Pothen, Gleich, and Khan Present Research at SC12
On Wednesday, November 14, 2012, researchers from Purdue Computer Science, along with a collaborator from Pacific Northwest National Laboratory (PNNL), presented a talk at this year's Supercomputing conference, SC12. The annual gathering, held this year in Salt Lake City, is the international conference for high-performance computing, networking, storage and analysis.
The presentation "A Multithreaded Algorithm for Network Alignment" (PDF) detailed research conducted by CS graduate student Arif Khan, Assistant Professor David Gleich, Professor Alex Pothen, and Mahantesh Halappanavar of PNNL.
PNNL's press release explains the presentation's content further:
Data is everywhere these days. Biologists sift through vast amounts of error-prone data to understand how our cells work. Even librarians slog through mountains of information to better understand the materials they catalog. The key to comprehending today's information explosion is finding meaningful patterns buried in the data — and then finding comparable data patterns in other, related sources. This technique is called network alignment. Computational scientists at Purdue University and the Department of Energy's Pacific Northwest National Laboratory (PNNL) have developed new methods to identify similar patterns in any type of data. Their procedures help find proteins that act the same in humans and mice, and help find ideas that act the same for librarians and Wikipedia editors.
The existing methods used to solve these kinds of problems have been too slow to cope with the growing amount of data, prompting the PNNL and Purdue team to make them faster. To do this, they developed a new algorithm that uses an approach called approximate matching, which saves time by matching nearly identical patterns instead of exactly identical ones. They also developed new computer implementations that enabled the algorithm to use all a computer's processors in parallel to quickly identify relationships between two different networks. Tests using both of these improvements showed that the algorithm found similar interactions between thousands of proteins in two species in just seconds and found comparable ideas between hundreds of thousands of topics in library systems and Wikipedia entries in less than a minute.