Engineers at the University of California, San Diego, have developed a new family of methods to significantly increase the speed of time-resolved numerical simulations in computational grand challenge problems.  Such problems often arise from the high-resolution approximation of the partial differential equations governing complex flows of fluids or plasmas. The breakthrough could be applied to simulations that include millions or billions of variables, including turbulence simulations. Modern computers are generally built from commodity hardware developed for serving and surfing the web.  When applied to cutting-edge problems in scientific computing, computers built from such general-purpose hardware usually spend most of their time moving data around in memory, and the hardware dedicated to floating point computations (that is, the actual addition and multiplication of numbers) spends most of its time idle.  The small memory footprint of the new schemes developed at UC San Diego means that numerical problems of a given size will run much faster on a given computer, and that even larger numerical problems may be considered. To read further, please visit http://www.jacobsschool.ucsd.edu/news/news_releases/release.sfe?id=1713.