Abstract
Recently, several research groups have demonstrated significant speedups of scientific computations using General Purpose Graphics Processor Units (GPGPU) as massively-parallel “co-processors” to the Central Processing Unit (CPU). However, the tremendous computational power of GPGPUs has come with a high price since their implementation to Computational Fluids Dynamics (CFD) solvers is still a challenge. To achieve this implementation, the RapidCFD library was developed from the Open Field Operation and Manipulation (OpenFOAM) CFD software to let that the multi-GPGPU were able of running almost the entire simulation in parallel. The parallel performance, as fixed-size speed-up, efficiency and parallel fraction, according to the Amdahl’s law, were compared in two massively parallel multi-GPGPU architectures using Nvidia Tesla C1060 and M2090 units. The simulations were executed on a 3D turbo-machinery benchmark which consist of a structured grid domain of 1 million cells. The results obtained from the implementation of the new library on different software and hardware layouts show that by transferring directly all the computations executed by the linear system solvers to the GPGPU, is possible to make a typical CFD simulation until 9 times faster. Additionally a grid convergence analysis and pressure recovery measurements were executed over scaled computational domains. Thus, it is expected to obtain an affordable low computational cost when the domain be scaled in order to achieve a high flow resolution.
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Molinero, D., Galván, S., Pacheco, J., Herrera, N. (2019). Multi GPU Implementation to Accelerate the CFD Simulation of a 3D Turbo-Machinery Benchmark Using the RapidCFD Library. In: Torres, M., Klapp, J. (eds) Supercomputing. ISUM 2019. Communications in Computer and Information Science, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-38043-4_15
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