CUDA-based ray tracing shows black hole shadows and emission rates vary with global monopole, charge, and rotation parameters but are insensitive to the Euler-Heisenberg nonlinearity, yielding observational bounds on those three quantities.
Up to 700k GPU cores, Kepler, and the Exascale future for simulations of star clusters around black holes
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abstract
We present direct astrophysical N-body simulations with up to a few million bodies using our parallel MPI/CUDA code on large GPU clusters in China, Ukraine and Germany, with different kinds of GPU hardware. These clusters are directly linked under the Chinese Academy of Sciences special GPU cluster program in the cooperation of ICCS (International Center for Computational Science). We reach about the half the peak Kepler K20 GPU performance for our phi-GPU code [2], in a real application scenario with individual hierarchically block time-steps with the high (4th, 6th and 8th) order Hermite integration schemes and a real core-halo density structure of the modeled stellar systems. The code and hardware are mainly used to simulate star clusters [23, 24] and galactic nuclei with supermassive black holes [20], in which correlations between distant particles cannot be neglected.
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Machine learning constrains non-commutative black hole parameters and reports consistency with Sgr A* Keck observations.
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On Computational CUDA Studies of Black Hole Shadows
CUDA-based ray tracing shows black hole shadows and emission rates vary with global monopole, charge, and rotation parameters but are insensitive to the Euler-Heisenberg nonlinearity, yielding observational bounds on those three quantities.
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Constraining Black Hole Parameters in Non-Commutative Geometry using Machine Learning
Machine learning constrains non-commutative black hole parameters and reports consistency with Sgr A* Keck observations.