A Framework of High-Performance Computation Algorithms for Processing Satellite Remote Sensing Data

Abstract

In this paper, we propose a framework for processing satellite remote sensing image data with high performance computation algorithms, which utilizes heterogeneous resources to harness enormous computing power and functions in a distributed way. Under the framework, we implemented a workload partitioning algorithm, combining static planning and dynamic partitioning, devised to realize the loadbalanced data parallelism for generic task workload. The algorithms for task granularity controlling strategy, locality awareness processing, and fault tolerance processing are designed. The experimental results demonstrated that the ability of the framework to securely process satellite data on distributed machines, and the computational performance which has been improved with the high performance algorithms.