English

On the performance of GPU accelerated q-LSKUM based meshfree solvers in Fortran, C++, Python, and Julia

Programming Languages 2023-05-03 v1 Performance Computational Physics

Abstract

This report presents a comprehensive analysis of the performance of GPU accelerated meshfree CFD solvers for two-dimensional compressible flows in Fortran, C++, Python, and Julia. 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 assess the computational efficiency of the GPU solvers and to compare their relative performance, benchmark calculations are performed on seven levels of point distribution. To analyse the difference in their run-times, the computationally intensive kernel is profiled. Various performance metrics are investigated from the profiled data to determine the cause of observed variation in run-times. To address some of the performance related issues, various optimisation strategies are employed. The optimised GPU codes are compared with the naive codes, and conclusions are drawn from their performance.

Keywords

Cite

@article{arxiv.2108.07031,
  title  = {On the performance of GPU accelerated q-LSKUM based meshfree solvers in Fortran, C++, Python, and Julia},
  author = {Nischay Ram Mamidi and Kumar Prasun and Dhruv Saxena and Anil Nemili and Bharatkumar Sharma and S. M. Deshpande},
  journal= {arXiv preprint arXiv:2108.07031},
  year   = {2023}
}

Comments

42 pages, 3 figures

R2 v1 2026-06-24T05:08:52.402Z