  Tumor growth from a single transformed cancer cell up to a clinically
apparent mass spans many spatial and temporal orders of magnitude.
Implementation of cellular automata simulations of such tumor growth can be
straightforward but computing performance often counterbalances simplicity.
Computationally convenient simulation times can be achieved by choosing
appropriate data structures, memory and cell handling as well as domain setup.
We propose a cellular automaton model of tumor growth with a domain that
expands dynamically as the tumor population increases. We discuss memory
access, data structures and implementation techniques that yield
high-performance multi-scale Monte Carlo simulations of tumor growth. We
present simulation results of the tumor growth model and discuss tumor
properties that favor the proposed high-performance design.
