Massive parallelization has lead to a dramatic increase in available computational power. However, data transfer speeds have failed to keep pace and are the major limiting factor in the development of exascale computing. New algorithms must be developed which minimize the transfer of data. Patch dynamics is a computational macroscale modeling scheme which provides a coarse macroscale solution of a problem defined on a fine microscale by dividing the domain into many nonoverlapping, coupled patches. Patch dynamics is readily adaptable to massive parallelization as each processor can evaluate the dynamics on one, or a few, patches. However, patch coupling conditions interpolate across the unevaluated parts of the domain between patches, and are typically reevaluated at every microscale time step, thus requiring almost continuous data transfer. We propose a modified patch dynamics scheme which minimizes data transfer by only reevaluating the patch coupling conditions at "mesoscale" time scales which are significantly larger than the microscale time of the microscale problem. We analyze the error arising from patch dynamics with mesoscale temporal coupling as a function of the mesoscale time interval, patch size, and ratio between the microscale and macroscale.
All Science Journal Classification (ASJC) codes
- Computational Mathematics
- Applied Mathematics
- Dynamical systems
- Multiscale modeling