High order accurate simulation of compressible flows on GPU clusters over Software Distributed Shared Memory

Karantasis, Konstantinos I.; Polychronopoulos, Eleftherios D.; Ekaterinaris, John A.

doi:10.1016/j.compfluid.2014.01.005

Cited by 20 publications

(11 citation statements)

References 31 publications

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“…Since appearance, GPU has shown distinctive prospects across a large range of fields in practice, for instance, artificial intelligence, deep learning, molecular dynamics, quantum chemistry, high-energy physics, and likewise, in CFD applications. Researchers have made the technology of extension mature from single to several GPUs and even clusters [6][7][8], including the different speedups between explicit and implicit schemes [9], the variance among structured, unstructured and hybrid grids [10,11], the influence of single and double precision [12], as well as high-order schemes and high-fidelity methods attracting increasing attention [13][14][15][16][17][18]. Contributed by hardware's development, GPU has possessed the power of simulating more complicated problems, such as turbulence, where LES was studies earlier [19,20] but DNS was still in the infancy [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Optimization and acceleration of flow simulations for CFD on CPU/GPU architecture

Jiang

Zhou

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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With the increasing requirement of high computational power in computational fluid dynamics (CFD) field, the graphic processing units (GPUs) with great floating-point computing capability play more important roles. This work explores the porting of an Euler solver from central processing units (CPUs) to three different CPU/GPU heterogeneous hardware platforms using MUSCL and NND schemes, and then the computational acceleration of one-dimensional (1D) Riemann problem and two-dimensional (2D) flow past a forward-facing step is investigated. Based on hardware structures, memory models and programming methods, the working manner of heterogeneous systems was firstly introduced in this paper. Subsequently, three different heterogeneous methods employed in the current study were presented in detail, while porting all parts of the solver loop to GPU possessed the best performance among them. Several optimization strategies suitable for the solver were adopted to achieve substantial execution speedups, while using shared memory on GPU was relatively rarely reported in CFD literature. Finally, the simulation of 1D Riemann verified the reliability of the modified codes on GPU, demonstrating strong ability in capturing discontinuities of both schemes. The two cases with their 1D computational domains discretized into 10,000 cells both realized a speedup exceeding 25, compared to that executed on a single-core CPU. In simulation of the 2D step flow, we came to the highest speedups of 260 for MUSCL scheme with 800 × 400 mesh size and 144 for NND scheme with 400 × 200 computational domain, respectively.

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Section: Introductionmentioning

confidence: 99%

Optimization and acceleration of flow simulations for CFD on CPU/GPU architecture

Jiang

Zhou

et al. 2019

J Braz. Soc. Mech. Sci. Eng.

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“…Due to this simple regular workflow, explicit solvers are suited to the GPU massively parallel architecture and can benefit largely from its computational power. Interesting speedups of one to two orders of magnitudes have been reported in the literature [Brandvik and Pullan, 2011;Brock et al, 2015;Karantasis et al, 2014;Elsen et al, 2008;Lefebvre et al, 2012]. In general, explicit solvers are stable only with a small time step, as the latter is controlled by relatively low Courant-Friedrichs-Lewy (CFL) conditions (e.g.…”

