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ExaLearn Project to bring Machine Learning to Exascale

As supercomputers become ever more capable in their march toward exascale levels of performance, scientists can run increasingly detailed and accurate simulations to study problems ranging from cleaner combustion to the nature of the universe. Enter ExaLearn, a new machine learning project supported by DOE’s Exascale Computing Project (ECP), aims to develop new tools to help scientists overcome this challenge by applying machine learning to very large experimental datasets and simulations. 

Watch 5,000 Robots Merge to Map the Universe in 3-D

In this video, scientists describe how the Dark Energy Spectroscopic Instrument (DESI) will measure the effect of dark energy on the expansion of the universe. It will obtain optical spectra for tens of millions of galaxies and quasars, constructing a 3D map spanning the nearby universe to 11 billion light years. “How do you create the largest 3D map of the universe? It’s as easy as teaching 5,000 robots how to “dance.” DESI, the Dark Energy Spectroscopic Instrument, is an experiment that will target millions of distant galaxies by automatically swiveling fiber-optic positioners (the robots) to point at them and gather their light.”