Abstract
We address the problem of generating a many-nucleon basis for ab initio nuclear structure modeling, which quickly becomes a significant runtime bottleneck for large model spaces. We first analyze the original basis generation algorithm, which does not employ multi-threading parallel paradigm. Based on the analysis, we propose and empirically evaluate a new efficient scalable basis generation algorithm. We report a reduction of basis generation runtime by a factor of 42 on the Blue Waters supercomputer and by two orders of magnitude on our test-bed computer system with Broadwell CPUs.
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Notes
- 1.
https://sourceforge.net/projects/lsu3shell/ (In the time of writing this paper, latest updates were included in the LSU3develop repository branch).
- 2.
Note that the simulation mode does not reflect the runtime of the MPI_Allreduce communication operation. However, such a reduction of a small array is generally very fast. For instance, on Blue Waters it takes up to few seconds even if majority of the nodes are involved [9].
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Acknowledgements
This work was supported by the Czech Science Foundation under Grant No. 16-16772S. This work is also part of the “Collaborative Research: Innovative ab initio symmetry-adapted no-core shell model for advancing fundamental physics and astrophysics” PRAC allocation support by NSF (award number ACI-1516338), and is part of the Blue Waters sustained-petascale computing project, which is supported by NSF (awards OCI-0725070 and ACI-1238993) and the state of Illinois (Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications). The authors acknowledge support from J.P. Draayer from the Louisiana State University, P. Tvrdík from the Czech Technical University in Prague, P. Vrchota from Výzkumný a zkušební letecký ústav, a.s., and M. Pajr from IHPCI.
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Langr, D., Dytrych, T., Oberhuber, T., Knapp, F. (2018). Efficient Parallel Generation of Many-Nucleon Basis for Large-Scale Ab Initio Nuclear Structure Calculations. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2017. Lecture Notes in Computer Science(), vol 10778. Springer, Cham. https://doi.org/10.1007/978-3-319-78054-2_32
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