Today’s data bonanza and increasing computational power provide many new opportunities for combining observations with sophisticated simulation results to improve complex models and make forecasts by analyzing their relationships. This should lead to well-presented actionable information that can support decisions and contribute trustworthy knowledge. Practitioners in all disciplines: computational scientists, data scientists and decision makers need improved tools to realize such potential. The Python library dispel4py is such a tool. It delivers a simple abstract model in familiar development environments with a fluent path to production use that automatically addresses scale without its users having to reformulate their methods. This depends on optimal mappings to many current HPC and data-intensive platforms.
|Title of host publication||Conquering Big Data Using High Performance Computing|
|Number of pages||29|
|Publication status||Published - 17 Sep 2016|