Efficient k-means on GPUs

Lutz, Clemens; Breß, Sebastian; Rabl, Tilmann; Zeuch, Steffen; Markl, Volker

doi:10.1145/3211922.3211925

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Lutz et al [26] proposed a parallel K-means implementation using an NVIDIA GTX 1080 GPU. They perfomed only experiments producing four groups and no further details are given in the paper about the dataset.…”

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%