Computational Aspects of Applying Multicore CPU and Graphics Processing Unit for Solving Reynolds Equations

• Main Authors: Kuan Lun Kao, 高冠倫
• Other Authors: N. Z. Wang
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/6ztr66

碩士 === 長庚大學 === 機械工程學系 === 105 === Fluid-film lubricated bearings are the key elements in many rotating machinery and machine tools. The performance of a bearing can usually be predicted by solving a proper Reynolds equation which is consistent with the operation conditions of that bearing. The computational capability of multicore CPUs and GPUs (graphics processing units) are improved exponentially in recent years and parallel computing is the only means to effectively use the advanced computing power. In this study, the SOR (successive-over-relaxation) type methods are applied to solve the incompressible- and compressible-fluid Reynolds equations by either the multicore CPU or GPUs. The computing paradigms used are OpenACC and OpenMP, which are the standards for multithreaded and GPU computing. The results of serial and parallel versions of the SOR type methods are presented and compared. A stopping criterion based on the truncation error analysis is used to terminate the SOR iterations with sufficient accuracy and efficiency. To speed up the computation a CPU-GPU hybrid setup with simultaneously 2 GPUs computing is tested and the simulation results are also presented and discussed. The simulation results show that this study can successfully apply the multicore processor and two GPUs to perform the parallel computing for solving the Reynolds equations. A similar setup and coding can be used in the future to increase the computing capability for fluid-film lubrication analysis.