With the advance of technology, geographic raster data's amount increases continuouslly. Single process cannot process large raster data efficiency, so it is necessary to adopt parallel processing. Traditional development method mixes algorithm, processes scheduling, memory management and data I/O together, thus it presents higher requirements for programmers and the code quality is difficult to control. This study proposes a Huge Geographic Raster Data Parallel Processing Framework (HGRDPPF). With the use of core class's real read and virtual read method, framework can achieve a large raster data's fast loading and writing by steps or blocks, and can achieve parallel task scheduling, data transfer and specific algorithm stage into tasks; through this framework, the raster file is split into sub-tasks according to the ability of computer in the cluster, and separate the raster processing algorithm from MPI API, disk IO and logic, developers can concentrate onto the algorithm itself, and achieve higher program quality. Experiments show that this framework can significantly reduce the amount of code while improving software quality, and to achieve a better parallel performance.