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Nuclear Computational Low Energy Initiative (NUCLEI)

We propose to advance large-scale nuclear physics computations to dramatically increase our understanding of nuclear structure and reactions and the properties of nucleonic matter. Quantum Monte Carlo, Configuration Interaction, Coupled Cluster, and Density Functional codes have been developed and scaled efficiently to the largest computers available, and we propose to work closely with applied mathematicians and computer scientists to further extend these codes to the machines that will become available over the next five years. Our computational studies of the strong correlated matter found in nuclei will impact experimental program throughout the U.S.; including FRIB (the structure of heavy neutron-rich nuclei and related astrophysical environments), ATLAS and other low-energy nuclear physics facilities (structure and reactions of nuclei and nuclear astrophysics), TJNAF (the neutron distribution in nuclei, few body systems, and electroweak processes), NIF (light-ion thermonuclear reactions in a terrestrially controlled plasma environment), MAJORANA and FNPB (neutrinoless double-beta decay and physics beyond the Standard Model), LANSCE (studies on the properties of fission), and other experimental and observational facilities in nuclear physics and nuclear astrophysics. This proposal is also strongly coupled to several SciDAC institutes with leading efforts in applied math and computer science.

This research will build on successful collaborations with the Iowa State University team through work on the NUCLEI SciDAC-3 project. Contributions are to be made in the optimization, work and GPU-based computing for ab initio nuclear physics calculations. The optimization challenge lies in function evaluations being very expensive, often needing the calculations of several nuclei for a single function evaluation. Hence, large-scale resources are required and may be added dynamically based on the optimization progress. The integration of the high-cost function evaluations with a parallel optimization algorithm will constitute our objective. The motivation for the graphics processing unit (GPU)-based computing is in speeding up the construction of the many-body Hamiltonian matrix and computing its entries on-the-fly rather than storing in memory.

This research is supported by the U.S. Department of Energy, Office of Advanced Scientific Computing Research, through the Ames Laboratory.  The Ames Laboratory is operated for the U.S. Department of Energy by Iowa State University under Contract No. DE-AC02-07CH11358.

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