OMPEGAS: Optimized Relativistic Code for Multicore Architecture

Elena N. Akimova, Vladimir E. Misilov, Igor M. Kulikov and Igor G. Chernykh
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Elena N. Akimova: Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
Vladimir E. Misilov: Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
Igor M. Kulikov: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090 Novosibirsk, Russia
Igor G. Chernykh: Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090 Novosibirsk, Russia

Mathematics, 2022, vol. 10, issue 14, 1-12

Abstract: The paper presents a new hydrodynamical code, OMPEGAS, for the 3D simulation of astrophysical flows on shared memory architectures. It provides a numerical method for solving the three-dimensional equations of the gravitational hydrodynamics based on Godunov’s method for solving the Riemann problem and the piecewise parabolic approximation with a local stencil. It obtains a high order of accuracy and low dissipation of the solution. The code is implemented for multicore processors with vector instructions using the OpenMP technology, Intel SDLT library, and compiler auto-vectorization tools. The model problem of simulating a star explosion was used to study the developed code. The experiments show that the presented code reproduces the behavior of the explosion correctly. Experiments for the model problem with a grid size of 128 × 128 × 128 were performed on an 16-core Intel Core i9-12900K CPU to study the efficiency and performance of the developed code. By using the autovectorization, we achieved a 3.3-fold increase in speed in comparison with the non-vectorized program on the processor with AVX2 support. By using multithreading with OpenMP, we achieved an increase in speed of 2.6 times on a 16-core processor in comparison with the vectorized single-threaded program. The total increase in speed was up to ninefold.

Keywords: computational astrophysics; type II supernova; high performance computing; OpenMP; SIMD (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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