A scalable parallel framework for microstructure analysis of large-scale molecular dynamics simulations data

Molecular dynamics (MD) simulation is a fundamental tool in computational materials science. With the development of parallel supercomputers, researchers can access the detail atomic responses of materials with MD simulations at unprecedented scales of physical and time. However, the size of generated output datasets is also growing rapidly, which poses a serious challenge for traditional data analysis methods. Therefore, parallel analysis methods to support faster and more scalable manipulation of atomic data are desperately needed. In this paper, we present a scalable parallel framework to meet the requirements. It allows users to implement a parallel analysis program using a simple interface, make use of the existing sequential analysis codes, and carry out distributed-memory post-simulation data analysis. We have integrated three popular microstructure characterization methods (lattice structure identification, Voronoi analysis, and Wigner-Seitz defect analysis) based on this framework. Performance evaluations run on massively parallel process computers with 109 atoms on up to 1024 processor cores demonstrate the scalability and efficiency of the proposed framework. The proposed framework is helping accelerate large-scale MD data analysis to a new level. (C) 2017 Elsevier B.V. All rights reserved.