Distributed computation for a hypercube network of sensor-driven processors with communication delays including setup time

In this paper, the problem of optimal distribution of measurement data to be processed in minimal time on a hypercube network of sensor driven processors is considered. An analytical model is developed for solving the problem efficiently. Unlike the previous models, this model considers: 1) explicitly the setup time which constrains exploiting all the available processors; 2) simultaneous use of links to expedite the communication; 3) partial solution combining time to encompass wider class of related problems. By deriving a lower bound on the amount of data to be received by a processor for efficient distribution, a new technique called fractal hypercube is introduced here to get the optimal solution with fewer processors, An optimal iterative method for hypercubes and a near-optimal recursive method with a refinement are presented for the same with the analysis. The effect of varying the originating processor and the choice of fractal hypercube are discussed with an effective technique called processor isomorphism. This study reveals that always the fractal hypercubes outperform the other two methods, the optimal iterative method for hypercubes and the near-optimal method