With a $9.2 million grant from Intelligence Advanced Research Projects Activity (IARPA), Prof. Andrew A. Chien will lead a team of University of Chicago computer science researchers building the UpDown Systema—a new approach that could speed up graph analytics a hundredfold.
Graph analytics is at the heart of some of today’s most exciting computational applications in science and technology. The organization of data into graphs—large networks of people, molecules, or locations connected by their interactions and relationships—can unleash powerful insights for ecommerce, scientific discovery, social networks, recommendation and search engines, and fraud or anomaly detection.
However, today’s computing architectures were not designed for graphs, and struggle with efficiency and scalability.
The grant from IARPA, the research arm of the U.S. intelligence community, will fund the development of the UpDown System, to speed up graph analytics. The effort will reinvent computer architecture, dramatically increasing efficiency and scalability for graph computing. Such a scope will be necessary to efficiently analyze the world’s largest graphs from social media, financial transactions, or Internet of Things device networks that contain billions or trillions of vertices and edges.
“Efficient and scalable computation over massive graph structures is the signature computing challenge for the next several decades,” said Chien, who is the William Eckhardt Distinguished Service Professor in the Department of Computer Science and Senior Computer Scientist at Argonne National Laboratory. “The UpDown architecture that we’ve invented has novel capabilities to both encode information efficiently and move it intelligently around the machine, both of which are critical for faster graph computing.”
Read more at UChicago News