Machine Learning and Understanding for

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The Office of Advanced Scientific Computing Research (ASCR) in the Office of Science (SC),U.S. Department of Energy (DOE), invites proposals for basic research that significantlyadvances Machine Learning and Understanding for High Performance Computing ScientificDiscovery in the context of emerging algorithms and software for extreme scale computingplatforms and next generation networks. The Department of Energy has the responsibility toaddress the energy, environmental and nuclear security challenges that face our nation. Themission of the Office of Science is the delivery of scientific discoveries and major scientific tools to transform our understanding of nature and to advance the energy, economic, and nationalsecurity of the United States.In the exascale computing timeframe, scientific progress will be predicated on our ability toprocess large, complex datasets from extreme scale simulations, experiments and observationalfacilities. Even at present, scientific data analysis is becoming a bottleneck in the discoveryprocess; we can only assume that the problem will become more so in the coming decade. At themoment, scientists are often forced to create ad hoc solutions where a lack of scalable analyticcapabilities means that there are large-scale experimental and simulation results that cannot befully and quickly utilized. Moreover, the scientists lack dynamic insight into their analyses, unable to modify the experiment or simulation on the fly. How could we enable broadlyapplicable solutions to address these challenges?In this program, we envision that Machine Learning and Understanding may offer the potential to transform basic scientific research best practices, by enabling systems to self-manage, heal and find patterns and provide tools for the discovery of new scientific insights. The goal of this program is to enable and identify basic fundamental research challenges to enable extreme scale machine learning and understanding focusing specifically on high performance computing challenges.

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