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PyCAC: The concurrent atomistic-continuum simulation environment

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Abstract

We present a novel distributed-memory parallel implementation of the concurrent atomistic-continuum (CAC) method. Written mostly in Fortran 2008 and wrapped with a Python scripting interface, the CAC simulator in PyCAC runs in parallel using Message Passing Interface with a spatial decomposition algorithm. Built upon the underlying Fortran code, the Python interface provides a robust and versatile way for users to build system configurations, run CAC simulations, and analyze results. In this paper, following a brief introduction to the theoretical background of the CAC method, we discuss the serial algorithms of dynamic, quasistatic, and hybrid CAC, along with some programming techniques used in the code. We then illustrate the parallel algorithm, quantify the parallel scalability, and discuss some software specifications of PyCAC; more information can be found in the PyCAC user’s manual that is hosted on http://www.pycac.org.

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Notes

  1. Currently available resource on XSEDE is San Diego Supercomputer Center’s Comet cluster; integration with other XSEDE resources is planned for the future. Users desiring to run PyCAC (and other available materials informatics tools) for large-scale simulation/modeling projects should have their own compute/storage allocations on PACE or XSEDE and contact MATIN Project Lead (Aleksandr Blekh,) to discuss relevant integration and collaboration.

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ACKNOWLEDGMENTS

These results are based upon work supported by the National Science Foundation as a collaborative effort between Georgia Tech (CMMI-1232878) and University of Florida (CMMI-1233113). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. The authors thank Dr. Jinghong Fan, Dr. Qian Deng, Dr. Shengfeng Yang, Dr. Xiang Chen, Mr. Rui Che, Mr. Weixuan Li, and Mr. Ji Rigelesaiyin for helpful discussions, and Dr. Aleksandr Blekh for arranging execution of PyCAC via MATIN. The work of SX was supported in part by Georgia Tech Institute for Materials and in part by the Elings Prize Fellowship in Science offered by the California NanoSystems Institute (CNSI) on the UC Santa Barbara campus. SX also acknowledges support from the Center for Scientific Computing from the CNSI, MRL: an NSF MRSEC (DMR-1121053). LX acknowledges the support from the Department of Energy, Office of Basic Energy Sciences under Award Number DE-SC0006539. The work of LX was also supported in part by the National Science Foundation under Award Number CMMI-1536925. DLM is grateful for the additional support of the Carter N. Paden, Jr. Distinguished Chair in Metals Processing. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575.

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Authors and Affiliations

  1. California NanoSystems Institute, University of California, Santa Barbara, Santa Barbara, California, 93106-6105, USA

    Shuozhi Xu

  2. School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, 30332-0245, USA

    Thomas G. Payne & David L. McDowell

  3. Department of Aerospace Engineering, Iowa State University, Ames, Iowa, 50011, USA

    Hao Chen & Liming Xiong

  4. School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, 30332, USA

    Yongchao Liu

  5. Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida, 32611-6250, USA

    Youping Chen

  6. GWW School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, 30332-0405, USA

    David L. McDowell

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  1. Shuozhi Xu
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Correspondence to Shuozhi Xu.

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Xu, S., Payne, T.G., Chen, H. et al. PyCAC: The concurrent atomistic-continuum simulation environment. Journal of Materials Research 33, 857–871 (2018). https://doi.org/10.1557/jmr.2018.8

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