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OMPEGAS: Optimized Relativistic Code for Multicore Architecture

Author

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  • Elena N. Akimova

    (Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
    Department of Information Technologies and Control Systems, Institute of Radioelectronics and Information Technology, Ural Federal University, Mira Street 19, 620002 Ekaterinburg, Russia)

  • Vladimir E. Misilov

    (Krasovskii Institute of Mathematics and Mechanics, Ural Branch of RAS, S. Kovalevskaya Street 16, 620108 Ekaterinburg, Russia
    Department of Information Technologies and Control Systems, Institute of Radioelectronics and Information Technology, Ural Federal University, Mira Street 19, 620002 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)

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.

Suggested Citation

  • Elena N. Akimova & Vladimir E. Misilov & Igor M. Kulikov & Igor G. Chernykh, 2022. "OMPEGAS: Optimized Relativistic Code for Multicore Architecture," Mathematics, MDPI, vol. 10(14), pages 1-12, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2546-:d:868616
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    References listed on IDEAS

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    2. Ji-an Jiang & Mamoru Doi & Keiichi Maeda & Toshikazu Shigeyama & Ken’ichi Nomoto & Naoki Yasuda & Saurabh W. Jha & Masaomi Tanaka & Tomoki Morokuma & Nozomu Tominaga & Željko Ivezić & Pilar Ruiz-Lapue, 2017. "A hybrid type Ia supernova with an early flash triggered by helium-shell detonation," Nature, Nature, vol. 550(7674), pages 80-83, October.
    3. John C. Forbes & Mark R. Krumholz & Nathan J. Goldbaum & Avishai Dekel, 2016. "Suppression of star formation in dwarf galaxies by photoelectric grain heating feedback," Nature, Nature, vol. 535(7613), pages 523-525, July.
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