01/30/2012

University of Texas at Austin

A big leap toward lowering the power consumption of microprocessors

The first systematic power profiles of microprocessors could help lower the energy consumption of both small cell phones and giant data centers, report computer science professors from The University of Texas at Austin and the Australian National University.

Their results may point the way to how companies like Google, Apple, Intel and Microsoft can make software and hardware that will lower the energy costs of very small and very large devices.

"The less power cell phones draw, the longer the battery will last," says Kathryn McKinley, professor of computer science at The University of Texas at Austin. "For companies like Google and Microsoft, which run these enormous data centers, there is a big incentive to find ways to be more power efficient. More and more of the money they're spending isn't going toward buying the hardware, but toward the power the datacenters draw."

McKinley says that without detailed power profiles of how microprocessors function with different software and different chip architectures, companies are limited in terms of how well they can optimize for energy usage.

The study she conducted with Stephen M. Blackburn of The Australian National University and their graduate students is the first to systematically measure and analyze application power, performance, and energy on a wide variety of hardware.

This work was recently invited to appear as a Research Highlight in the Communications of the Association for Computer Machinery (CACM). It's also been selected as one of this year's "most significant research papers in computer architecture based on novelty and long-term impact" by the journal IEEE Micro.

"We did some measurements that no one else had done before," says McKinley. "We showed that different software, and different classes of software, have really different power usage."

McKinley says that such an analysis has become necessary as both the culture and the technologies of computing have shifted over the past decade.

Source: