GPU computing of compressible flow problems by a meshless method with space-filling curves

Ma, Zhihua; Wang, H.; Pu, S.H.

doi:10.1016/j.jcp.2014.01.023

Cited by 23 publications

(20 citation statements)

References 39 publications

(54 reference statements)

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“…Since 3D compressible multiphase flow simulations request very much more intensive computation than 1D problems, it is necessary to consider parallel computing implementations via many-core GPUs. The benefits of utilising GPU acceleration in CFD can be found in recent works pertaining to single-phase flows which successfully accelerated the flow solvers by several times and even orders of magnitude [37][38][39][40][41]. It is very likely that applying many core GPU techniques to dramatically reduce the computing time for simulating compressible multiphase flows will be attractive and beneficial to the academic and industrial communities.…”

Section: Introductionmentioning

confidence: 99%

A GPU based compressible multiphase hydrocode for modelling violent hydrodynamic impact problems

Causon

Qian

et al. 2015

Computers & Fluids

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Highlights• A versatile compressible multiphase hydrocode for marine engineering impact problems.• Non-physical pressure oscillation near the material interface is successfully avoided.• It can accurately model violent hydrodynamic slamming problems by properly dealing the crucial compressibility effects of fluid.• It can also model complex hull cavitation problems in addition to incipient cavitation.• The hydrocode is successfully accelerated on the GPU by a speed up over 60x. AbstractThis paper presents a GPU based compressible multiphase hydrocode for modelling violent hydrodynamic impacts under harsh conditions such as slamming and underwater explosion. An effort is made to extend a one-dimensional five-equation reduced model (Kapila et al., Two-phase modeling of deflagration-to-detonation transition in granular materials: Reduced equations, Phys. Fluids, 2001, Vol.13, 3002 -3024) to compute three-dimensional hydrodynamic impact problems on modern graphics hardware. In order to deal with free-surface problems such as water waves, gravitational terms, which are initially absent from the original model, are now considered and included in the governing equations. A third-order finite volume based MUSCL scheme is applied to discretise the integral form of the governing equations. The numerical flux across a mesh cell face is estimated by means of the HLLC approximate Riemann solver. The serial CPU program is firstly parallelised on multi-core CPUs with the OpenMP programming model and then further accelerated on many-core graphics processing units (GPUs) using the CUDA C programming language.To balance memory usage, computing efficiency and accuracy on multi-and many-core processors, a mixture of single and double precision floating-point operations is implemented. The most important data like conservative flow variables are handled with double-precision dynamic arrays, whilst all the other variables/arrays like fluxes, residual and source terms are treated in single precision. Several benchmark test cases including water-air shock tubes, one-dimensional liquid cavitation tube, dam break, 2D cylindrical underwater explosion near a planar rigid wall, 3D spherical explosion in a rigid cylindrical container and water entry of a 3D rigid flat plate have been calculated using the present approach. The obtained results agree well with experiments, exact solutions and other independent numerical computations. This demonstrates the capability of the present approach to deal with not only violent free-surface impact problems but also hull cavitation associated with underwater explosions. Performance analysis reveals that the running time cost of numerical simulations is dramatically reduced by use of GPUs with much less consumption of electrical energy than on the CPU.

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Section: Introductionmentioning

confidence: 99%

A GPU based compressible multiphase hydrocode for modelling violent hydrodynamic impact problems

Causon

Qian

et al. 2015

Computers & Fluids

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“…It should be emphasized that when using GPU for parallel computing, the challenge is not only parallelization of the code, but also optimization of the access to device memory by making the best use of registers and shared memory (Corrigan, Camelli, Löhner, & Wallin, 2011;Julien & Inanc, 2009). Meanwhile, it should be pointed out that the shared memory is difficult to use for an irregular meshless structure of clouds of points due to the unpredictable memory access pattern (see Ma et al (2014) for details); therefore, the present work is mainly focused on the best use of registers by searching for an appropriate size of thread block used for the meshless solver; this will be discussed in section 4.2.2.…”

Section: Cuda Programming Modelmentioning

confidence: 99%

“…As mentioned above, Ma et al (2014) renumbered the points of meshless clouds to improve the performance of the global memory address; here, we manage the kernel algorithm directly by developing a point-based algorithm to achieve an appropriate global memory address. In this way, the GPU kernels can own the same thread structure, and the new measurement can therefore be taken by combining part of the kernels to enable further improvement of the performance.…”

