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

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 en...

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Vydáno v:Proceedings - International Conference on High Performance Computing s. 156 - 165
Hlavní autoři: Mamidi, Nischay Ram, Saxena, Dhruv, Prasun, Kumar, Nemili, Anil, Sharma, Bharatkumar, Deshpande, S. M.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2022
Témata:
Benchmark testing
Code optimisation
Codes
CUDA
Entropy
Fortran
GPUs
Graphics processing units
Julia
Kinetic theory
LSKUM
Measurement
Mesh-free methods
Performance analysis
Python
ISSN:2640-0316
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Shrnutí: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.
ISSN:2640-0316
DOI:10.1109/HiPC56025.2022.00031