Image partitioning using system characteristics in heterogeneous computing systems

Many image processing tasks can be computed efficiently in a single program multiple data (SPMD) fashion on massively parallel systems. Although executing SPMD tasks on coarse-grained heterogeneous systems yields a cost-effective solution, heterogeneity introduces more complexity in data partitioning, mapping, and scheduling problems. In this paper, three image data partitioning schemes for parallel image processing in heterogeneous systems are investigated and implemented using the parallel virtual machine message passing library. The partitioning schemes are based on the system characteristics (processing capability) incorporated within the distributed computing primitives (DCP) environment using SpecInt92 benchmark and our DCP-based benchmark. We compare the results with the baseline (Eq-based) scheme that equally partitions images regardless of processing speed. The results from the experiments show that the DCP-based partitioning scheme outperforms the Eq-based and Spec-based schemes