Middleware for large scale in situ analytics workflows

In situ;High performance computing;Big data;Code coupling;Workflows

Dayal, Jai ; Wolf, Matthew Computer Science Gavrilovsk, Ada Lofstead, Gerald Pande, Santosh Liu, Ling ; Wolf, Matthew

University:Georgia Institute of Technology

Department:Computer Science

关键词: In situ;    High performance computing;    Big data;    Code coupling;    Workflows;   

Others  :  https://smartech.gatech.edu/bitstream/1853/56355/1/DAYAL-DISSERTATION-2016.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

The trend to exascale is causing researchers to rethink the entire computa- tional science stack, as future generation machines will contain both diverse hardware environments and run times that manage them. Additionally, the science applications themselves are stepping away from the traditional bulk-synchronous model and are moving towards a more dynamic and decoupled environment where analysis routines are run in situ alongside the large scale simulations. This thesis presents CoApps, a middleware that allows in situ science analytics applications to operate in a location-flexible manner. Additionally, CoApps explores methods to extract information from, and issue management operations to, lower level run times that are managing the diverse hardware expected to be found on next generation exascale machines. This work leverages experience with several extremely scalable applications in materials and fusion, and has been evaluated on machines ranging from local Linux clusters to the supercomputer Titan.