Section: Introductionmentioning

confidence: 99%

GPU-accelerated CFD Simulations for Turbomachinery Design Optimization

Aissa¹

2018

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Curriculum vitae 157 List of publications and presentations 159 My research work within my Ph.D. started with the project AMEDEO enabling me to meet and exchange with great people, visit many research institutes and access valuable training all over the journey. For that, I am very grateful to my supervisor Dr. Tom Verstraete who believed on my small but growing GPU expertise back on 2013. Tom has done a fantastic supervising work with me by first giving me the freedom to act in my research but then closely checking my results. Moreover, I would like to thank Juliet Jopson for successfully coordinating the AMEDEO project and making the administration part much easier for all research fellows within the project. I would like also to thank Prof. Harvey Thomson for offering me a secondment within his team at the University of Leeds. During the short stay, I cooperated with Nicolas Delbosc from whom I learned so much about GPU performance assessment. I am very grateful to you Nic. I am thankful to all of AMEDEO people and a special thank to Dr. Roeland de Breuker for putting me in contact with Prof. Kees Vuik who later become my Ph.D. promoter. Prof. Vuik knows how to get a student fully motivated and prepared for the challenges within a Ph.D. project. You name a software library or a research facility and he would surely know someone to contact to make the stuff happen that you asked for (e.g. DAS-5, SURFsara, Paralution). For all the time and the effort you invested in my project I am really thankful. I would like also to thank the graduate school at TU Delft for the enriching courses and the possibility they bring to meet other Ph.D. students and exchange about ideas, challenges, views, fears, and ambitions. I would like to thank Deborah Dongor for helping me through the process of being an external Ph.D. student. My work would not have reached the maturity it has now without the constant support of the people of the TU department in VKI. My supervisor Tom and Prof. Tony Arts were always supportive when it comes to attending conferences and presenting my work. I owe a lot of what I learned during these last years to my colleague Lasse Müller. Besides the valuable discussions he provided me with the CPU version of the CFD simulation, the base of all my work. I am grateful to my colleague Christopher Chahine who taught me a lot of practical knowledge in design optimization. I would like to thank Roberto with whom I cooperated the last months and learned a lot. Eager to continue the collaboration. For the rest of the TU group I xi xii hope we will renew our biweekly department meeting to continue exchanging about our latest results. I would like to thank all the computer center staff here at VKI and also Christelle and Evelyne for supporting my numerous requests for papers and documentation. I am thankful to the VKI administration and especially to Dirk who made all administrative procedures so easy for me at the beginning of my Ph.D. and after him, Simone took over and facilitated all the paper work for me. I would ...

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“…They achieved an acceleration of 20x for the Euler equations. Additionally, several recent CFD applications can be seen in Wang, Abel, and Kaehler (2010); Xian and Takayuki (2011); Karantasis, Polychronopoulos, and Ekaterinaris (2014); Ma, Wang, and Pu (2014); Liu, Zhong, and Xu (2016), and Jude and Baeder (2016).…”

Section: Introductionmentioning

confidence: 99%

An implementation of the direct-forcing immersed boundary method using GPU power

Tutkun

Edis

2016

Engineering Applications of Computational Fluid Mechanics

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A graphics processing unit (GPU) is utilized to apply the direct-forcing immersed boundary method. The code running on the GPU is generated with the help of the Compute Unified Device Architecture (CUDA). The first and second spatial derivatives of the incompressible Navier-Stokes equations are discretized by the sixth-order central compact finite-difference schemes. Two flow fields are simulated. The first test case is the simulated flow around a square cylinder, with the results providing good estimations of the wake region mechanics and vortex shedding. The second test case is the simulated flow around a circular cylinder. This case was selected to better understand the effects of sharp corners on the force coefficients. It was observed that the estimation of the force coefficients did not result in any troubles in the case of a circular cylinder. Additionally, the performance values obtained for the calculation time for the solution of the Poisson equation are compared with the values for other CPUs and GPUs from the literature. Consequently, approximately 3× and 20× speedups are achieved in comparison with GPU (using CUSP library) and CPU, respectively. CUSP is an open-source library for sparse linear algebra and graph computations on CUDA.

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High order accurate simulation of compressible flows on GPU clusters over Software Distributed Shared Memory

Cited by 20 publications

References 31 publications

Optimization and acceleration of flow simulations for CFD on CPU/GPU architecture

Optimization and acceleration of flow simulations for CFD on CPU/GPU architecture

GPU-accelerated CFD Simulations for Turbomachinery Design Optimization

An implementation of the direct-forcing immersed boundary method using GPU power

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