Solving the Euler Equations on Graphics Processing Units

Hagen, Trond Runar; Lie, Knut–Andreas; Natvig, Jostein R.

doi:10.1007/11758549_34

Cited by 66 publications

(39 citation statements)

References 5 publications

(6 reference statements)

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“…Goddeke et al [13,14,15], Elsen et al [9] and Thibault et al [38] produce effective speedups ranging from 10x to 20x for finite element Navier-Stokes solvers for more complex problems. Brandvik et al [1] and Hagen et al [16] also show 10x to 20x speedups for 3D Euler solvers. Stantchev et al [37] achieved up to a 14x speedup for plasma turbulence modeling using the Hasegawa-Mima equation solver.…”

Section: Literature Reviewmentioning

confidence: 95%

Development and acceleration of parallel chemical transport models

Eller

Singh

Sandu

2010

Proceedings of the 2010 Spring Simulation Multiconference

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The STEM chemical transport model provides a large scale end-to-end application to experiment with running chemical integration methods and transport methods on GPUs. GPUs provide high computational power at a fairly cheap cost. The CUDA programming environment simplifies the GPU development process by providing access to powerful functions to execute parallel code. This work demonstrates the accleration of a large scale end-to-end application on GPUs showing significant speedups. This is achieved by implementing all relevant kernels on the GPU using CUDA. Nevertheless, further improvements to GPUs are needed to allow these applications to fully exploit the power of GPUs. AcknowledgmentsI would like to thank a number of people who have provided me with support and guidance over the last few years. I would like to thank my advisor Dr.Sandu for helping to guide my research over the last few years and for helping me to better understand the issues necessary to simulate large scale chemical transport models such as GEOS-Chem and STEM from both a mathematical and computational perspective.

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Section: Literature Reviewmentioning

confidence: 95%

Development and acceleration of parallel chemical transport models

Eller

Singh

Sandu

2010

Proceedings of the 2010 Spring Simulation Multiconference

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“…Harris et al [27] used GPU-based NSE solutions with partial differential equations (PDEs) for thermodynamics and water condensation and light scattering simulation to develop visual simulation of cloud dynamics. A simulation of the dynamics of ideal gases in two and three dimensions using the Euler equations on the GPU was described in [24].…”

Section: Physically Based Simulationmentioning

confidence: 99%

Hyper-Real-Time Ice Simulation and Modeling Using GPGPU

Alawneh

Dragt

Peters

et al. 2015

IEEE Trans. Comput.

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Simulation of the behaviour of a ship operating in pack ice is a computationally intensive process to which General Purpose Computing on Graphical Processing Units (GPGPU) can be applied. GPGPU is the use of a GPU (graphics processing unit) to do general purpose scientific and engineering computing. The model for GPU computing is to use a CPU and GPU together in a heterogeneous co-processing computing platform. The sequential part of the application runs on the CPU and the computationally-intensive part is accelerated by the GPU. From the users perspective, the application just runs faster because it is using the high-performance of the GPU to boost performance. This thesis presents an efficient parallel implementation of such a simulator developed using the NVIDIA Compute Unified Device Architecture (CUDA). This simulator can be used to give users the ability to analyze ice-interactions for design, assessment and training purposes. This thesis also describes the execution of experiments to evaluate the performance of the simulator and to validate the numerical modeling of ship operations in pack ice. It also describes the useful applications that have been done using this simulator in planning ice management activities. i

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“…Later works by Hagen et al 10 began to incorporate more advanced numerical methods such as high-resolution finite volume with Runga-Kutta time integrations, and study compressible flows with both 2D and 3D Euler equations accelerated by the G70 GPU. They use Cartesian meshes which unfortunately limits the application to simple geometries.…”

Section: A Previous Workmentioning

confidence: 99%

Unsteady Turbulent Simulations on a Cluster of Graphics Processors

Phillips

Davis

Owens

2010

40th Fluid Dynamics Conference and Exhibit

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This paper describes the GPU accelerated MBFLO2 multi-block turbulent flow solver completely in double precision using CUDA and the latest generation of GPU processors. On a cluster of 8 Tesla C2050 "Fermi" GPUs and Intel Xeon X5550 "Nehalem" quad-core CPUs, we achieve 9x speedup over the parallel CPU solver or 70x speedup over the serial solver. High performance is obtained by optimizing the data layout on the GPU, optimizing data transfers and using asynchronous memory copies to overlap GPU execution with communications. We test the solver on a turbulent flat plate and an unsteady turbulent cylinder with 3.2 million grid points. We confirm the GPU results are in agreement with turbulent flow theory. We discuss the GPU optimization techniques used to reach this level of performance. Nomenclature E = total energy g = acceleration due to gravity H = total enthalpy h = static enthalpy I = rothalpy k = turbulent kinetic energy p = pressure Pr = Prandtl number Pr t = turbulent Prandtl number R = radius from specified axis of rotation S ij = mean strain-rate tensor u = axial velocity component û = internal energy v = tangential velocity component V = velocity magnitude z = elevation = coefficient of viscosity = turbulent coefficient of viscosity = turbulent dissipation rate divided by turbulent kinetic energy = rotational velocity about specified axis of rotation (rads/s) ij = shear stress tensor = density

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Solving the Euler Equations on Graphics Processing Units

Cited by 66 publications

References 5 publications

Development and acceleration of parallel chemical transport models

Development and acceleration of parallel chemical transport models

Hyper-Real-Time Ice Simulation and Modeling Using GPGPU

Unsteady Turbulent Simulations on a Cluster of Graphics Processors

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