gpuSPHASE—A shared memory caching implementation for 2D SPH using CUDA (new version announcement)

Winkler, Daniel; Rezavand, Massoud; Meister, Michael; Rauch, Wolfgang

doi:10.1016/j.cpc.2018.08.016

Cited by 10 publications

(7 citation statements)

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“…A good agreement in the overall behavior of the pressure is noted in the prediction of both time and value, with lower peak pressure values compared to those from experiments. Furthermore, in contrast to the single-phase SPH simulation, the peak pressure values predicted by the present method are much lower, implying the cushioning effect of entrapped air pockets, which is excluded in single-phase SPH simulations and can lead to highly overestimated peak pressure values [50].…”

Section: Two-phase Liquid Sloshingcontrasting

confidence: 57%

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A weakly compressible SPH method for violent multi-phase flows with high density ratio

Rezavand

Zhang

2020

Journal of Computational Physics

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The weakly compressible SPH (WCSPH) method is known suffering from low computational efficiency, or unnatural voids and unrealistic phase separation when it is applied to simulate highly violent multi-phase flows with high density ratio, such as that between water and air. In this paper, to remedy these issues, we propose a multi-phase WCSPH method based on a low-dissipation Riemann solver and the transport-velocity formulation. The two-phase Riemann problem is first constructed to handle the pairwise interaction between fluid particles, then modified for the fluid-wall interaction to impose the solid wall boundary condition. Since the method uses the same artificial speed of sound for both heavy and light phases, the computational efficiency increases greatly. Furthermore, due to the transport-velocity formulation employed for the light phase and application of the two-phase Riemann problem, the unnatural voids and unrealistic phase separation are effectively eliminated. The method is validated with several 2-and 3D cases involving violent water-air flows. The results have been compared with existing experimental data, previous numerical and analytical solutions, where the proposed method demonstrates good robustness, improved or comparable accuracy, respectively, comparing to previous methods with same choice of sound speed or those with much less computational efficiency.

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Section: Two-phase Liquid Sloshingcontrasting

confidence: 57%

“…Due to the highly non-linear phenomena in sloshing, this benchmark case has been used to validate many single-phase SPH methods (see e.g. [49,50,42]), as well as few two-phase counterparts [19]. In this section we consider a two-phase liquid sloshing case, which has been experimentally studied by Rafiee et al [49].…”

Section: Two-phase Liquid Sloshingmentioning

confidence: 99%

A weakly compressible SPH method for violent multi-phase flows with high density ratio

Rezavand

Zhang

2020

Journal of Computational Physics

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“…In contrast, researchers in computer graphics seem to prefer uniform grids, e.g. [Gon07, Gre10, PH10, DCGGM11, DCVB*13, IOS*14, WMRR17, WSG*18] that can be built in

O (N)

and queried in

O (1)

.…”

Section: Related Workmentioning

confidence: 99%

“…In addition to uniform grids, Verlet‐Lists (VL) [Ver67, VBC08, WMRR17] have been proposed to alleviate the performance issue of recomputing the neighbour lists in each simulation step. As the neighbour lists are created from a larger support, they can be retained for several steps.…”

Section: Related Workmentioning

confidence: 99%

Compressed Neighbour Lists for SPH

Band

Gissler

Teschner

2019

Computer Graphics Forum

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We propose a novel compression scheme to store neighbour lists for iterative solvers that employ Smoothed Particle Hydrodynamics (SPH). The compression scheme is inspired by Stream VByte, but uses a non‐linear mapping from data to data bytes, yielding memory savings of up to 87%. It is part of a novel variant of the Cell‐Linked‐List (CLL) concept that is inspired by compact hashing with an improved processing of the cell‐particle relations. We show that the resulting neighbour search outperforms compact hashing in terms of speed and memory consumption. Divergence‐Free SPH (DFSPH) scenarios with up to 1.3 billion SPH particles can be processed on a 24‐core PC using 172 GB of memory. Scenes with more than 7 billion SPH particles can be processed in a Message Passing Interface (MPI) environment with 112 cores and 880 GB of RAM. The neighbour search is also useful for interactive applications. A DFSPH simulation step for up to 0.2 million particles can be computed in less than 40 ms on a 12‐core PC.

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“…The SPH approach occupies a significant market quota in astrophysical computational fluid dynamics and engineering applications. It should be specially distinguished the GASOLINE code [15], Weakly Compressible Smoothed Particle Hydrodynamics for multi-GPUs systems [4] and gpuSPHASE for the engineering calculations [16]. The aim of our research is the computational characteristics analysis of a parallel program for galaxies' gas self-gravitating subsystems modeling by the SPH and direct N-body methods using GPU technology.…”

Section: Introductionmentioning

confidence: 99%

Supercomputing

2017

Communications in Computer and Information Science

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Parallel implementation features of self-gravitating gas dynamics modeling on multiple GPUs are considered applying the GPU-Direct technology. The parallel algorithm for solving of the selfgravitating gas dynamics problem based on hybrid OpenMP-CUDA parallel programming model has been described in detail. The gas-dynamic forces are calculated by the modified SPH-method (Smoothed Particle Hydrodynamics) while the N-body problem gravitational interaction is obtained by the direct method (so-called Particle-Particle algorithm).The key factor in the SPH-method performance is creation of the neighbor lists of the particles which contribute into the gas-dynamic forces calculation. Our implementation is based on hierarchical grid sorting method using a cascading algorithm for parallel computations of partial sums at CUDA block. The parallelization efficiency of the algorithm for various GPUs of the Nvidia Tesla line (K20, K40, K80) is studied in the framework of galactic' gaseous halos collisions models by the SPHmethod 1 .

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gpuSPHASE—A shared memory caching implementation for 2D SPH using CUDA (new version announcement)

Cited by 10 publications

References 0 publications

A weakly compressible SPH method for violent multi-phase flows with high density ratio

A weakly compressible SPH method for violent multi-phase flows with high density ratio

Compressed Neighbour Lists for SPH

Supercomputing

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