  We introduce a high-performance simulation framework that permits the
semi-independent, task-based solution of sets of partial differential
equations, typically manifesting as updates to a collection of `patches' in
space-time. A hybrid MPI/OpenMP execution model is adopted, where work tasks
are controlled by a rank-local `dispatcher' which selects, from a set of tasks
generally much larger than the number of physical cores (or hardware threads),
tasks that are ready for updating. The definition of a task can vary, for
example, with some solving the equations of ideal magnetohydrodynamics (MHD),
others non-ideal MHD, radiative transfer, or particle motion, and yet others
applying particle-in-cell (PIC) methods. Tasks do not have to be grid-based,
while tasks that are, may use either Cartesian or orthogonal curvilinear
meshes. Patches may be stationary or moving. Mesh refinement can be static or
dynamic. A feature of decisive importance for the overall performance of the
framework is that time steps are determined and applied locally; this allows
potentially large reductions in the total number of updates required in cases
when the signal speed varies greatly across the computational domain, and
therefore a corresponding reduction in computing time. Another feature is a
load balancing algorithm that operates `locally' and aims to simultaneously
minimise load and communication imbalance. The framework generally relies on
already existing solvers, whose performance is augmented when run under the
framework, due to more efficient cache usage, vectorisation, local
time-stepping, plus near-linear and, in principle, unlimited OpenMP and MPI
scaling.
