Data Management for Exascale Scientific ApplicationsMy current research focuses on optimizing coupled scientific workflow applications on high-end computing platforms. My work enables in-situ/in-transit execution of user-defined data analysis operations as part of the coupled simulation-analysis workflow, and employs the data-aware task mapping and scheduling approach to reduce the amount of network data movement.
ActiveSpaces: Exploring dynamic code deployment for extreme scale data processing”,
In Concurrency and Computation: Practice and Experience 2014 , CCPE November 2014.
Scalable Run-time Data Indexing and Querying for Scientific Simulations,
In Big Data Analytics: Challenges and Opportunities (BDAC-14) Workshop at Supercomputing Conference, New Orleans, Louisiana, U.S.A., November, 2014.
In-situ feature-based objects tracking for data-intensive scientific and enterprise analytics workflows,
In Cluster Computing , September 2014.
Leveraging Deep Memory Hierarchies for Data Staging in Coupled Data Intensive Simulation Workflows,
In IEEE Cluster 2014 , Madrid, Spain, September, 2014.
Using Cross-Layer Adaptations for Dynamic Data Management in Large Scale Coupled Scientific Workflows,
ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC) , Denver, Colorado, U.S.A., November, 2013.
In-situ Feature-based Objects Tracking for Large-Scale Scientific Simulations,
International Workshop on Data-Intensive Scalable Computing Systems, SC12 November, 2012, Salt Lake City, Utah, USA.
XpressSpace: A Programming Framework for Coupling PGAS Simulation Codes,
Concurrency and Computation: Practice and Experience, 2013.