Juliane Mueller, a research scientist in the Computational Research Division, is developing an "optimization algorithm" toolset that pinpoints which variables of a "black box" simulation model will churn out the most realistic data in less time. (Credit: Roy Kaltschmidt/Berkeley Lab)
"What would happen if..'" To answer that question, scientists - whether they're studying climate science or cosmology - rely on computer simulations to bring their abstract data to life in 3D. The process is painstakingly slow, however: One simulation can take up to a million CPU (central processing unit) hours on a supercomputer. But according to Juliane Mueller, a research scientist in the Computational Research Division of the U.S. Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab), simulation work doesn't have to take that long. For the past year, she has been leading an effort to develop an "optimization algorithm" toolset that figures out which variables or "parameters" of a simulation model - what Mueller calls a "black box" - will churn out the most realistic data in less time, and therefore make the most efficient use of supercomputers. A better path to world-class science All simulation models contain parameters - in the case of climate science, for example, the parameters used in cloud simulations significantly impact a climate model's predictive ability. Choosing the correct parameters is critical to ensuring the model is close to your observations, said Mueller, but without an efficient, automated way to sift through the massive number of possible parameter combinations, scientists often have to rely on guesswork to find the best ones. "They might have to try a thousand different combinations, and out of this thousand they might take the best one, or choose parameters based on previous results from a published paper," she said.
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