Abstract
In this study, the solution methods suitable for use in a workstation to solve the discretized incompressible and compressible fluid Reynolds equations are examined. The workstation used for computing consists of dual six-core central processing units (CPUs) and a 256-core graphics processing unit (GPU) dedicated for computing. To compare the computational performance, the Reynolds equations are solved by a parallel iterative method using multithreaded and GPU computing. Multithreaded computing is conducted by OpenMP directives and GPU computing is executed using either NVIDIA's compute unified device architecture (CUDA) programming or the accelerator programming model (APM). The lubrication models used are an inclined surface slider with or without a central recess and an air journal bearing. In GPU computing, both the CUDA and APM are subjected to communication latency to achieve efficient fine-grained computations. The results show that the performance of the less expensive GPU computing is close to that of multithreaded computing in which the grid sizes are large in the analyses. The performance of the OpenMP-like APM programming is compatible with CUDA computing in the slider cases but is significantly worse for air bearings. This study illustrates the workstation computing for lubrication analyses in which the computing devices can be multi-core CPUs or many-core GPUs.
Published Version
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