*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Title: Middleware for Large Scale in Situ Analytics Workflows
Jai Dayal
School of Computer Science
College of Computing
Georgia Institute of Technology
Date: Friday, October 28th, 2016
Time: 11:00am
Location: KACB 3402
Committee:
---------
Dr. Matthew Wolf (Advisor and Committee Chair, Georgia Tech and Oak Ridge National Laboratory)
Dr. Karsten Schwan (Advisor, School of Computer Science, Georgia Tech)
Dr. Ling Liu (School of Computer Science, Georgia Tech)
Dr. Santosh Pande (School of Computer Science, Georgia Tech)
Dr. Ada Gavrilovska (School of Computer Science, Georgia Tech)
Dr. Jay Lofstead (Computer Science Research Institute, Sandia National Laboratories)
Abstract:
--------
The trend to exascale is causing researchers to rethink the entire computational 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 analytics applications to operate in a
location-flexible manner. CoApps also explores methods to extract information from, and issue management operations
to, lower level run times that are managing the diverse hardware expected to be operating 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.