Section: Point-based Algorithmsmentioning

confidence: 99%

“…Hence, it is necessary to develop a GPU-based meshless method to acheive further accelerations. Recently, a GPU-based meshless method (Ma, Wang, & Pu, 2014) was presented and successfully applied to accelerating simulations of compressible inviscid flows, which was achieved by applying a Hilbert space-filling curve to renumber the points to enhance performance of the global memory address. However, few attempts to simulate viscous flows and the direct management of kernel algorithm to achieve appropriate global memory address are known.…”

Section: Introductionmentioning

confidence: 99%

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A graphics processing unit-accelerated meshless method for two-dimensional compressible flows

Zhang

Chen

Cao

2017

Engineering Applications of Computational Fluid Mechanics

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A graphics processing unit (GPU) -accelerated meshless method is presented for solving twodimensional compressible flows over aerodynamic bodies. The Compute Unified Device Architecture (CUDA) Fortran programming model is employed to port the meshless method from central processing unit to GPU as a way of achieving efficiency, which involves implementation of CUDA kernels and management of data storage structure and thread hierarchy. The CUDA kernel subroutines are designed to meet with the point-based computing of the meshless method. The corresponding point-based data structure and thread hierarchy are constructed or manipulated in the paper by presenting two specific GPU implementations of the meshless method, which are developed for solving Navier-Stokes equations. The Jameson-Schmidt-Turkel scheme is used to estimate the flux terms of the Navier-Stokes equations and an explicit four-stage Runge-Kutta scheme is applied to update the solution at time level. After tuning the performances of the resulting two GPU-accelerated meshless solvers by changing the number of threads in a block, a set of typical flows over aerodynamic bodies are simulated for validation. Numerical results are shown in a comparison with available experimental data or computational values that appear in extant literature with an analysis of code performance. This reveals that the cost of computing time of the presented test cases is significantly reduced for both solvers without losing accuracy, while impressive speedups up to 64 times are achieved due to careful management of memory access. ARTICLE HISTORY

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“…In the context fluid dynamics and meshless methods, Kelly et al (2014) explore a GPU implementation of an incompressible Navier-Stokes code based on a Radial-Basis Function Collocation Meshless Method for two-fluid flows. For compressive flows, Ma et al (2014) show a GPU implementation.…”

Section: Introductionmentioning

confidence: 99%

An Iterative Parallel Solver in GPU Applied to Frequency Domain Linear Water Wave Problems by the Boundary Element Method

Molina

Martínez-Castro

Ortiz

2018

Front. Built Environ.

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In this paper a parallel iterative solver based on the Generalized Minimum Residual Method (GMRES) with complex-valued coefficients is explored, with applications to the Boundary Element Method (BEM). The solver is designed to be executed in a GPU (Graphic Processing Unit) device, exploiting its massively parallel capabilities. The BEM is a competitive method in terms of reduction in the number of degrees of freedom. Nonetheless, the BEM shows disadvantages when the dimension of the system grows, due to the particular structure of the system matrix. With difference to other acceleration techniques, the main objective of the proposed solver is the direct acceleration of existing standard BEM codes, by transfering to the GPU the solver task. The CUDA programming language is used, exploiting the particular architecture of the GPU device for complex-valued systems. To explore the performances of the solver, two linear water wave problems have been tested: the frequency-dependent added mass and damping matrices of a 3D floating body, and the Helmholtz equation in a 2D domain. A NVidia GeForce GTX 1080 graphic card has been used. The parallelized GMRES solver shows reductions in computing times when compared with its CPU implementation.

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GPU computing of compressible flow problems by a meshless method with space-filling curves

Cited by 23 publications

References 39 publications

A GPU based compressible multiphase hydrocode for modelling violent hydrodynamic impact problems

A GPU based compressible multiphase hydrocode for modelling violent hydrodynamic impact problems

A graphics processing unit-accelerated meshless method for two-dimensional compressible flows

An Iterative Parallel Solver in GPU Applied to Frequency Domain Linear Water Wave Problems by the Boundary Element Method

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