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

TL;DR

This paper evaluates the performance of GPU-accelerated meshfree CFD solvers based on q-LSKUM across four programming languages, analyzing efficiency, profiling kernels, and applying optimizations to improve computational speed.

Contribution

It provides a comprehensive performance comparison and optimization analysis of GPU-based meshfree solvers implemented in Fortran, C++, Python, and Julia.

Findings

GPU codes show significant performance variation across languages.

Profiling reveals key bottlenecks in kernel execution.

Optimizations improve GPU solver efficiency notably.

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.