Computer systems are being tasked with addressing a proliferation of graph-based, data intensive problems in areas ranging from medical informatics and social networks. As a result, there has been an ongoing emphasis on research that addresses these types of problems.
A four-year National Science Foundation project is taking aim at developing a new computer system that will focus on solving complex graph-based problems that will push supercomputing into the exascale era.
At the root of the project is Jeanine Cook, an associate professor at New Mexico State University’s department of Electrical and Computer Engineering and director of the university’s Advanced Computer Architectre Performance and Simulation Laboratory.
Cook specializes in micro-architecture simulation, performance modeling and analysis, workload characterization and power optimization. In short, as Cook describes, she creates “software models of computer processor components and their behavior to use these models to predict and analyze performance of future designs.”
Her team has developed a model that could improve the way current systems work with large unstructured datasets using applications running on Sandia systems.
It was her work while on sabbatical with Sandia’s Algorithms and Architectures group in 2009 that led to the $2.7 million NSF collaborative project. Cook developed processor and simulation tools and statistical performance models that identified performance bottlenecks in Sandia applications.
As Cook explained during a recent interview:
“Our system will be created specifically for solving [graph-based] problems. Intuitively, I believe that it will be an improvement. These are the most difficult types of problems to solve, mainly because the amount of data they require is huge and is not organized in a way that current computers can use efficiently.”