Parallel computation of a dam-break flow model using OpenMP on a multi-core computer

Zhang, Shanghong; Zhongxi, Xia; Yuan, Rui; Jiang, Xiaoming

doi:10.1016/j.jhydrol.2014.02.035

Cited by 45 publications

(27 citation statements)

References 21 publications

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“…So the key to a parallel implementation based on shared memory is to determine the main loops that can be run separately in the model. OpenMP parallel instructions [38,39] can then be used to allocate these uncorrelated units to different processors, decreasing the calculation time. In our model, because the simulation is iterated over a series of time steps, it is most beneficial if all the calculation units in a time step can be run separately.…”

Section: Parallel Model Implementationmentioning

confidence: 99%

“…In our model, because the simulation is iterated over a series of time steps, it is most beneficial if all the calculation units in a time step can be run separately. In Conjugate Gradient Squared (CGS) [37], adding OpenMP instructions [38,39] to the circulation calculations in an iterative step can realize parallel computing. Different vector multiply vector calculations are distributed to different threads.…”

Section: Introductionmentioning

confidence: 99%

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A parallel CGS block-centered finite difference method for a nonlinear time-fractional parabolic equation

Liu

2016

Computer Methods in Applied Mechanics and Engineering

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Section: Parallel Model Implementationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A parallel CGS block-centered finite difference method for a nonlinear time-fractional parabolic equation

Liu

2016

Computer Methods in Applied Mechanics and Engineering

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“…The parallel computation is put into practice in three ways: general-purpose computing on graphics processing units, via a message processing interface, or using shared memory architecture (OpenMP) [18]. In a shared memory model system, threads, which are processes such that each runs as parallel, are communicated each other by reading and writing directly to the same memory.…”

Section: Implementation Of the Parallel Directivesmentioning

confidence: 99%

Parallelization of a Hierarchical Graph-Based Image Segmentation using OpenMP

Sağlam

Baykan

2016

International Journal of Applied Mathematics, Electronics and Computers

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Abstract:In many image-processing applications, image segmentation is an essential stage. In this stage, an image is partitioned into several regions according to the similarity of its pixels. In addition to the accuracy of the image segmentation, the speed is also very important for real-time image processing applications. Many computer applications take advantages of the multi-processor architecture to up to their running performance. However, to run an algorithm as parallel is very difficult in many cases. Due to using the same memory blocks, many conflicts might be happened between the processors. Moreover, each process of one processor may depend on those of another processor. For this reason, the algorithm to be parallelized must be suitable to parallel. In addition, the processing traffic that is pursued by the processors must be controlled within some parallel directives. In this paper, we provide a parallel implementation to a hierarchical graph-based image segmentation method by using its hierarchical processing steps. To achieve this goal, we utilize the OpenMP (Open Multi-Processing) Library to run the segmentation process as parallel on images of different sizes from the INRIA Holidays dataset. The experimental results show that the parallel implementation of the algorithm is more effective than the serial type according to processing time.

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“…The LISFLOOD-FP explicit storage cell model was parallelized using OpenMP [13], and Neal et al [14] tested the OpenMP version of the LISFLOOD-FP for a range of test cases with grid sizes that varied from 3 km to 3 m. The latter study showed that such test cases had a significant effect on acceleration. Zhang et al [15] applied OpenMP to the parallel computation of the dam-break model and gained a speedup factor of 8.64× on a 16-core computer. In the MPI parallel applications, TRENT [16,17], CalTWiMS [18], RMA [19], and Oger et al [20] used MPI and regional decomposition in the parallel simulation cases.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Different Parallel Computation Methods for a Two‐Dimensional Dam‐Break Model

Zhang

Jing

et al. 2017

Mathematical Problems in Engineering

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Three parallel methods (OpenMP, MPI, and OpenACC) are evaluated for the computation of a two-dimensional dam-break model using the explicit finite volume method. A dam-break event in the Pangtoupao flood storage area in China is selected as a case study to demonstrate the key technologies for implementing parallel computation. The subsequent acceleration of the methods is also evaluated. The simulation results show that the OpenMP and MPI parallel methods achieve a speedup factor of 9.8× and 5.1×, respectively, on a 32-core computer, whereas the OpenACC parallel method achieves a speedup factor of 20.7× on NVIDIA Tesla K20c graphics card. The results show that if the memory required by the dam-break simulation does not exceed the memory capacity of a single computer, the OpenMP parallel method is a good choice. Moreover, if GPU acceleration is used, the acceleration of the OpenACC parallel method is the best. Finally, the MPI parallel method is suitable for a model that requires little data exchange and large-scale calculation. This study compares the efficiency and methodology of accelerating algorithms for a dam-break model and can also be used as a reference for selecting the best acceleration method for a similar hydrodynamic model.

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Parallel computation of a dam-break flow model using OpenMP on a multi-core computer

Cited by 45 publications

References 21 publications

A parallel CGS block-centered finite difference method for a nonlinear time-fractional parabolic equation

A parallel CGS block-centered finite difference method for a nonlinear time-fractional parabolic equation

Parallelization of a Hierarchical Graph-Based Image Segmentation using OpenMP

Comparison of Three Different Parallel Computation Methods for a Two‐Dimensional Dam‐Break Model

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