GRASS GIS on High Performance Computing with MPI, OpenMP and Ninf-G Programming Framework

GRASS GIS (Geographical Resources Analysis Support System) is a free, open source software and has been used for RemoteSensing (RS) and Geographic Information System (GIS) data analysis and visualization. Inside GRASS, different modules have beendeveloped for processing satellite images. Currently, GRASS uses databases to handle large datasets and the performance andcapabilities of GRASS for large datasets can be greatly improved by integrating GRASS modules with parallel and distributedcomputing. Multi computer based distributed systems (clusters and Grids) have a large processing capacity for a lower cost, naturally,choice turns towards developing High Performance Computing (HPC) applications. However, it is not an easy job to port GRASSmodules directly to HPC environment. The developers of satellite image processing applications need to solve the problem of both data and task distribution, or how to distribute data and tasks among single or multiple clusters environment. The workload in HPC,the bandwidth, the processors speed, parameters of evaluation methods and data size are additional concerning factors. GRASSmodules, i.e. i) 'i.vi' is developed by Kamble and Chemin (2006) to process 13 vegetation indices, ii) 'i.lmf' is developed by Akhteret al. (2008) to remove the atmospheric effects from RS images and iii) 'r.gaswap' is developed by Akhter et al. (2006) to find outthe crop parameters those are not directly visible from RS images, will be discussed as three case studies to developed GRASSmodule framework on HPC. Developing the methodology, which enables to run GRASS GIS environment for RS images processingon HPC systems, will be the main concerning issue of this paper. Additionally, different implementations for distributed GRASSmodels will be discussed on three different programming platforms (MPI, Ninf-G and OpenMP)and their performance will also be presented in this paper.