Block data decomposition for data-parallel programming on a heterogeneous workstation network

The authors present a block data decomposition algorithm for two-dimensional grid problems. Their method includes local balancing to accommodate heterogeneous processors, and they characterize the conditions that must be met for their partitioning strategy to be of value. While they concentrate on the workstation network model of parallel processing because of its high communication costs and inherent heterogeneity, their method is applicable to other parallel architectures.<>