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New insights into backbending in the symmetry-adapted shell-model framework

https://doi.org/10.1103/PhysRevC.108.024304

Heller, Nicholas D.; Sargsyan, Grigor H.; Launey, Kristina D.; Johnson, Calvin W.; Dytrych, Tomáš; Draayer, Jerry P. (August 2023, Physical Review C)

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SU3lib: A C++ library for accurate computation of Wigner and Racah coefficients of SU(3)

https://doi.org/10.1016/j.cpc.2021.108137

Dytrych, Tomáš; Langr, Daniel; Draayer, Jerry P.; Launey, Kristina D.; Gazda, Daniel (December 2021, Computer Physics Communications)

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Emergent symplectic symmetry in atomic nuclei: Ab initio symmetry-adapted no-core shell model

https://doi.org/10.1140/epjst/e2020-000178-3

Launey, Kristina D.; Dytrych, Tomáš; Sargsyan, Grigor H.; Baker, Robert B.; Draayer, Jerry P. (October 2020, The European Physical Journal Special Topics)
null (Ed.)
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Efficient algorithm for representations of U(3) in U(N)

https://doi.org/10.1016/j.cpc.2019.05.018

Langr, Daniel; Dytrych, Tomáš; Draayer, Jerry P.; Launey, Kristina D.; Tvrdík, Pavel (June 2019, Computer Physics Communications)

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Accelerating many-nucleon basis generation for high performance computing enabled ab initio nuclear structure studies

https://doi.org/10.1177/1094342019838314

Langr, Daniel; Dytrych, Tomáš; Launey, Kristina_D; Draayer, Jerry_P (March 2019, The International Journal of High Performance Computing Applications)

We present the problem of generating a many-nucleon basis in [Formula: see text]-scheme for ab initio nuclear structure calculations in a symmetry-adapted no-core shell model framework. We first discuss and analyze the basis construction algorithm whose baseline implementation quickly becomes a significant bottleneck for large model spaces and heavier nuclei. The outcomes of this analysis are utilized to propose a new scalable version of the algorithm. Its performance is consequently studied empirically using the Blue Waters supercomputer. The measurements show significant acceleration achieved with over two orders of magnitude speedups realized for larger model spaces.
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