Modeling and identifying bottlenecks in EOSDIS

Many parallel application areas that exploit massive parallelism, such as climate modeling, require massive storage systems for the archival and retrieval of data sets. As such, advances in massively parallel computation must be coupled with advances in mass storage technology in order to satisfy I/O constraints of these applications. We demonstrate the effects of such I/O-computation disparity for a representative distributed information system, NASA´s Earth Observing System Distributed Information System (EOSDIS). We use performance modeling to identify bottlenecks in EOSDIS for two representative user scenarios from climate change research