Abstract
Recently, the phenomenon which quantifies the agricultural and water management practices from remote sensing (RS) imagery, has been adapted to help the policy makers and farm/water managers to make better operational decisions. SWAP-GA is a combined model of the SWAP (Soil Water Atmosphere and Plant) crop model and the Remote Sensing (RS) data assimilation technique, which is optimized by Genetic Algorithm (GA). However, to run the SWAP-GA model on a single PC requires a massive amount of processing time. Based on the above observation, distributed or parallel computing can be a preeminent and convincing solution. At present, Multi-cluster Grids have emerged as the most popular type of distributed computing system. However, the performances of different parallelization methodologies on the Grid have not been discussed thoroughly. This paper presents the implementation of the SWAP-GA application and discusses its impact on the performance of parallelization methods.
