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.
Exploring Data Staging Across Deep Memory Hierarchies for Coupled Data Intensive Simulation Workflows,
In Proc. of the 29th IEEE International Parallel and Distributed Processing Symposium (IPDPS'15) , Hyderabad, India, May 2015.
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.