Researchers at North Carolina State University have developed a new technique for using multi-core chips more efficiently, significantly enhancing a computer's ability to build computer models of biological systems. The technique improved the efficiency of algorithms used to build models of biological systems more than seven-fold, creating more realistic models that can account for uncertainty and biological variation. This could impact research areas ranging from drug development to the engineering of biofuels.
Computer models of biological systems have many uses, from predicting potential side-effects of new drugs to understanding the ability of plants to adjust to climate change. But developing models for living things is challenging because, unlike machines, biological systems can have a significant amount of uncertainty and variation.
"When developing a model of a biological system, you have to use techniques that account for that uncertainty, and those techniques require a lot of computational power," says Dr. Cranos Williams, an assistant professor of electrical engineering at NC State and co-author of a paper describing the research. "That means using powerful computers. Those computers are expensive, and access to them can be limited.
"Our goal was to develop software that enables scientists to run biological models on conventional computers by utilizing their multi-core chips more efficiently."
The brain of a computer chip is its central processing unit, or "core." Most personal computers now use chips that have between four and eight cores. However, most programs only operate in one core at a time. For a program to utilize all of these cores, it has to be broken down into separate "threads" so that each core can execute a different part of the program simultaneously. The process of breaking down a program into threads is called parallelization, and allows computers to run programs very quickly.
|Contact: Matt Shipman|
North Carolina State University