means based vector quantizer as an encoder and a DNN as a decoder. The decoder is initialized by the decoder network of deep auto-encoder, fed with the codes provided by the k-means based vector quantizer, and trained to minimize the coding error of VQ system. Experiments on speech spectrogram coding demonstrate that, compared with the k-means based method and a recently introduced DNN-based method, the proposed method significantly reduces the coding error. Furthermore, in the experiments of coding multi-frame speech spectrogram, the proposed method achieves about 11% relative gain over the k-means based method in terms of segmental signal to noise ratio (SegSNR).

An improved vector quantization method using deep neural network

Spectral clustering is one of the most popular graph clustering algorithms, which achieves the best performance for many scientific and engineering applications. However, existing implementations in commonly used software platforms such as Matlab and Python do not scale well for many of the emerging Big Data applications. In this paper, we present a fast implementation of the spectral clustering algorithm on a CPU-GPU heterogeneous platform. Our implementation takes advantage of the computational power of the multi-core CPU and the massive multithreading and SIMD capabilities of GPUs. Given the input as data points in high dimensional space, we propose a parallel scheme to build a sparse similarity graph represented in a standard sparse representation format. Then we compute the smallest k eigenvectors of the Laplacian matrix by utilizing the reverse communication interfaces of ARPACK software and cuSPARSE library, where k is typically very large. Moreover, we implement a very fast parallelized k-means algorithm on GPUs. Our implementation is shown to be significantly faster compared to the best known Matlab and Python implementations for each step. In addition, our algorithm scales to problems with a very large number of clusters.

A High Performance Implementation of Spectral Clustering on CPU-GPU Platforms

The $k$-means algorithm is widely used for unsupervised clustering. This paper describes an efficient CUDA-based $k$-means algorithm. Different from existing GPU-based k-means algorithms, our algorithm achieves better efficiency by utilizing the triangle inequality. Our algorithm explores the trade-off between load balance and memory access coalescing through data layout management. Because the effectiveness of the triangle inequity depends on the input data, we further propose a hybrid algorithm that adaptively determines whether to apply the triangle inequality. The efficiency of our algorithm is validated through extensive experiments, which demonstrate improved performance over existing CPU-based and CUDA-based k-means algorithms, in terms of both speed and scalability.

An Efficient k-Means Algorithm on CUDA

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An Efficient k-Means Algorithm on CUDA