CERCS Technical Reports

GIT-CERCS-10-01

PreDatA - Preparatory Data Analytics on Peta-Scale Machines

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 the massive datasets while data is being produced by simulations. PreDatA, short for Preparatory Data Analytics, is an approach for 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 peta-scale machine as staging nodes and staging simulation's output data through these nodes, PreDatA can exploit their computational power to perform selected data manipulations with lower latency than attainable by first moving data into file systems and storage. Such in-transit manipulations are supported by the PreDatA middleware through RDMA-based 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. As a result, PreDatA enhances the scalability and flexibility of 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 simulation models. Performance evaluations with several production peta-scale applications on Oak Ridge National Laboratory's Leadership Computing Facility demonstrate the feasibility of the PreDatA approach, including its minimal impact on the total execution time of large-scale simulations. They also demonstrate advantages derived from using PreDatA, such as in-transit data reorganizations able to hide write latency by 99.9% and improve total simulation time by 2.7% and the ability to generate online insights into the 260GB data being output from 16384 compute cores in 40 seconds. In addition, online data reorganization is shown to improve read performance by 10 times with only 1.5% additional resource usage at simulation time.