Cost-effective parallel processing for remote sensing applications

Network-based computing with cluster of personal computers or workstations recently has become an important and successful technique. Network-based computing enables fast computation and resolves short of storage with computers on the desks. A cluster of inexpensive computers offers them aggregated computing power and storage to challenge very large-scale problems. Iterative solvers are used for the large sparse linear systems. Preconditioners accelerate the rate of convergence of the iterative solvers. However, parallelization of preconditioners is far from satisfactory so far. Thus, the simplest diagonal scaling is a good alternative for improved convergence and easy parallelization. In order to reduce communication overheads, a data compression technique is considered. Data communication time can be reduced at the cost of convergence rate, MATLAB simulation result is given in this paper. This method is effective to cluster of computers connected through slow networks