This paper shows methodology, which enables profiling macro data flow graphs (
) that represent computation and communication patterns for the Finite Difference Time Domain (
) problem in irregular computational areas.
optimization is performed in three phases: simulation area partitioning with generation of initial
, macro data nodes merging with static load balancing to obtain given number of macro nodes and communication optimization to minimize (balance) inter-node data transmissions, computational cells redeployment to take into account computational system restrictions. Efficiency of computations for several communication systems (
) is discussed. Experimental results obtained by simulation are presented.