Researchers are developing novel computational techniques that could lead to better simulation of complex systems, such as the spread of diseases, the evolution of financial markets, and the flow of Internet traffic.

With a $450,000 grant from the National Science Foundation, Gyorgy Korniss, assistant professor of physics, will use a computational technique called Parallel Discrete-Event Simulation (PDES) to model large-scale systems, where events occur randomly in space and time.

What makes Korniss' work unique is that he uses naturally occurring systems to help understand and develop the sophisticated algorithms necessary for this modeling process. The physical surface growth of crystals, for example, can be likened to the evolution of events in a large class of other systems because they have similar asynchronous, or random, characteristics. Comparing these natural systems with the simulated time horizon developed by the researchers can help explain how advanced algorithms work and how they can be optimized for the large-scale systems.

Because these systems are so large, the modeling and simulation would normally be a slow process. Korniss and his colleagues can speed up the process by breaking down the system and distributing it over many processors.

Once the system is broken down, the challenge lies with accurately preserving the random nature of the evolution of the physical system being modeled, said Korniss. In order to accomplish this, sophisticated algorithms are used to program each processor to simulate a random time stream. When linked together, these streams constitute the simulated time horizon for the entire system.

Korniss is working with Mark Novotny in the department of physics and astronomy at Mississippi State University and collaborating with researchers at Lucent Technologies.

Originally published in Campus.News, November 2001

Published November 1, 2001