Analysis Of Self-Describing Gridded Geoscience Data using High Performance Cluster Computing

Abstract

Unidata's netCDF data model, data access libraries, and machine independent for-mat are widely used in the creation, access, and sharing of geoscience data. netCDF is widely used in earth, ocean, and atmospheric sciences because of its simple data model, ease of use, portability, and strong user support infrastructure.

The netCDF format provides a platform-independent binary representation for self-describing data in a form that permits efficient access to a small subset of a large data set, without first reading through all the preceding data. The format also allows appending data along one dimension without copying the data set or reddening its structure.

But a geoscience researcher cannot deal with programming environment, so to develop such an interface which can make reading and writing netCDF data possible for them is the scope of the project.

The interface needs to be implemented on high performance computing platforms to deal with large data _les which results into long delays or out of memory problem while serial execution. Java is to be used as High Performance Computation Language and JOMP for providing parallel programming interface.