Researcher Receives $1 Million NSF Grant to Devise New Supercomputing Model
More and different types of data could be handled at one time to avoid data bottlenecks.
A Texas Tech University computer scientist received a $1 million grant from the National Science Foundation (NSF) to create a faster, better method for supercomputing.
Yong Chen, an assistant professor of computer science and director of the Data-Intensive Scalable Computing Lab, will lead a team of researchers to develop a new concept called “compute on data path” toward “data-centric” computing that assimilates and analyzes more and different types of data used in scientific discovery and does so all at one time.
“This is a sizable grant awarded from a very competitive NSF core program, and we deeply appreciate the support to our work from the NSF and the recognition from our peers,” Chen said. “We are primarily doing research on trying to address data-intensive scientific computing needs to create ‘data-centric computing’ for better scientific discovery and innovation.”
Chen said he and other scientists will lay groundwork for a new data assimilation computing concept capable of combining data that may not be similar.