Abstract
This paper presents FleCSPHg, a GPU accelerated framework dedicated to Smoothed Particle Hydrodynamics (SPH) and gravitation (FMM) computation. Astrophysical simulations, with the case of binary neutron stars coalescence, are used as test cases. In this context we show the efficiency of the tree data structure in two conditions. The first for near-neighbors search with SPH and the second with N-body algorithm for the gravitation computation.
FleCSPHg is based on FleCI and FleCSPH developed at the Los Alamos National Laboratory. This work is a first step to provide a multi-physics framework for tree-based methods.
This paper details either SPH, FMM methods and the simulation we propose. It describes FleCSI and FleCSPH and our strategy to divide the work load between CPU and GPU. The CPU is associate with the tree traversal and generates tasks at a specific depth for the GPU. These tasks are offloaded to the GPU and gathered on the CPU at the end of the traversal.
The computation time is up to 3.5 times faster on the GPU version than classical CPU. We also give details on the simulation itself for the binary neutron star coalescence.
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References
Abbott, B.P., et al.: GW170817: observation of gravitational waves from a binary neutron star inspiral. Phys. Rev. Lett. 119(16), 161101 (2017)
Barnes, J., Hut, P.: A hierarchical O(N log N) force-calculation algorithm. Nature 324(6096), 446–449 (1986)
Barnes, J.E.: A modified tree code: don’t laugh; it runs. J. Comput. Phys. 87(1), 161–170 (1990)
Bauer, M., Treichler, S., Slaughter, E., Aiken, A.: Legion: expressing locality and independence with logical regions. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, p. 66. IEEE Computer Society Press (2012)
Beatson, R., Greengard, L.: A short course on fast multipole methods. Wavelets Multilevel Methods Elliptic PDEs 1, 1–37 (1997)
Bergen, B., Moss, N., Charest, M.R.J.: Flexible computational science infrastructure. Technical report, Los Alamos National Laboratory (LANL), Los Alamos, NM, United States (2016)
Gingold, R., Monaghan, J.: Kernel estimates as a basis for general particle methods in hydrodynamics. J. Comput. Phys. 46(3), 429–453 (1982)
Gingold, R.A., Monaghan, J.J.: Smoothed particle hydrodynamics: theory and application to non-spherical stars. Mon. Not. R. Astron. Soc. 181(3), 375–389 (1977)
Hopkins, P.F.: Gizmo: multi-method magneto-hydrodynamics+ gravity code. Astrophysics Source Code Library (2014)
Kale, L.V., Krishnan, S.: CHARM++: a portable concurrent object oriented system based on C++. ACM SIGPLAN Not. 28, 91–108 (1993)
Landau, L.D., Lifshitz, E.M.: Fluid mechanics (1959)
Lucy, L.B.: A numerical approach to the testing of the fission hypothesis. Astron. J. 82, 1013–1024 (1977)
Miki, Y., Umemura, M.: Gothic: Gravitational Oct-Tree code accelerated by hierarchical time step controlling. New Astron. 52, 65–81 (2017)
Monaghan, J., Gingold, R.: Shock simulation by the particle method SPH. J. Comput. Phys. 52(2), 374–389 (1983). https://doi.org/10.1016/0021-9991(83)90036-0. http://www.sciencedirect.com/science/article/pii/0021999183900360
Rosswog, S.: Astrophysical smooth particle hydrodynamics. New Astron. Rev. 53(4), 78–104 (2009). https://doi.org/10.1016/j.newar.2009.08.007. http://www.sciencedirect.com/science/article/pii/S1387647309000487
Springel, V.: The cosmological simulation code GADGET-2. Mon. Not. R. Astron. Soc. 364(4), 1105–1134 (2005)
Wadsley, J.W., Keller, B.W., Quinn, T.R.: Gasoline2: a modern smoothed particle hydrodynamics code. Mon. Not. R. Astron. Soc. 471(2), 2357–2369 (2017)
Warren, M.S.: 2HOT: an improved parallel hashed Oct-Tree N-body algorithm for cosmological simulation. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, p. 72. ACM (2013)
Yokota, R., Barba, L.A.: Treecode and fast multipole method for N-body simulation with CUDA. In: GPU Computing Gems Emerald Edition, pp. 113–132. Elsevier (2011)
Acknowledgement
We would like to thanks the ROMEO supercomputer center on which all the tests below were performed. This work is part of the FleCSI and FleCSPH development. We would like to thanks the Los Alamos National Laboratory and the CCS-7 for the contributions on this work.
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Loiseau, J., Alin, F., Jaillet, C., Krajecki, M. (2019). FleCSPHg: A GPU Accelerated Framework for Physics and Astrophysics Simulations. In: Meneses, E., Castro, H., Barrios Hernández, C., Ramos-Pollan, R. (eds) High Performance Computing. CARLA 2018. Communications in Computer and Information Science, vol 979. Springer, Cham. https://doi.org/10.1007/978-3-030-16205-4_10
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