4.4 Article

IllinoisGRMHD: an open-source, user-friendly GRMHD code for dynamical spacetimes

Journal

CLASSICAL AND QUANTUM GRAVITY
Volume 32, Issue 17, Pages -

Publisher

IOP PUBLISHING LTD
DOI: 10.1088/0264-9381/32/17/175009

Keywords

GRMHD; magnetohydrodynamics; general relativity; black holes; neutron stars; gamma-ray bursts; relativistic astrophysics

Funding

  1. NSF Grant [PHY-1300903, PHY-1305682, OCI-1053575]
  2. NASA Grants [NNX13AH44G, 13-ATP13-0077]
  3. Simons Foundation
  4. Division Of Physics
  5. Direct For Mathematical & Physical Scien [1212460, 1300903] Funding Source: National Science Foundation

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In the extreme violence of merger and mass accretion, compact objects like black holes and neutron stars are thought to launch some of the most luminous outbursts of electromagnetic and gravitational wave energy in the Universe. Modeling these systems realistically is a central problem in theoretical astro-physics, but has proven extremely challenging, requiring the development of numerical relativity codes that solve Einstein's equations for the spacetime, coupled to the equations of general relativistic ( ideal) magnetohydrodynamics (GRMHD) for the magnetized fluids. Over the past decade, the Illinois numerical relativity (ILNR) group's dynamical spacetime GRMHD code has proven itself as a robust and reliable tool for theoretical modeling of such GRMHD phenomena. However, the code was written 'by experts and for experts' of the code, with a steep learning curve that would severely hinder community adoption if it were open-sourced. Here we present IllinoisGRMHD, which is an open-source, highly extensible rewrite of the original closed-source GRMHD code of the ILNR group. Reducing the learning curve was the primary focus of this rewrite, with the goal of facilitating community involvement in the code's use and development, as well as the minimization of human effort in generating new science. IllinoisGRMHD also saves computer time, generating roundoff-precision identical output to the original code on adaptive-mesh grids, but nearly twice as fast at scales of hundreds to thousands of cores.

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