PreDatA – preparatory data analytics on peta-scale machines

Abstract

Peta-scale scientific applications running on High End Computing (HEC) platforms can generate large volumes of data. For high performance storage and in order to be useful to science end users, such data must be organized in its layout, indexed, sorted, and otherwise manipulated for subsequent data presentation, visualization, and detailed analysis. In addition, scientists desire to gain insights into selected data characteristics `hidden' or `latent' in these massive datasets while data is being produced by simulations. PreDatA, short for Preparatory Data Analytics, is an approach to preparing and characterizing data while it is being produced by the large scale simulations running on peta-scale machines. By dedicating additional compute nodes on the machine as `staging' nodes and by staging simulations' output data through these nodes, PreDatA can exploit their computational power to perform select data manipulations with lower latency than attainable by first moving data into file systems and storage. Such intransit manipulations are supported by the PreDatA middleware through asynchronous data movement to reduce write latency, application-specific operations on streaming data that are able to discover latent data characteristics, and appropriate data reorganization and metadata annotation to speed up subsequent data access. PreDatA enhances the scalability and flexibility of the current I/O stack on HEC platforms and is useful for data pre-processing, runtime data analysis and inspection, as well as for data exchange between concurrently running simulations.