Parallel graph component labelling with GPUs and CUDA

Hawick, K. A.; Leist, A.; Playne, Daniel P.

doi:10.1016/j.parco.2010.07.002

Cited by 146 publications

(111 citation statements)

References 35 publications

Supporting

Mentioning

106

Contrasting

Unclassified

Order By: Relevance

“…Two cells are said to be part of the same cluster if they are first neighbors. The Directional Propagation Labelling algorithm (DPL) for GPUs [22] was employed to determine if there was a percolating cluster connecting the lower and upper borders of the cube. It was necessary to implement this algorithm for GPUs because the systems were very large ((800×800×800) cells for L = 64) and the procedure becomes very time demanding.…”

Section: Powders and Grains 2017mentioning

confidence: 99%

Percolation study for the capillary ascent of a liquid through a granular soil

Cárdenas-Barrantes¹,

Muñoz²,

Araújo³

2017

EPJ Web Conf.

View full text Add to dashboard Cite

Abstract. Capillary rise plays a crucial role in the construction of road embankments in flood zones, where hydrophobic compounds are added to the soil to suppress the rising of water and avoid possible damage of the pavement. Water rises through liquid bridges, menisci and trimers, whose width and connectivity depends on the maximal half-length λ of the capillary bridges among grains. Low λs generate a disconnect structure, with small clusters everywhere. On the contrary, for high λ, create a percolating cluster of trimers and enclosed volumes that form a natural path for capillary rise. Hereby, we study the percolation transition of this geometric structure as a function of λ on a granular media of monodisperse spheres in a random close packing. We determine both the percolating threshold λ c = (0.049 ± 0.004)R (with R the radius of the granular spheres), and the critical exponent of the correlation length ν = 0.830 ± 0.051, suggesting that the percolation transition falls into the universality class of ordinary percolation.

show abstract

Section: Powders and Grains 2017mentioning

confidence: 99%

Percolation study for the capillary ascent of a liquid through a granular soil

Cárdenas-Barrantes¹,

Muñoz²,

Araújo³

2017

EPJ Web Conf.

View full text Add to dashboard Cite

show abstract

“…However, such methods based on this approach use sequential computations. Several methods have been proposed to exploit parallel computing architectures [5,9]. However, these methods were applied to relatively small images.…”

Section: Related Workmentioning

confidence: 99%

Gpu-Based Object Identification in Large-Scale Images for Real-Time Radar Signal Analysis

Shioya¹,

Yamamoto²

2016

IJNCAA

View full text Add to dashboard Cite

As the computing power of processors is being drastically improved, the sizes of image data for various applications are also increasing. However, CCL cannot be easily implemented in a parallel fashion because the connected pixels can be found basically only by graph traversal. In this paper, we propose a GPU-based efficient algorithm for object identification in large-scale images and the performance of the proposed method is compared with that of the most commonly used method implemented with OpenCV libraries. The experimental results show that the proposed method outperforms the reference method when the pixel density is below 0.7. Object identification in image data is the fundamental operation and rapid computation is highly requested as the sizes of the currently available image data rapidly increase. The method proposed in this paper is applied to the analysis of radar signals. Since the radar signals contains complex and large data, the efficiency of the method is crucial when realtime analysis is required. The experimental results show the proposed method can be a good solution to the object identification in largescale image data such as radar signals.

show abstract

“…GPUs have been used for various finite-difference PDE integrator codes with good results [4][5][6][7][8][9]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. [9], the authors use CUDA to simulate the multi-dimensional complex Ginzburg-Landau equation (a generalization of the NLSE), but speedups versus CPU codes were not reported. All these studies indicate that a GPU treatment of the NLSE would be beneficial.…”

Section: Introductionmentioning

confidence: 99%

NLSEmagic: Nonlinear Schrödinger equation multi-dimensional Matlab-based GPU-accelerated integrators using compact high-order schemes

Caplan

2013

Computer Physics Communications

View full text Add to dashboard Cite

We present a simple to use, yet powerful code package called NLSEmagic to numerically integrate the nonlinear Schrödinger equation in one, two, and three dimensions. NLSEmagic is a high-order finite-difference code package which utilizes graphic processing unit (GPU) parallel architectures. The codes running on the GPU are many times faster than their serial counterparts, and are much cheaper to run than on standard parallel clusters. The codes are developed with usability and portability in mind, and therefore are written to interface with MATLAB utilizing custom GPUenabled C codes with the MEX-compiler interface. The packages are freely distributed, including user manuals and set-up files. The integrators utilize a fully-explicit fourth-order Runge-Kutta scheme in time and both second-and fourthorder differencing in space. The integrators are written to run on NVIDIA GPUs and are interfaced with MATLAB including built-in visualization and analysis tools. Restrictions:The main restriction for the GPU integrators is the amount of RAM on the GPU as the code is currently only designed for running on a single GPU. Unusual features:Ability to visualize real-time simulations through the interaction of MATLAB and the compiled GPU integrators. Additional comments:Program has a dedicated web site at www.nlsemagic.com. Running time:A three-dimensional run with a grid dimension of 87x87x203 for 3360 time steps (100 non-dimensional time units) takes about one and a half minutes on a GeForce GTX 580 GPU card.

show abstract

Parallel graph component labelling with GPUs and CUDA

Cited by 146 publications

References 35 publications

Percolation study for the capillary ascent of a liquid through a granular soil

Percolation study for the capillary ascent of a liquid through a granular soil

Gpu-Based Object Identification in Large-Scale Images for Real-Time Radar Signal Analysis

NLSEmagic: Nonlinear Schrödinger equation multi-dimensional Matlab-based GPU-accelerated integrators using compact high-order schemes

Contact Info

Product

Resources

About