Performance analysis of GPU accelerated meshfree q-LSKUM solvers in Fortran, C, Python, and Julia

Nischay Ram Mamidi, D. Saxena, K. Prasun, Anil Nemili, Bharatkumar Sharma, S. Deshpande
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引用次数: 1

Abstract

This paper presents a comprehensive analysis of the performance of Fortran, C, Python, and Julia based GPU accelerated meshfree solvers for compressible flows. The programming model CUDA is used to develop the GPU codes. The meshfree solver is based on the least squares kinetic upwind method with entropy variables (q-LSKUM). To measure the performance of baseline codes, benchmark calculations are performed. The codes are then profiled to investigate the differences in their performance. Analysing various performance metrics for the computationally expensive flux residual kernel helped identify various bottlenecks in the codes. To resolve the bottlenecks, several optimisation techniques are employed. Post optimisation, the performance metrics have improved significantly, with the C GPU code exhibiting the best performance.
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GPU加速无网格q-LSKUM求解器在Fortran, C, Python和Julia中的性能分析
本文全面分析了基于Fortran、C、Python和Julia的GPU加速可压缩流无网格求解器的性能。采用CUDA编程模型开发GPU代码。无网格求解基于带熵变量的最小二乘逆风动力学方法(q-LSKUM)。为了测量基准代码的性能,需要执行基准计算。然后对代码进行分析,以调查它们在性能上的差异。分析计算量大的通量残差核的各种性能指标有助于识别代码中的各种瓶颈。为了解决瓶颈,采用了几种优化技术。优化后,性能指标有了显著改善,C GPU代码表现出最佳性能。
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