Emina Soljanin received a new NSF grant titled Collaborative Research: CIF: Small: Maximizing Coding Gain in Coded Computing. It is a collaboration between Rutgers and Texas A&M University. Rutgers is the project's lead institution, with a share of $375K. (Total award is $600K over three years.)
Artificial intelligence and machine learning algorithms rely on parallel, distributed computing systems to efficiently carry out intricate, data-heavy tasks. A significant challenge in designing large-scale distributed computing systems is addressing the unpredictable variations in service times across multiple servers. Computing redundancy, such as task replication, is a promising powerful tool to curtail the overall variability in service time. This project focuses on the intelligent management of redundancy in distributed computing that will affect the execution efficiency of data-intensive algorithms in large-scale systems.
More information is available at
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2327509&HistoricalAwards=false