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Sacrificing a tiny bit of accuracy to run computer models, like those used to create weather forecasts, could save significant amounts of energy.

Scientists say such “inexact computing” wouldn’t comprise quality and would, in fact, allow them to reinvest the energy saved to increase the quality of the final answer.

“It is important to realize that there are very real costs, in terms of energy expended, to arrive at the more accurate answer.”

“In many situations, having an answer that is accurate to seven or eight decimal places is of no greater value than having an answer that is accurate to three or four decimal places, and it is important to realize that there are very real costs, in terms of energy expended, to arrive at the more accurate answer,” says Krishna Palem, director of Rice University’s Center for Computing at the Margins. “The discipline of inexact computing centers on saving energy wherever possibly by paying only for the accuracy that is required in a given situation.”

Computer scientists from Rice University, Argonne National Laboratory and the University of Illinois at Urbana-Champaign have used one of Isaac Newton’s numerical methods to demonstrate how “inexact computing” can dramatically improve the quality of simulations run on supercomputers and show it is possible to leapfrog from one part of a computation to the next and reinvest the energy.

Palem likened the new approach to calculating answers in a relay of sprints rather than in a marathon.

“By cutting precision and handing off the saved energy, we achieve significant quality improvements,” he says. “This model allows us to change the way computational energy resources are utilized in supercomputers to dramatically improve solutions within a fixed energy budget.”

The research team took advantage of one of the most commonly used tools of numerical analysis, a method known as Newton-Raphson that was created in the 1600s by Isaac Newton and Joseph Raphson. In supercomputing, the method is used to allow high-performance computers to find successively better approximations to complex mathematical functions.

The researchers demonstrated that the solution’s quality could be improved by more than three orders of magnitude for a fixed energy cost when an inexact approach to calculation was used rather than a traditional high-precision approach.

“In simple terms, it is analogous to rebalancing an investment portfolio,” says Marc Snir, a computer science professor at the University of Illinois at Urbana-Champaign. “If you have one investment that’s done well but has maxed out its potential, you might want to reinvest some or all of those funds to a new source with more potential for a much better return on investment.”

The Department of Energy, the Defense Advanced Research Projects Agency, and the Guggenheim Foundation funded the work.

Source: Rice University