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Challenges on Porting Lattice Boltzmann Method on Accelerators: NVIDIA Graphic Processing Units and Intel Xeon Phi

Challenges on Porting Lattice Boltzmann Method on Accelerators: NVIDIA Graphic Processing Units and Intel Xeon Phi

Claudio Schepke, João V. F. Lima, Matheus S. Serpa
Copyright: © 2018 |Pages: 24
ISBN13: 9781522547600|ISBN10: 1522547606|EISBN13: 9781522547617
DOI: 10.4018/978-1-5225-4760-0.ch002
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MLA

Schepke, Claudio, et al. "Challenges on Porting Lattice Boltzmann Method on Accelerators: NVIDIA Graphic Processing Units and Intel Xeon Phi." Analysis and Applications of Lattice Boltzmann Simulations, edited by Pedro Valero-Lara, IGI Global, 2018, pp. 30-53. https://doi.org/10.4018/978-1-5225-4760-0.ch002

APA

Schepke, C., Lima, J. V., & Serpa, M. S. (2018). Challenges on Porting Lattice Boltzmann Method on Accelerators: NVIDIA Graphic Processing Units and Intel Xeon Phi. In P. Valero-Lara (Ed.), Analysis and Applications of Lattice Boltzmann Simulations (pp. 30-53). IGI Global. https://doi.org/10.4018/978-1-5225-4760-0.ch002

Chicago

Schepke, Claudio, João V. F. Lima, and Matheus S. Serpa. "Challenges on Porting Lattice Boltzmann Method on Accelerators: NVIDIA Graphic Processing Units and Intel Xeon Phi." In Analysis and Applications of Lattice Boltzmann Simulations, edited by Pedro Valero-Lara, 30-53. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-4760-0.ch002

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Abstract

Currently NVIDIA GPUs and Intel Xeon Phi accelerators are alternatives of computational architectures to provide high performance. This chapter investigates the performance impact of these architectures on the lattice Boltzmann method. This method is an alternative to simulate fluid flows iteratively using discrete representations. It can be adopted for a large number of flows simulations using simple operation rules. In the experiments, it was considered a three-dimensional version of the method, with 19 discrete directions of propagation (D3Q19). Performance evaluation compare three modern GPUs: K20M, K80, and Titan X; and two architectures of Xeon Phi: Knights Corner (KNC) and Knights Landing (KNL). Titan X provides the fastest execution time of all hardware considered. The results show that GPUs offer better processing time for the application. A KNL cache implementation presents the best results for Xeon Phi architectures and the new Xeon Phi (KNL) is two times faster than the previous model (KNC).

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