A higher-order Godunov method for multidimensional ideal magnetohydrodynamics
SIAM Journal on Scientific Computing
An approximate Riemann solver for ideal magnetohydrodynamics
Journal of Computational Physics
Extension of the piecewise parabolic method to multidimensional ideal magnetohydrodynamics
Journal of Computational Physics
Notes on the eigensystem of magnetohydrodynamics
SIAM Journal on Applied Mathematics
Shock-capturing approach and nonevolutionary solutions in magnetohydrodynamics
Journal of Computational Physics
A High-Order Godunov-Type Scheme for Shock Interactions in Ideal Magnetohydrodynamics
SIAM Journal on Scientific Computing
Parallel programming: techniques and applications using networked workstations and parallel computers
A Parallel Algorithm to Evaluate Chebyshev Series on a Message Passing Environment
SIAM Journal on Scientific Computing
Interpretive performance prediction for parallel application development
Journal of Parallel and Distributed Computing
Efficient Algebraic Multigrid Algorithms and Their Convergence
SIAM Journal on Scientific Computing
Finite Difference WENO Schemes with Lax--Wendroff-Type Time Discretizations
SIAM Journal on Scientific Computing
Code Optimizations for Complex Microprocessors Applied to CFD Software
SIAM Journal on Scientific Computing
Scalability of hybrid programming for a CFD code on the earth simulator
Parallel Computing
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This paper presents a dynamic domain decomposition (D^3) technique for implementing the parallelization of the piecewise parabolic method (PPM) for solving the ideal magnetohydrodynamics (MHD) equations. The key point of D^3 is distributing the work dynamically among processes during the execution of the PPM algorithm. This parallel code utilizes D^3 with a message passing interface (MPI) in order to permit efficient implementation on clusters of distributed memory machines and may also simultaneously exploit threading for multiprocessing shared address space architectures. 3D global MHD simulation results for the Earth's magnetosphere on the massively parallel supercomputers Deepcomp 1800 and 6800 demonstrate the scalability and efficiency of our parallelization strategy.