Located in the Rensselaer Technology Park, the Center for Computational Innovations (CCI) is the heart of the university's high performance computing activities, serving as a vital resource for a broad range of Rensselaer researchers and research partners from academe and industry. CCI is home to two of the university's most famous non-carbon residents: AMOS and Watson at Rensselaer.
The Advanced Multiprocessing Optimized System, or AMOS, is the new petascale supercomputing system at Rensselaer. With the ability to perform more than one quadrillion (1015) calculations per second, the five-rack IBM Blue Gene/Q supercomputer is the most powerful university-based supercomputer in New York state and the Northeast, and among the most powerful in the world. In addition to massive computational power, AMOS has high-performance networking capabilities with a bandwidth of more than four terabytes per second-more than the combined bandwidth of 2 million home Internet subscribers.
AMOS's CCI "roommate" is Watson at Rensselaer, a version of the IBM cognitive computing machine that became famous in 2011 for appearing on national television and besting the all-time champions of the game show Jeopardy. IBM provided the system to Rensselaer earlier this year, making Rensselaer the first university in the world to receive such a system. Watson at Rensselaer has a unique ability to understand the nuances of human language and sift through vast amounts of data.
The Rensselaer IDEA aims to leverage the computational horsepower and unprecedented networking capabilities of AMOS, along with Watson at Rensselaer's unique language skills and capacity to sleuth through unstructured data, to develop new technologies that enable Rensselaer faculty and students to work with data, whether in traditional databases or in documents on the Internet, at larger scales and in exciting new ways.
One example of this is agent-based modeling-or computational models that can simulate the effects, on a system, of the decision-making of individuals. Creating an agent-based model of the population of Manhattan, for example, could provide insight into the way human dynamics affect the evacuation of the borough following a disaster, and save many lives. Agent-based modeling would also inform the creation of a smart electric grid that takes into very detailed account the various individual human responses to price incentives.
To learn more, visit http://cci.rpi.edu/